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By | Powerlearnings | No Comments

Written by Vinay Kalyan Parakala, SVP – Global Sales (Cloud Practice), LTI

Contributor Agnel Bankien, Head – Marketing at Powerup Cloud Technologies


With the significant growth in cloud markets, it is necessary that organizations inculcate cloud governance and operational excellence through dedicated Cloud Centre of Excellence (CCoE). It helps streamline and strengthen businesses by executing governance strategies across the infrastructure, platform and software as a service cloud models.

Every organization essentially needs to adopt a particular type of CCoE that best fits, in order to modernize their business and progress alongside continuously evolving technologies and innovation. CCoEs are internal and external teams built across finance, operations, security, compliance, architecture and human resources functions that aligns cloud offerings with the organizational strategies.

Therefore to facilitate effortless migrations along with agility, flexibility, cost optimization and multi-cloud management while perceiving how enterprises can structure and standardize their operations, it is important to establish a CCoE that would lead organizations through an effective cloud transformation journey. 


  1. What is Cloud Centre of Excellence (CCoE)?
  2. The need for CCoE
  3. Types of CCoE 
    1. Functional CCoE
    2. Advisory CCoE
    3. Prescriptive CCoE
  4. Best practices for configuring a CCoE
  5. Conclusion

What is Cloud Centre of Excellence (CCoE)?

Fortune Business Insights predict that the global cloud computing market size is anticipated to hit USD 760.98 billion by 2027. With the cloud markets accelerating, it is vital that organizations emphasize strongly on strategic planning and migration more than ever. 

A successful shift to the cloud needs complete alignment of businesses and resources, which is why a comprehensive cloud governance structure may not be enough to interact with and support cloud environments. 

Forging ahead, enterprises will need to focus attention on cloud operational excellence in order to streamline and enhance their businesses, thus driving them to establish a dedicated centralized Cloud Centre of Excellence (CCoE).

A CCoE is a cross-functional internal or external team comprising mainly of DevOps, CloudOps, Infrastructure, Security and FinOps that oversees cloud adoption, migration and operational functions. Additionally, the CCoE team also governs the IT and cloud infrastructure, confirming that it meets the organization’s expectations. 

To elaborate further, it signifies that CCOE enables cloud operational excellence across all cloud service models of infrastructure, platform and software as a service (IaaS, PaaS, and SaaS). The three pillars of CCoE being; 

Governance – CCoE creates cloud policies and guidelines in collaboration with cross functional teams, helps plan technical strategies and select centralized governance tools to address financial and risk management processes. 

Brokerage – Encompasses selection of cloud providers, architects cloud solutions as well as directs contract negotiation and vendor management.

Community – CCoE puts up a community of practice, enables knowledge sharing via building knowledge and source code repositories, conducts trainings, CoP councils and collaboration across all segments of an organization.

In totality, the CCoE ensures that cloud adoption is not siloed and encourages repeatable cloud processes and standards to be incepted as best practices. According to a recent survey, only 16 % of organizations have a fully-fledged CCoE, while 47 % are still working towards it.

The need for CCoE

The objective of the CCoE is to focus on the modernization of existing ITIL-based processes and governance standards while taking people, processes and technology, collectively into account.

  • By assembling the right people from the right functions, CCoEs can accurately comprehend and address opportunities to keep pace with progressing technology and innovative transformations.

The CCoE has the ability to answer migration related concerns like;

  • Is re-platforming needed? 
  • Will the lift and shift strategy be a better choice? 
  • What must be done to the original data while migrating? Etc. 

This strengthens and eases the decision-making capabilities of organizational teams kindling a structured cloud ecosystem in the long run.

When CCoE is successfully implemented, there is significant reduction in time-to-market, increased reliability, security and performance efficiency.

  • Over time, the CCoE team operations can gain more maturity and experience; affirming notable improvement in quality, security, reliability and performances on cloud. Organizations would eventually shape shift towards agility, paving way for smoother migrations, multi-cloud management, asset management, cost governance and customer satisfaction. 
  • Since a CCoE model works in coalition with cloud adoption, cloud strategy, governance, cloud platform and automation, the focus is more on delegated responsibility and centralized control, unlike the traditional IT set up, bringing about an impactful cultural shift within enterprises. 

Types of CCoE 

There are mainly three operational CCoEs that can help reinforce a cloud governance model. 

  • Functional CCoE: Encourages in building an exclusive team that can drive cloud initiatives, speed up analysis and decisions making processes, set cloud expertise standards, and act as a delivery catalyst in the cloud transformation.
  • Advisory CCoE: This is a team that provides consultative reviews and guidance on cloud best practices. Advisory teams establish and propel standards for new policies especially in a large and dynamic multi-project organizational set up.
  • Prescriptive CCoE: Acts as a leadership policy board highlighting how cloud projects should be constituted and executed within organizations. They help in defining policies for application deployment as well as identity and access management, set automation, security and audit standards and ensure that large enterprises become cloud governance competent.

Best practices for configuring a CCoE

Once organizations determine what type of CCoE fits them, the right team constructs and role definitions are vital in defining the cloud governance model. It is recommended that the founding team starts small with 3 to 5 members who can focus on a finite vision to begin with.

The most critical role in the CCoE team is that of an Executive Sponsor who leads the change bringing along other stakeholders from functions like finance, operations, security, compliance, architecture and human resources. 

Finance implements cost controls and optimization; operations manage the entire DevOps lifecycle round the clock whereas security and compliance define and establish cloud security standards and governance compliance practices. Cloud architecture expertise is included to bring in best practices and define a future roadmap that is cloud technology led. The CCoE is incomplete without the human resources who execute training programs and changes in workforce to make organizations cloud savvy. 

As soon as the appropriate team is formed, a CCoE charter stating the objectives, operational goals along with roles and responsibilities need to be scripted. 

For CCOE to define how cloud solutions will be extended and be in line with the organization’s project lifecycle, it is essential to draft a deployment plan.

It is important that the CCoE team works with authority and yet maintains harmony while integrating with the rest of the organization for a successful cloud transformation.

Lastly, organizations that migrate to cloud do so, to avail cost benefits, increase efficiency, flexibility and scalability of operations. Therefore, it is the responsibility of the CCoE team to measure key performances to keep a check on the cloud usage, infrastructure cost and performance at regular intervals.


The Cloud Center of Excellence (CCoE) helps accelerate cloud adoption by driving governance, developing reusable frameworks, overseeing cloud usage, and maintaining cloud learning. It aligns cloud offerings with the organizational strategies to lead an effective cloud transformation journey.

Deciphering Compliance on Cloud

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Compiled by Kiran Kumar, Business analyst at Powerup Cloud Technologies

Contributor Agnel Bankien, Head – Marketing at Powerup Cloud Technologies

Blog Flow

  1. What is cloud compliance?
  2. Why is it important to be compliant?
  3. Types of cloud compliance 
    1. ISO
    2. HIPAA
    3. PCI DSS
    4. GLBA
    5. GDPR
    6. FedRAMP
    7. SOX
    8. FISMA
    9. FERPA
  4. Challenges in cloud compliance
  5. How can organizations ascertain security and compliance standards are met with while moving to cloud?
  6. Conclusion


As time progresses, businesses are getting more data-driven and cloud-centric imposing the need for stringent security and compliance measures. With the alarming rise in the number of cyber-attacks and data breaches lately, it is crucial that organizations understand, implement and monitor data and infrastructure protection on the cloud. 

It is important yet challenging for large distributed organizations with complex virtual and physical architectures across multiple locations, to define compliance policies and establish security standards that will help them accelerate change and innovation while enhancing data storage and privacy.

There are various compliance standards like HIPAA, ISO, GDPR, SOX, FISMA, and more that ensure appropriate guidelines and compliance updates are met to augment cloud security and compliance. Prioritizing cloud security, determining accurate cloud platforms, implementing change management, investing in automated compliance tools, and administering cloud governance are some of the measures that will surely warrant cloud compliance across all domains.

What is cloud compliance?

Most of the businesses today are largely data-driven. The 2020 Global State of Enterprise Analytics Report states that 94% of businesses feel data and analytics are drivers of growth and digital transformation today, out of which, 56% of organizations leveraging analytics are experiencing significant financial benefits along with more scope for innovation and effective decision-making capacities.

To accelerate further, organizations are steering rapidly towards the cloud for its obvious versatile offerings like guaranteed business continuity, reduced IT costs, scalability and flexibility. 

With cloud, strengthening security and compliance policies has become a necessity. Cloud compliance is about consenting with the industry rules, laws and regulatory policies while delivering through the cloud. The law compels the cloud users to verify if the effective security provisions of their vendors are in line with their compliance needs.

Consequently, the cloud-delivered systems are better placed to be compliant with the various industry standards and internal policies while also being able to efficiently track and report status. 

The shift to cloud enables businesses to not just transit from capital to operational expenses but also from internal to external operational security. Issues related to security and compliance can pose as barriers especially with regards to cloud storage and back up services.

Therefore, it is imperative to understand in which part of the world will our data be stored and processed, the kind of authorities and laws that will be applicable to this data and its impact on business. Every country has varied information security laws, data protection laws, access to information laws, information retention and sovereignty laws that need to be taken into consideration in order to build appropriate security measures that adhere to these set standards. 

Why is it important to be compliant?

Gartner research Vice President Sid Nag says, “At this point, cloud adoption is mainstream.” 

Recent data from Risk Based Security revealed that the number of records exposed has increased to a staggering 36 billion in 2020 with Q3 alone depicting an additional 8.3 billion records to what was already the “worst year so far.”

“There were a number of notable data breaches but the compromise of the Twitter accounts held by several high profile celebrities probably garnered the most headlines”, says Chris Hallenbeck, Chief Information Security Officer for the Americas at Tanium.

With enterprises moving their data and applications substantially on cloud, security threats and breaches across all operations emerge as their biggest concern. 

Therefore it is crucial for organizations to attain full visibility and foresight on security, governance and compliance on cloud.

Data storage and its privacy is the topmost concern and not being compliant with industry rules and regulations would augment data violation and confidentiality breach. A structured compliance management system also enables organizations to steer clear of heavy non-compliance penalties.

An effective corporate compliance management guarantees a positive business image and fabricates the customer’s trust and loyalty. It constructs customer reliability and commitment that helps build a strong and lasting customer base. 

Administering compliance solutions reduce unforced errors and helps keep a check on genuine risks and errors arising out of internal business performances.

Compliance is considered a valuable asset for driving innovation and change.

Types of cloud compliance 

Until recently, most service providers focused on providing data and cloud storage services without much concern towards data security or industry standards. As the cloud scales up, the need for compliance with regards to data storage also increases requiring service providers to draft new guidelines and compliance updates while measuring up to the ever changing national and industry regulations. 

Some of the most seasoned regulations governing cloud compliance today are:

1. International Organization for Standardization (ISO)

ISO is one of the most eminent administrative bodies in charge of cloud guidelines and has developed numerous laws that govern the applications of cloud computing.

ISO/IEC 27001:2013 is one of the most widely used of all ISO cloud requirements. Right from formation to maintenance of information security management systems, ISO specifies how organizations must address their security risks, how to establish reliable security measures for cloud vendors and users and helps set firm IT governance standards.

2. Health Insurance Portability and Accountability Act (HIPAA)

HIPAA, applicable only within the United States, provisions for security and management of protected health information (PHI). It helps institutions like the hospitals; doctors’ clinics and health insurance organizations to follow strict guidelines on how confidential patient information can be used, managed and stored along with reporting security breaches, if any. Title II, the most significant section of HIPAA ensures that the healthcare industry adopts secure encryption processes to secure data and operate electronic transactions with significant safety measures.

3. PCI DSS (Payment Card Industry Data Security Standard) 

PCI DSS is a standard pertaining to organizations that process or handle payment card information like credit cards, where it is mandatory that each of the stated 12 requirements in the act are met with to achieve compliance. Major credit card companies like American Express, MasterCard, Discover and Visa came together to establish PCI DSS to provide better security for cardholder’s data and payment transactions. PCI DSS has further implemented new controls for multifactor user authentication and data encryption requirements of late. 

4. GLBA (Gramm-Leach-Bliley Act) 

GLBA applies to financial institutions that need to understand and define how a customer’s confidential data should be protected. The law enforces organizations to create transparency by sharing with customers how their data is being stored and secured.

5. General Data Protection Regulation (GDPR)

GDPR regulations facilitate organizations that work with European Union residents to govern and control their data in order to create a better international standard for business.

The GDPR levies heavy fines, as much as 4% of the annual global turnover or €20 million, whichever is greater, if not complied with. Identity and access management frameworks can enable organizations to comply with GDPR requirements like managing consent from individuals to have their data recorded and tracked, responding to individuals’ right to have their data erased and notifying people in the event of a personal data breach. 

6. Federal Risk and Authorization Management Program (FedRAMP)

FedRAMP provides enhanced security within the cloud as per the numerous security controls set through the National Institute of Standards and Technology (NIST) Special Publication 800-53. It helps in the evaluation of management and analysis of different cloud solutions and products while also ensuring that the cloud service vendors remain in compliance with the stated security controls as well.

7. Sarbanes-Oxley Act of 2002 (SOX)

SOX regulations were introduced after prominent financial scandals in the early 2000. It ensures all public companies in the US take steps to mitigate fraudulent accounting and financial activities. SOX safeguards the American public from corporate wrongdoing and it is mandatory for organizations that constitute under SOX, to work only with those cloud providers that employ SSAE 16 or SAS 70 auditing guidelines.

8. Federal Information Security Management Act (FISMA)

FISMA is responsible for governing the US Federal Government ensuring that federal agencies safeguard their assets and information by creating and implementing an internal security plan. FISMA sets a one-year timeline for review of this plan to enhance the effectiveness of the program and the ongoing mitigation of risks.

FISMA also controls the technology security of third-party cloud vendors.

9. Family Educational Rights and Privacy Act of 1974 (FERPA)

FERPA caters to governing student records maintained by educational institutions and agencies and also applies to all federally funded elementary, secondary, and post secondary institutions. It plans for these institutes to identify and authenticate the identity of parents, students, school officials and other parties before permitting access to personally identifiable information (PII). FERPA enforces relevant policies to reduce authentication misuse in order to efficiently manage user identity life cycle with periodic account recertification.

Challenges in cloud compliance

With an on-premise data center set up, enterprises are responsible for the entire network, security controls and hardware, physically present in the premises whereas security controls in the cloud are virtual and are usually provided by third-party cloud vendors. 

Keeping track of data and assuring its security, especially if it involves large, distributed organizations with complex architectures spread across multiple locations and systems, both physical and virtual, is extremely challenging.

The pressure builds up even more on enterprises when industry regulators are imposed to tighten data protection techniques, violating which leads to heavy fines. Regular audits and security policy checks have to be embraced by organizations to manifest compliance.

The challenges with cloud compliance are:

  • Multi-location regulations: Large organizations serving clients globally need to adhere to regional, national and international regulations with regards to data possession and transfer. However while migrating to cloud, the preferred cloud vendor may not always be able to offer the exact stated requirements. Adopting technology that supports major public cloud vendors, promoting hybrid cloud strategies, determining which data can be safely moved to cloud while retaining sensitive data on-premises are some measures that will help establish security and compliance on cloud.
  • Data Visibility: Data storage is a huge challenge in terms of where and how data can be stored resulting in poor data visibility. Moving to cloud facilitates using distributed cloud storage services for different types of data, entitling organizations to act in accordance with security directives while data storage and back ups.
  • Data Breach: Security compliance regulations on cloud need to be set in place to evade data security vulnerabilities and risks in real time. Adopting microservices on cloud, which is breaking down the applications into smaller components, each of which are categorized to its own dedicated resource is a must. This process improves data security among other features, as it generates additional layers of isolation with the breakdown approach, making it tougher for invaders to hack the infrastructure. 
  • Data Protection Authority: Moving to the cloud enables enterprises to offload their responsibility of securing their physical infrastructure on to the cloud service provider. However, it is still compelling for organizations to oblige to privacy and security of data that is under their control and verify appropriate data protection measures internally.
  •  Network Visibility: Managing firewall policies where traffic flows are typically complex are a challenge. Visibility of the network becomes tricky. Many organizations are using the multi-cloud approach to support their infrastructure in order to curb network issues.
  • Network management: Automation is the key to management of network firewalls that have countless security policies across multiple devices, which is otherwise difficult to manage as well as time-consuming. Also, appropriate network security configurations are a prerequisite but with compliance management mostly left to cloud providers, the regulations and implementation process often end up haywire. 
  • Data Privacy and Storage: Keeping track of personal data by mapping the flow of data on cloud is a must. The right to access, modify and delete data can be strengthened via implementation of privacy laws. The cloud can further simplify matters by offering low-cost storage solutions for backup and archiving.
  • Data Inventory Management: Data is stored in unstructured formats on both on-premises and cloud, mainly to be used for business forecasting, social media analytics and fraud prevention. This would require data inventory management solutions to ensure speedy and efficient responses to requests that need to be compliant with regulatory laws.

How can organizations ascertain security and compliance standards are met with while moving to cloud?

According to a recent Sophos report of The State of Cloud Security 2020, 70% of companies that host data or workloads in the cloud have experienced a breach of their public cloud environment and the most common attack types were malware (34%), followed by exposed data (29%), ransomware (28%), account compromises (25%), and cryptojacking (17%).

The biggest areas of concern are data loss, detection and response and multi-cloud management. Organizations that use two or more public cloud providers experienced the most security incidents. India was the worst affected country with 93% of organizations experiencing a cloud security breach. 

It is of utmost importance for cloud service providers (CSP) to ensure that security and compliance standards are met with while moving data on to cloud and to do so, some of the following measures can be administered:

  • Determine appropriate cloud platforms: Organizations must evaluate initial cloud risks to determine suitable cloud platforms. It is also essential to realize which set of data and applications can be moved to cloud. For example: Sensitive data or critical applications may still remain on premise or use the private cloud whereas non-critical applications may be hosted on public or hybrid models. Relevant security control frameworks, irrespective of whether data and applications are hosted on private, public, multi-cloud or hybrid platforms need to be established. Continuous compliance monitoring via these security measures, prioritization and remediation of compliance risks, if any and generation of periodic compliance reports help in developing a consolidated picture of all cloud accounts. 
  • Undertake a security-first approach: Leveraging real-time tracking tools and automated security policies, processes and controls holistically across internal and external environments from the very beginning, help in keeping complete and continuous visibility on cloud compliance. 

Monitoring and managing security breach and threats via compliance checklists for all the services that include infrastructure, networks, applications, servers, data, storage, OS and virtualization establishes pertinent data protection measures, reduces costs and simplifies cloud operations.

  • Implementing change management: AI and tailored workflows facilitate identifying, remediating and integrating security policy changes that can be processed in no time. 

Automation streamlines and helps tighten the entire security policy change management through auditing. 

  • Building resources: It is important to collaborate IT Security and DevOps, commonly known as SecOps, to effectively mitigate risks across the software development life cycle. Through SecOps, business teams can prioritize and amend critical vulnerabilities as well as address compliance violations via an integrated approach across all work segments. It enables a faster and risk-free deployment into production. 
  • Invest in tools: Advanced automated tools comprise of built-in templates that certify and maintain security management standards. Compliance tools based on AI technology, acts as a framework towards protecting privacy of all stakeholders, meets data security needs, provides frequent reports on stored cloud data and detects possible violations beforehand. Thus, investing in tools enhances visibility, data encryption and control over cloud deployments. 
  • Ensuring efficient incident response: Due to seamless integration with the leading cloud solutions, compliance tools are able to map security incidents to actual business processes that can potentially be impacted. Organizations can instantly evaluate the scale of the risk and prioritize remediation efforts consequently leading to efficient incident response management. For instance, in case of a cyber attack, the compliance tool enables isolation of those servers that have been compromised ensuring business continuity.
  • Administer cloud governance: Cloud security governance is an effective regulatory model designed to define and address security standards, policies and processes. The governance tool provides consolidated synopsis of all security issues, which are monitored, tracked and compiled in the form of dashboards. They also facilitate configuration of customized audits and policies, generation of periodic summarization of compliance checks and one-click remediation capabilities with a fully traceable remediation history of all the fixed issues. It also generates pre-populated, audit-ready reports that provides information before an audit is actually conducted. 

LTI Powerup’s CloudEnsure is a prominent instance of an autonomous multi-cloud governance platform that has been successfully offering audit, compliance, remediation and governance services in order to construct and maintain a well architected and healthy cloud environment for their customers.

  • Conducting audits: It is recommended to have compliance checks both manual and automated, against all the major industry regulations like PCI DSS, HIPAA and SOX, including customized corporate policies in order to keep a constant check on all security policy changes and compliance violations. A cloud health score reveals how compliant all the operations are.

Audits furnish reports on cloud security and cloud compliance summary, security compliance by policy that tracks real-time risks and vulnerabilities against set policies, detailed automated metrics on the health of your multi-cloud infrastructure which displays critical risks along with an overall security compliance summary to name a few.

  • Drive digital transformation: Security tools that can accelerate application delivery; prioritize security policy change management while enhancing and extending security across all data, applications, platforms and processes regardless of location must be embraced to accelerate digitization of business processes.


Compliance is a shared responsibility between cloud service providers and organizations availing their services. 

Today, a majority of cloud service providers have begun to recognize the importance of giving precedence to security and compliance services with the aim to continually improve their offerings. 

Therefore, organizations are endlessly striving to reassess and redeploy their security strategies by trying to revive and control their cloud undertakings especially post pandemic. 

No matter what type of cloud is chosen, the migrated data must meet all of the compliance regulations and guidelines. 

How Containers Enable Cloud Modernization

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Compiled by Kiran Kumar, Business analyst at Powerup Cloud Technologies

Contributor Agnel Bankien, Head – Marketing at Powerup Cloud Technologies


Container-as-a-service (CaaS) is a business model offered by cloud service providers that facilitates the software developers in organizing, running, managing, and deploying containers by using container-based virtualization.

Containers are responsible for packaging applications and their dependencies all together in a compact format that can be version controlled, are scalable and can be replicated across teams and clusters as and when required. 

By segregating the infrastructure and application components of a system, containers can accommodate themselves between multi-cloud and hybrid environments without altering the code, thus posing as a significant layer between the IaaS and PaaS platforms of cloud computing.

Implementing CaaS has advantages like rapid delivery and deployment of new application containers, operational simplicity, scalability, cost-effectiveness, increased productivity, automated testing and platform independence to list a few. CaaS markets are growing rapidly with enterprises across all domains adapting to container technology.


1. What are Containers?

2. What is Container-as-a-Service (CaaS)?

3. How CaaS differs from other cloud models?

3.1 How CaaS works?

4. Who should use CaaS?

4.1 Type of companies

4.2 Type of Workloads

4.3 Type of Use Cases

5. How CaaS has impacted the cloud market?

6. Benefits and drawbacks

What are Containers?

Containers are a set of software capable of bundling application code along with its dependencies, which can be run on traditional IT setups or cloud. Dependencies include all necessary executable files or programs, code, runtime, system libraries and configuration files.

Since containers are efficient in running application files without exhausting a great deal of resources, users see it as an approach to operating system virtualization.

56% of the organizations that polled for the 2020 edition of “The State of Enterprise Open Source” report said they expected their use of containers to increase in the next 12 months.

Containers leverage operating system features to control as well as isolate the amount of CPU, memory and disk being used while running only those files that an application needs to run unlike virtual machines that end up running additional files and services.

Containerized environment can thus optimize a system to run several hundreds of containers as against 5 or 6 virtual machines that would typically run on a traditional virtualization approach.

What is Container-as-a-Service (CaaS)?

Container as a service (CaaS) is an automated cloud based service that enables users to host and deploy highly secure and scalable containers, applications and clusters via on-premise data centers or cloud.

CaaS acts as a significant bridge between the IaaS and PaaS layers of cloud computing services often regarded as a sub-division of IaaS delivery model.

By segregating the infrastructure and application components of a system, containers can accommodate themselves between multi-cloud and hybrid environments without altering the code.

Gartner says that 81 % of companies that have already adopted public cloud are working with two or more cloud providers.

CaaS is considered the most flexible and best-fit framework in providing tools to cater to the entire application lifecycle while also being capable of operating in any language and under any OS and infrastructure benefitting the organization’s software development and operations teams.

It helps organizations attain product excellence, speedy deployment, agility and application portability while assuring improved application delivery and stellar customer service.

How it differs from other cloud models

With the evolution of cloud computing, several “ as a service” offerings have actualized in enhancing core business operations. The three traditional service models securing maximum prominence in the recent past are:

Infrastructure-as-a-Service (IaaS) that provides virtual hardware, storage and network capacity, Platform-as-a-Service (PaaS) catering to the entire software development lifecycle and Software-as-a-Service (SaaS) that deals completely with running application software on cloud.

The first six months of 2020 saw a 22% rise in organizations that have containerized more than half of their applications.

Container-as-a-Service can be positioned between the IaaS and PaaS layers of cloud computing models where using container technology, CaaS is able to create a virtualized abstract layer that captures applications and files from the elementary system and directs it on any container based platform for operations.

Meaning, CaaS utilizes native functions of an OS to isolate and virtualize individual processes within the same operating system unlike IaaS model, where the user is responsible for installing and maintaining the virtual hardware and operating systems. Thus, CaaS manages the software application lifecycle similar to what IaaS and PaaS do but with a slight difference.

Additionally, in traditional cloud systems, software developers are heavily dependent on technologies provided by the cloud vendor.

For instance, a developer who uses PaaS to test applications needs to load his own code onto the cloud while all technical requirements for the build process as well as managing and deploying applications are taken care of by the PaaS platform. However, Container-as-a-Service, provides users with a relatively independent programming platform and framework, where applications confined in containers can be scaled over diverse infrastructures, regardless of their technical requirements making it less reliant on the PaaS model.

How CaaS works

CaaS platform is a comprehensive container management environment comprising orchestration tools, image repositories, cluster management software, service discovery, storage and network plug-ins that enable IT and DevOps to effortlessly deploy, manage and scale container based applications and services.

The interaction with the cloud-based container environment is either through the graphical user interface (GUI) or API calls and the provider controls which container technologies should be made available to users.

Docker Swarm, Kubernetes, and Mesosphere DC/OS are the three most dominating orchestration tools in the market. With such built-in orchestration engines, CaaS solutions enable automated provisioning, scaling, and administration of container applications on distributed IT infrastructures. Moreover, cluster management features allow applications to run as clusters of containers that can integrate and work collaboratively as a single system.

Containers are capable of overcoming problems arising from having multiple environments with disparate configurations as it enables development teams to use the same image that gets deployed to production. Also, since containers are meant to be recreated whenever needed, it is considered best to centralize logs. CaaS facilitates aggregation and standardization of logs along with monitoring capacities.

All leading IaaS providers like Google, AWS, Microsoft Azure, Red Hat, Docker Cloud and OpenShift have CaaS solutions built on top of their IaaS platforms whose underlying orchestration solutions help automate provisioning, clustering and load balancing. Some of these providers also offer PaaS solutions that allow developers to build their codes before deploying to CaaS.

Who should use CaaS?

A recent survey by Gartner predicts that by 2023, 70% of organizations will be running three or more containerized applications in production where containers, Kubernetes and microservices would emerge as leading drivers of IT digitization.

With everyone favoring DevOps these days, numerous large IT organizations are attaining container capabilities by purchasing smaller startups.

According to the Datadog report, the move to Docker is actually being led by larger companies (with 500 or more hosts), rather than smaller startups which supports the fact that Docker use by enterprise-scale organizations is greater than average for all businesses as it is considered relatively simple to deploy.

For example, the scale of deployment reported at a SaaS company was as high as 15,000 containers per day where the process of container deployment was easier and faster making the present-day transition achievable.

Type of workloads

Most infrastructures are complex and host a diverse set of workloads. While virtual machines virtualize physical hardware keeping an efficient isolation mechanism, containers virtualize the operating system provisioning very little for workload isolation. Hence it is important for enterprises to determine the percentage and portions of infrastructure that are best suited for containerization.

Containers, regardless of their popularity, would continue to coexist with virtual machines and servers, as they cannot substitute them entirely.

If there are workloads that need to scale significantly or applications that need prompt and swift updates, deploying new container images could be a reliable and authentic solution as well.

High number of workloads, understanding how open an organization’s container solutions are and availability of container expertise teams are a few more factors that decide how much to containerize.

Type of use cases

Organizations are known to use containers to either lift or shift existing applications into modern cloud architectures, which provide restricted benefits of operating system virtualization or they restructure existing applications for containers offering full-fledged advantage of container-based application architectures.

Similar to refactoring, developing new native applications also provide full benefits of containers.

Moreover, distributed applications and microservices can be conveniently isolated, deployed and scaled using independent container blocks, containers can provide DevOps support to streamline integration and deployment (CI/CD) as well as allow smooth implementation of repetitive processes that can run in the background.

One of LTI Powerup’s distinguished clients, a large ecommerce start-up, was running all their applications on AWS multi-container environment. Due to this set up, the existing environment was unable to scale for individual services, cost of running their micro-services were increasing and deployment of one service was affecting other services as well. The DevOps team at LTI Powerup proposed and implemented the Kubernetes cluster from scratch to help the client overcome all the above stated issues.

Read the full case study here 

How CaaS has impacted the cloud market?

The Containers as a Service (CaaS) market is expected to grow at a Compound Annual Growth Rate (CAGR) of 35% for the period 2020 to 2025.

The CaaS market has been segregated based on deployment models, service types, size of the enterprise, end user application and geographical regions.

The demand for CaaS is navigated by factors like rapid delivery and deployment of new application containers, operational simplicity, benefits of cost-effectiveness, increased productivity, automated testing, platform independence, reduced shipment time due to hosted applications and increasing popularity of microservices.

As per market studies, the security and network capability segments are expected to grow at the highest CAGR while it is also anticipated that CaaS will provide new business opportunities to small-medium enterprises during the forecast period. Technologies like mobile banking and digital payments are transfiguring the banking industry, especially in emerging countries like India and China where major BFSI companies have already started deploying container application platforms in their systems.

Among the deployment models, the public cloud segment is estimated to continue to hold a significant market share as it offers scalability, reliability, more agility and flexibility to organizations adopting containers.

However, markets foresee data security threats on cloud that may hamper the growth trend, which needs to be strengthened by implementing security and compliance measures with immediate effect.

North America showcased the largest market share in 2017 whereas the Asia Pacific (APAC) region is projected to grow at the highest CAGR by 2022. The increasing use of microservices and the shift of focus from DevOps to serverless architecture are driving the demand for CaaS globally.

Some major influential public cloud vendors providing CaaS are Google Container Engine (GKE), Amazon Elastic Container Service (ECS) , Microsoft Azure kubernetes Service (AKS) followed closely by Docker, to name a few. Google Kubernetes and Docker Swarm are two examples of CaaS orchestration platforms while Docker Hub can be integrated as a registry for Docker images.

CaaS markets have evolved rapidly in the past 3 years and Enterprise clients from all industries are seeing the benefits of CaaS and container technology.

Benefits and drawbacks

  • Speedy Development: Containers are definitely the answer to organizations looking at developing an application at a fast pace subject to maintaining scalability and security. Since containers do not need operating systems, it takes only seconds to initialize, replicate or terminate a container leading to speedy development processes, consolidating new features, timely response to defects and enhanced customer experience.
  • Easy Deployment: Containers simplify the process of deployment and composition of distributed systems or micro service architectures. To illustrate, if a software system is organized by business domain ownership in microservice architecture, where the service domain can be payments, authentication and shopping cart, each of these services will have its own code base  and can be containerized. Therefore, using CaaS, these service containers can be instantly   employed to a live system.
  • Efficiency: Containerized application tools such as log aggregation and monitoring enable performance efficiency.
  • Scalability and High Availability: Built in CaaS functions for auto scaling and orchestration management allows teams to swiftly build high visibility and high availability distributed systems. Besides, it not just builds consistency but also accelerates deployments.
  • Cost Effectiveness: As CaaS do not need a separate operating system, it calls for minimal resources, thus significantly controlling engineering operating costs as well as optimally minimizing the DevOps team size.
  • Increased Portability: Containers trigger portability that enables end users to accurately launch applications in different environments, such as public or private clouds. Furthermore, it lets incorporation of multiple identical containers within the same cluster in order to scale.
  • Business continuity: As containers, to a certain degree, remain isolated from other containers on the same servers, in case of an application malfunction or crash for one container, other containers can continue to run efficiently without experiencing technical issues. Similarly, the shielding that the containers have from each other, doubles as a safety feature, minimizing the risk. If an application is at risk, the effects will not extend to other containers.

However, organizations need to contemplate whether they even need containers before implementing CaaS. To begin with, containerization increases complexity because it introduces components that are not present in the IaaS platform.

Containers are conditional to network layers and interfacing with host systems can hinder operational performances. Constant data storage on containers is a challenge as all the data disappears by default once containers are shut down.

Additionally, container platforms may not be compatible with other container products on the CaaS ecosystem. Lastly, creative GUI apps may not work well as CaaS services were designed to mainly cater to applications that do not need graphics.

CaaS is a powerful modern hosting model, most beneficial to applications that are designed to run as independent microservices. Migration to containers may not necessarily be the best choice for all users as in some cases, traditional virtual machines may serve better. Nevertheless, CaaS, IaaS and PaaS are distinct services with different management models and only organizations can determine how and when CaaS can benefit their operations.

Cloud Report Card: 3 Months of COVID-19 Impact

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Siva S, CEO of Powerupcloud, Global Cloud Practice Head at LTI

So, here we are. May 2020. It has been 3 months since the Covid-19 pandemic started impacting the global economy and with it the business functions globally. This has not been a smooth ride for governments, businesses, entrepreneurs, and most importantly, the people. I have been actively speaking to several CIOs, CEOs of global businesses with operations in the USA, UK, Germany, France, UAE, SouthAfrica, India, Singapore, Australia, and New Zealand. The business sentiments and decisions seem to follow a similar pattern irrespective of the country or government or the business itself. That’s the effect the COVID-19 pandemic has had so far on all of us.

In this article, I would be covering the major trends we are witnessing public cloud adoption and the change in priorities based on our customer and OEM interactions.

  1. Cloud Cost Optimization: The highest demand we see is with the cost optimization of cloud spend at businesses that are already on the cloud. Irrespective of their spending, be it $0.5M or $20M per year, reducing their cloud spend is a key focus for the CIOs. The ‘Save Now, Pay Later’ program that we launched which helps large businesses save cloud costs with the help of our cloud governance platform – has seen a massive uptake with our global customers due to the nature of the program. The gain-share model, where our success fee is a % of cost savings we bring to the client, thus bringing in a win-win situation for both the vendor and the client, seems exactly what the businesses need at this time of the hour.
  2. Remote Workforce Enablement: This is the second area where we see high demand from our enterprise customers. Be it migration to virtual desktops on cloud or launching a fully scalable virtual contact center on the cloud or adopting virtual collaboration platforms, CIOs are keen to explore technologies that will improve the productivity of their employees working from home. With most businesses taking a call to have their employees work from home till the end of 2020, enabling their remote workers with technology that aids them to work better is at the top of the agenda for businesses. Check out our Remote Workforce Enablement program.
  3. Data Analytics on Cloud: While most of the businesses I interact have literally stopped their big-bang data transformation exercises, they, however, do not want to stop the adoption of cloud for their data environments. We are witnessing a trend wherein customers are identifying their business-critical applications and moving them to cloud including the data layer for 2 reasons – 1. to improve the availability and reliability of the data layer powering these applications 2. to feed the data lake with data in real-time which will allow them to run ML models on the fly. This trend is most likely to follow for the next 12 months. The best part is, by the end of 12 months, businesses who follow this approach will have most of their critical applications running on the cloud with a centralized data approach.
  4. Large Scale Cloud Migrations: Plans to migrate the entire data center to the cloud is seeing a mixed response. I am interacting with a couple of CIOs of large manufacturing businesses in the EU & USA who are going ahead with their plans to migrate completely to the public cloud platforms. These companies have workloads in the nature of 15000+ servers, 1000+ applications. Their argument, a valid one, is to do this entire migration when the manufacturing activity is at its lowest due to COVID-19 impact. But this represents just 10% of our total migration pipeline.
  5. Continuous Cloud Adoption: Most of the other industries are adopting the concept of ‘continuous cloud adoption’ model where they subscribe for a ‘Cloud POD’ (a 6 member team comprising cloud architects, cloud engineers, and a project manager) for a 12 month period. The Cloud POD will work with the customer to identify the key applications and migrate them to the cloud in a sequential manner. This allows the customer to continue their cloud adoption, enabling businesses with better reliability for their key applications and helping the CFO with moving to an OPEX model on an incremental basis. My vote goes to this approach as this model brings in more flexibility to the CIOs. They can use the Cloud POD for security & governance implementations or cost optimization by pausing the migration activity when the situation demands.
  6. AI/ML Adoption: Many artificial intelligence solutions that used to be a hard sell to businesses all these years are seeing a voluntary increased adoption by businesses in these last 3 months and we expect this trend to continue for the next 2 years to come. Chatbots, for example, has seen a 200% increase in demand in this period. We are seeing requirements from building customer support chatbots to internal employee engagement HRMS chatbots to ease the dependency on the human support system to fulfill the end-user needs. Banks, Insurance companies, eCommerce players, OTT platforms, Healthcare organizations, Educational institutes are the ones that often feature in our chatbot requirements pipeline. AI+RPA is another area of focus where businesses are focusing on implementing AI & RPA technologies either in combination or stand alone to automate some of their business processes.

The bottom line is, for almost all the businesses cash conservation is the primary focus. But at the same time, they cannot afford to completely stop their digital transformation journey. The key here is to balance these 2 things so that they are better prepared when the global economy comes back on track. Businesses that take aggressive decisions on either end of the spectrum will see a greater risk of failure. It is completely fine to continue to take an ‘ambiguous’ approach and keeps things in balance instead of boiling the ocean.

Cash conservation should be your primary focus. But don’t stop your digital transformation journey.

The Era Of Contact Center AI Has Started, Officially!!!

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Siva S, CEO of Powerupcloud, Global Cloud Practice Head at LTI

Before I start, I sincerely hope you and your loved ones are safe and well.

There are no doubts that COVID-19 Pandemic has brought great distress to our lives, both in the personal and business front. We see businesses, governments, large corporations are reeling from the effect of lockdowns and COVID-19 spread. Over 90% of the businesses worldwide are suffering due to the lockdown. But the tech industry, especially Cloud and AI are seeing a very different trend. Businesses are realizing that they cannot ignore Cloud and AI anymore and with each day passing, they start feeling more and more pain with their existing traditional IT systems.

In today’s article, I would like to focus on the Contact Center AI solution which is currently the #1 sought after technology on the cloud for businesses across the world.

In 2016, I envisioned a Chatbot platform named IRA.AI (now called as a customer support chatbot which will automate the customer support process by interacting with customers and provide them with answers in real-time. I must admit that it was a super-hard sell back in 2016. We were one of the first to build a robust chatbot development and management platform, well before AWS & Azure launched their versions. We used Python NLTK to power our chatbot platform. Fast forward to the second half of 2019, the scene wasn’t very different. We still saw a majority of businesses experimenting but not fully adopting the AI Chatbot solutions.

But the COVID-19 Pandemic has changed this situation overnight, very much like how it changed a lot of our lives in a very short time. We are seeing the customer care calls going over the roof since January 2020. One instance where the US citizens applying for unemployment benefits had to wait for almost 48 hours to get through a customer support agent. Another instance where a UK based Telco experiencing a surge of 30% increase in the incoming customer care calls as a lot of their users were struggling with internet bandwidth issues with most of the population started working from home. While the large BPO industry in countries like India and the Philippines were struggling to get their employees working from home, some of the US and UK businesses have canceled the contracts and moved the jobs back to their respective home countries in order to comply with the regulations around data security. This has invariably increased the customer care spend for these businesses.

All these have resulted in businesses looking towards AI Chatbot powered digital agents to help them cope up with the surge in demand and at the same time, keep the costs in check.

How was the AI Chatbot adoption before 2020?

As I mentioned earlier, AI Chatbot concept was seen more as an R&D investment rather than a viable solution to automate the customer care center operations. We saw some early success with insurance companies, banks, airlines, and real-estate companies. But it was always a hard sell to the majority of the businesses primarily because of below reasons,

  • the existing customer care support process was reliable and relatively cheaper when outsourced to countries like India, Philippines
  • there was more emphasis on customer loyalty management, providing the human touch
  • the Natural Language Processing (NLP) technology was not very evolved and fool-proof to be considered as a real alternative
  • the demand was predictable and the training materials were designed to train humans and not AI

But, several technology companies like us have been relentless in their efforts to solve the above-mentioned problems seen with AI Chatbot technology. The NLP accuracy has improved a lot (proof: Alexa, Google Home, Siri) and the leading cloud platforms have launched these NLP techs as full-fledged services for developers to integrate them and build end-to-end AI Chatbot solutions (Amazon Lex, Microsoft LUIS, Google DialogFlow).

How does the Contact Center AI actually work?

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  • The customer calls the customer care support number
  • The contact center software, once it receives the call, will check with the internal customer database (or CRM) to identify the customer
  • The contact center software will then route the call to the workflow tool which will interact with the customer and identify the entity & intent from the customer’s query
  • Based on the preset business logic (or algorithm), the workflow tool will then call the right set of application APIs to resolve the customer’s queries or pick the appropriate response from the AI Chatbot

Below is an architecture diagram that depicts a Contact Center AI implementation on AWS using Amazon Connect (cloud-based contact center software), Amazon Lex (cloud-based NLP service), and Amazon Lambda (cloud-based workflow service).

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So how do you go about AI adoption for your Contact Center AI?

Rome was not built in a day. So is the case with your vision of bringing in AI automation for a large part of your customer care process. Projects in this space would fail and leave a bad taste if we embark on this journey with very high expectations and immediate results. And I have witnessed the pain several times from close quarters. So how does one go about adopting AI for their contact center?

Step 1: Analyse your existing customer care process and identify the low hanging fruits which can be moved to the AI model quickly (almost all AI Chatbot consulting & product companies can help you with this).

Step 2: Segregate the queries and workflows which can be handled by a simple ‘if-else’ rule vs an ‘intent-entity’ identifying the NLP model. Using NLP for queries that can be handled by a simple ‘if-else’ rule will be an overkill and will bring down the accuracy of your NLP engine.

Step 3: Once you have experienced a fair amount of success with the NLP powered model to identify intents, entities in answering customer queries, bring in Machine Learning to further improve on the accuracy of intent identification, bot training, and customer experience management. Yes, there is a whole different world out there already in the field of Contact Center AI. 🙂

Why AI chatbots win over application design?

I often get this very common question on ‘why chatbots when you have beautifully designed applications which can do the job?’. I totally agree with the fact that the apps with better UX will make a customer’s life easier. But the problem with apps is that you will have to adhere to workflow which has been designed in the app. You cannot skip any step, you cannot change your inputs as you wish. The AI Chatbot however allows you to interact the way you would interact with a human and not a machine. You need not learn to use an app (though the learning curve may be less for better-designed apps), you can simply post your query and get your answers or post your intent and get the workflow executed (like policy claims, refund processing, airline reservations, blocking credit cards, etc).

Let the customers interact with your business in their natural way. AI Chatbots allow you to achieve that and this goes a long way in customer experience.

What should customers look for while choosing to embark on this path?

Building and launching your Contact Center AI solution powered by chatbot agents is just the first step. I see a lot of customers struggling with managing the bot they launch and further improve them on a continuous basis. This leads to a low customer satisfaction rating and eventually resulting in a failed project.

Any business looking to implement Contact Center AI to automate their customer care process should consider below check-points.

  1. Check if a solution like Contact Center AI will actually improve efficiency and bring down costs. If your existing support model is broken, do not embark on this before fixing the overall support process.
  2. Choose a bot management platform (like which will not only help you with building and launching the AI chatbots which will power your contact center, but also help you track the performance of the bots closely.
  3. The bot management platform should allow you to pick up anomalies and help you train the new queries quickly.
  4. The bot management platform should also allow the Contact Center AI solution to handover the call to a human agent if the bot fails to resolve the customer’s queries.
  5. And the most important part, the bot management platform should have rich analytics embedded in the tool which will allow you to track your customer experience score on a real-time basis. This will help you course-correct in your bot training process and will prevent you from experiencing negative reviews in your customer care process.

How will this evolve in future? Will the Contact Center AI replace humans?

No. The Contact Center AI will not replace humans entirely. A healthy model will have a good mix of AI chatbots and human agents working hand-in-hand to support the customers’ queries. The below architecture is highly recommended when you are looking to implement a Contact Center AI for your business.

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I am seeing an increase in Contact Center AI adoption from businesses in industries including insurance, food delivery, e-commerce, healthcare, airlines, telco, banks, etc.

If you have been mulling with the idea of introducing AI into your businesses, the time is here for you to initiate the AI adoption. I would suggest you start with the Contact Center AI solution. It works. And it is one of the mature AI solutions that you can adopt.

The Evolution of Serverless Computing

By | Powerlearnings | No Comments

Compiled by Kiran Kumar, Business analyst at Powerup Cloud Technologies

Contributor Agnel Bankien, Head – Marketing at Powerup Cloud Technologies


Serverless computing also known as serverless architecture or Function-as-a-Service is an emerging cloud deployment model provided by cloud service providers to govern server management and infrastructure services. The blog helps understand how serverless is different from the other cloud computing service models and who should be using it, lists its architectural and economic impact on cloud computing and states a few features like serverless stacks and event driven computing, that has successfully brought about a revolutionary shift in the way businesses are run today. 


1. What is Serverless Computing?

2. Other Cloud Computing Models Vs. Serverless

3. Serverless Computing – Architectural impact 

4. Serverless Computing – Economic impact 

5. Who Should use a Serverless Architecture?

6. How Serverless has Impacted Cloud Computing?

7. Serverless Computing Benefits and Drawbacks

7.1 Reduces Organizational Costs

7.2 Serverless Stacks

7.3 Optimizes Release Cycle Time

7.4 Improved Flexibility and Deployment

7.5 Event based Computing

7.6 Green Computing

7.7 Better Scalability

What is Serverless Computing?

Servers have always been an integral part of computer architecture and today, with the onset of cloud computing, the IT sector has progressed dynamically towards web based architecture further leading the way to serverless computing. 

Gartner estimated that by 2020, 20% of the world’s organizations would have gone serverless. 

Using a virtual server from a cloud provider not only offloads their development team from taking care of server infrastructure but also helps the operations team in running the code smoothly.  

Serverless computing, also known as serverless architecture or function as a service (FaaS) is a cloud deployment model offered by cloud service providers, to govern server and infrastructure management services of their customers.  

This model provisions for allocation of resources, equipping virtual machines, container management and even tasks like multithreading which are built into the application code, thus reducing the responsibility and accountability of software developers and application architects.

As a result, the application developers can solely focus on building the code more efficiently while cloud providers maintain complete transparency with them.

Actual physical servers are nevertheless used by cloud service providers to implement the code into production; but developers are least concerned with regards to executing, altering or scaling a server.

An organization seeking serverless computing services is charged on a flexible ‘pay-as-you-go’ basis, where you pay for only the actual amount of resources utilized by an application. The service is auto-scaling and paying for a fixed amount of bandwidth or servers like before has become redundant.

With a serverless architecture, the focus is mainly on the individual functions in an application code, while the cloud service provider automatically provisions, scales and manages the infrastructure required to run the code.

Other Cloud Computing Models Vs. Serverless

Cloud computing is the on-demand delivery of services pertaining to server, storage, database, networking, software and more via the Internet.

The three main service models of cloud computing are Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) with serverless being the newest addition in the stack. All four services address distinct requirements, supplement each other and focus on specific functions within cloud computing and therefore commonly referred to as the “cloud-computing stack.”

Serverless computing ensures server provisioning and infrastructure management is handled only by the cloud provider on-demand on a per-request basis with auto-scaling capabilities. It shifts the onus away from developers and operations, thus mitigating serious issues like security breach, downtime and loss of customer data that otherwise proves uneconomical.

In the conventional cloud computing set up, the resources are dedicatedly available irrespective of whether it is put to use or idle while serverless enables customers to pay only for resources being used, which means that serverless is capable of delivering the exact units of resources in response to a demand from the application.

To elaborate further, applications are framed into independent autonomous functions and whenever a request from a particular application comes in, the corresponding functions are instanced and resources are applied across relevant functions as needed. The key advantage of a serverless model is to facilitate whatever the application calls for, whether it is additional computational power or more storage capacity.

Traditionally, the process of spinning up and maintaining a server is a tedious and risky task that may pose high security threats especially in case of misconfigurations or errors. On the FaaS or serverless models, virtual servers are utilized with nominal operations to keep applications running in the background. 

In other cloud computing models, resource allocation occurs in sections and buffers need to be accommodated for, in order to avoid failures in case of excess loads. This arrangement eventually leads to the application not always operating in full capacity resulting in unwanted expenses. However, in serverless computing, functions are invoked only on demand and turned off when not in use enhancing cost optimization.

Serverless Computing – Architectural Impact 

Rather than running services on a continuous basis, users can deploy individual functions and pay only for the CPU time when their code is actually executing. Such Function as a Service (FaaS) capabilities are capable of significantly changing how client/server applications are designed, developed and operated. 

Gartner predicts that half of global enterprises will have deployed function Platform as a Service (fPaaS) by 2025, up from only 20% today.

The technology stack for an IT service delivery can be re-conceptualized to fit the serverless stack across each layers of network, compute and database. A serverless architecture includes three main components:

  • API Gateway 
  • FaaS
  • DbaaS,

where the API Gateway is nothing but a communication layer between the frontend and FaaS. It maps the architectural interface with the respective functions that run the business logic.

With the abdication of servers in the serverless set up, the need for distribution of network or application traffic via load balancers takes a backseat as well.

FaaS helps execute codes in response to events while the cloud provider attends to the underlying infrastructure associated with building and managing microservices applications.

DbaaS is a cloud based backend service that basically helps get rid of database administration overheads.

For serverless architectures, the key objective is to divide the system into a group of individual functions where costs are directly proportional to usage and not reserved capacity. All benefits of bundling and individual services sharing the same reserved capacity become obsolete. FaaS provisions for development of secured remote modules that can be maintained or replaced more efficiently.

Another major development with the serverless model is that it facilitates client applications to directly access backend resources like storage, with appropriately distributed authentication and authorization techniques in place.

Serverless Computing – Economic Impact 

The pay as you go model offers a considerable remunerative benefit, as users are not paying for idle capacity. For instance, a 300 milliseconds service task that needs to run every five minutes would need a dedicated service instance in the traditional setup, but with a FaaS model, organizations will be billed for only those 300 milliseconds out of every five minutes, resulting in a potential saving of almost 99.5%

Also, as different cloud services are billed according to its utilization, allowing client applications to connect directly to backend resources can optimize costs significantly. The costs of event-driven serverless cloud computing services rises with the increase in memory requirement and processing time and any service that does not charge for execution time adds to cost effectiveness.

Who Should Use a Serverless Architecture?

In the recent past, Gartner identified Serverless computing as one of the most emerging software infrastructure and operations architecture stating that going forward, serverless would eliminate the need for infrastructure provisioning and management. IT enterprises need to adopt an application-centric approach to serverless computing, managing APIs and SLAs, rather than physical infrastructures. 

Organizations looking for scalability, flexibility and better testability of their applications should opt for serverless computing. 

Developers wanting to achieve reduced time to market with building optimal and agile applications would also benefit from serverless architecture models.

The need to have a server running 24/7 is no longer relevant and the module-based functions can be called by applications only when required, thus incurring costs only while at use. 

This in turn paves the way for organizations to have a product based approach where a part of the development team can focus on developing and launching new features without the hassle of having to deploy an entire server for the same. 

Also, with serverless architecture, developers have the option to provide users with access to some of the applications or functions in order to reduce latency.

Running a robust and scalable server along with being able to reduce the time and complexity of the infrastructure is a must. With serverless, the effort required to maintain the IT infrastructure is nominal as most of the server related issues are resolved automatically.

One of the most preferred cloud serverless services is AWS Lambda, which tops the list when it comes to integrating with other services. It offers features like event triggering, layers, high-level security control and online code editing. 

Microsoft Azure functions and Google Cloud functions that offer similar services by integrating with their own set of services and triggers are a close second.

There are players like Auth0, AWS Cognito UserPools and Azure B2C that offer serverless identity management with single sign-on and custom domain support while implementing real-time application features are provisioned by platforms like PubNub, Google Firebase, Azure SignalR and AWS AppSync. 

Amazon S3 by AWS is a leader in file storage services and Azure Blog Storage is an alternative to it.

Azure DevOps and the combination of AWS Code Commit, AWS Code Build, AWS Code Pipeline and AWS Code Star services cater to the entire DevOps management with tools like CircleCI, Bamboo focusing mainly on CI/CD functions. 

Thus, there are numerous serverless offerings in the market to evaluate and choose from, based on the platform that an organization is using with respect to their application needs.

How Serverless has Impacted Cloud Computing?

In a recent worldwide IDC survey of more than 3,000 developers, 55.7% of respondents indicated they are currently using or have solid plans to implement serverless computing on public cloud infrastructure.

While physical servers are still a part of the serverless set up, serverless applications do not need to cater to or manage hardware and software constituents. Cloud service providers are equipped to offer lucrative alternatives to configuration selection, integration testing, operations and all other tasks related to infrastructure management. 

This is a notable shift in the IT infrastructure services. 

Developers are now responsible primarily for the code they develop while FaaS takes care of right sizing, scalability, operations, resource provisioning, testing and high availability of infrastructure. 

Therefore, infrastructure related costs are significantly reduced promoting a highly economical business set up.

As per Google trends, serverless computing is gaining immense popularity due to the simplicity and economical advantages it offers. The market size for FaaS services is estimated to grow to 7.72 billion by 2021.

Serverless Computing Benefits and Drawbacks

Serverless computing has initiated a revolutionary shift in the way businesses are run improving the accuracy and impact of technology services. Some of the benefits derived from implementing a serverless architecture are:

Reduces Organizational Costs

Adopting serverless computing eliminates IT infrastructure costs, as cloud providers build and maintain physical servers on behalf of     organizations. In addition, servers are exposed to breakdown, require maintenance and need additional workforce to deploy and operate them on a regular basis, all of which can be excluded by going serverless. It facilitates  enhanced workflow management, as organizations are able to convert operational processes into functions, thus, maintaining    profitability and bringing down expenses to a large extent. 

Serverless Stacks

Serverless stacks act as an alternative to the conventional technology stacks by creating a responsive environment to develop agile        applications without being concerned about building complicated application stacks themselves.

Optimizes Release Cycle Time

Serverless computing offers microservices that can be deployed and run on a serverless infrastructure only when needed by the application. It enables organizations to make the smallest of application-specific   developments, isolate and resolve issues and manage independent applications as well. According to a survey conducted, serverless        microservices have proven to bring down the standard release cycle from 65 to just 16 days.

Improved Flexibility and Deployment

Serverless computing microservices provide flexibility, technical support and clarity needed to process data owing to which organizations can boost a more consistent and well structured data warehouse. Similarly, since remote applications can be created, deployed and fixed in serverless, it is feasible to schedule specific automated repetitive tasks to enhance quick deployments and reduce the time to market.

Event based Computing

 With FaaS, cloud providers are able to offer   event driven computing methodologies where molecular functions respond to application needs when called for. Therefore, developers can focus only on building codes allowing organizations to escape the conditional time-consuming traditional workflows. It moreover reduces the DevOps costs and lets developers focus on building new features and products.

Green Computing

It is important for organizations to be mindful of the climatic and environmental changes in today’s times. With serverless computing,      organizations can operate servers on demand rather than run servers at all times, ensure energy consumption is reduced and help decrease the amount of radiation shed from actual physical servers and data centers.

Better Scalability

Serverless is highly scalable and accommodates growth and increase in load   without any additional infrastructure. It is researched that 30% of the world’s servers remain idle at any point in time and most servers only utilize 5%-15% of their total capacity, which makes it best to opt for       scalable serverless solutions.

However, organizations need to be wary of the downside of serverless computing as well.

  • Not Universally Suitable 

Serverless is best for transitory applications and not efficient if workloads have to be run long-term on a dedicated server

  • Vendor Lock-in

Applications are entirely dependent on third party vendors with organizations having minimal or no control over them. It is also difficult for customers to change the cloud platform or provider without making changes to the applications.

  • Security Issues

Major security issues may arise due to cloud providers conducting multi-tenancy operations on a shared environment in order to use their own resources more efficiently.

  • Not Ideal for Testing  

Some of the FaaS services do not facilitate testing of functions locally assuming that developers will use the same cloud for testing.

  • Practical Difficulties

A scalable serverless platform needs to initialize or stop internal resources when application requests come in or when there have been no requests for a long time. Usually when functions handle such first time requests, they take more time than usual triggering an issue called cold start. Additional overheads may be incurred for function calls if the two communicating functions are located on different servers.

Serverless computing is an emerging technology with considerable scope for advancement. In the future, businesses can anticipate a more unified approach between FaaS, APIs and frameworks to overcome the listed drawbacks. 

As of today, serverless architecture gives organizations the freedom to focus on their core business offerings in order to develop a competitive edge over their counterparts. Its high deliverability and multi-cloud support coupled with the immense opportunities it promises, makes it a must-adopt in any organization.

“DRaaS” Disaster Recovery-as-a-Service and the Data Protection Imperative

By | CS, Powerlearnings | No Comments

Compiled by Kiran Kumar, Business analyst at Powerup Cloud Technologies

Contributor Agnel Bankien, Head – Marketing at Powerup Cloud Technologies


For efficient and uninterrupted IT operations it is important that all organizations formulate an effective disaster recovery plan. However, disaster recovery on cloud takes a whole new dimension with centralized infrastructure, bundled software and virtual servers. In order to ascertain systematic and cost effective data back ups and recovery, organizations need to determine what type of disaster recovery would suit them the best. The blog aspires at providing a brief on traditional data center versus cloud DR before moving on to the implications of cloud DR and how data can be safeguarded on the cloud. 


1. DR on-premise Versus DR on Cloud

2. What Changes in the Cloud?

2.1 Reduced Downtime

2.2 Easy and Secure Deployment

2.3 Faster Turnaround Time

2.4 Cost Effectiveness

2.5 High Availability

2.6 Reliability and Business Continuity

3. Data Protection on Cloud  

3.1 Authentication and identity 

3.2 Access Control

3.3 Encryption

3.4 Deletion of Data

3.5 Data Masking 

DR on-premise Versus DR on Cloud

Disaster Recovery (DR) is the process of enabling recovery or business continuation of IT functions, systems, and infrastructure performances in case of unforeseen events such as natural disasters, data security breaches, or any other calamity disrupting normal business operations.

It is vital for every organization to have a disaster recovery plan that states the backup and recovery strategies to be taken before, during, and after a disaster in the interest of recovering and safeguarding their business IT infrastructure. 

While on-premise servers offer more control, privacy, and offline data access, they are often expensive given the cost incurred from hardware, software, and skilled resources required to run them. Moreover, conventional DR solutions limit scalability and are incompetent in protecting data during a disaster.

Disaster recovery on the cloud takes a completely different approach as the entire server, including the operating systems, applications, patches, and data are encapsulated into a single software bundle or on virtual servers.

Due to the virtual set up, it is regarded as an infrastructure as a service (IaaS) solution where centralized data on the remote cloud server can be backed up to an offsite data center and wheeled around on a virtual host within no time.

However, if the business is heavily invested in on-premise solutions, decision-makers and stakeholders need to arrive at a reasonable basis on when and how to make a shift to the cloud and whether it is truly necessary. Organizations cannot afford to be carried away by the cost-effectiveness and benefits associated with the cloud.

What Changes in the Cloud?

With cyber-attacks and system failures occurring frequently coupled with the rise in the demand for systematic and cost-effective data recovery and backups, organizations are now more aware and turning to invest in DR on cloud services. Organizations may find on-premise DR as the right fit for some workloads, while cloud DR may be most suitable for others. Both alternatives can be utilized consensually to arrive at the best DR protection solutions. Let us look at some of the most significant benefits offered by cloud disaster recovery:

Reduced Downtime

As virtual setups are hardware-independent, Cloud DR, whether run solely or as a service (DRaaS) makes it easy for critical data and all applications on cloud to be safely and accurately replicated on multiple data centers.

This eminently decreases the recovery time compared to conventional (non-virtualized) disaster recovery methods where usually, servers first need to be loaded with the OS and application software and patched to the last configuration used in production before the data can be restored. 

A cloud-based disaster recovery solution also offers increased scalability and flexibility of data while engaging minimum resources to run the setup on the cloud.

Easy and Secure Deployment

Cloud DR facilitates organizations to configure and build customized architecture as per their business needs. Whether it is the security and control of a private cloud deployment, the cost-effectiveness, and ease of a public cloud, or hybrid, which is the best of both solutions, cloud DR ascertains unique opportunities to transform and secure businesses efficiently and with greater agility.

Faster Turnaround Time

The periodic online backups between data centers on cloud have almost dispelled the offsite tape backup practices. With the cloud DR taking over, maintaining a cold site disaster recovery facility has also become redundant as a cost-effective backup of critical servers can be spun up in minutes on a shared or private cloud host platform.


Additionally, SAN-to-SAN replication with its centralized repository of archived data helps duplicate data between multiple storage sites and can easily support private cloud environments providing fast recovery times (RTO of 1 hour and RPO of minutes). Conventional DR systems, restrained by their high costs and testing related challenges were unable to facilitate the same.

High Availability

One of the most stimulating capacities of cloud disaster recovery is the ability to provide multi-site availability. 

In case of a disaster, SAN replication not only provides swift failover to the DR site but also capacitates reinstating to the production site once the DR test or disaster event is taken care of. 

Reliability and Business Continuity

Integrated backup and disaster recovery for on-premise as well as cloud workloads promote centralized management that simplifies data protection across the entire cloud infrastructure. One-click automated DR ensures timely recovery, reduces network congestion during backup, and clones applications and systems across multiple cloud accounts.

Moreover, cloud disaster recovery proves highly beneficial allowing organizations to regulate the costs and performance of the DR platform. In case of a disaster, applications and servers that are considered less critical can be rendered with fewer resources, while ensuring that critical applications that need instant attention are catered to with immediate effect in order to keep the business running through the disaster.

With cloud computing, there is zero onsite hardware building cost, significant high-speed recovery time, continuous system availability, and data backup that is feasible every 15 minutes. Eventually, in the long run, disaster recovery becomes much more cost-effective, secure, and scalable despite the fixed on-going cloud costs incurred.

Some of the most cost-effective cloud-based disaster recovery platforms are AWS, Azure, and GCP. They offer infrastructure and data recovery solutions, solutions that provide data backup, minimum downtime while protecting major IT systems.

Data Protection on Cloud  

According to research conducted by ESG 38% of organizations’ data is expected to be cloud-resident within 24 months. With data being back-uped across multiple data centers it is essential to understand some of the common methods used to protect your data on cloud.

Cloud data protection is the process of safekeeping stored, static, and moving data in the cloud also known as Data Protection as a Service (DPaaS), designed to execute the most optimal data storage and security methodologies.

Cloud data protection provides data integrity, states policies, and measures that ensure cloud infrastructure security and creates a compatible data storage management system.

As organizations are accumulating and moving large amounts of data on cloud, it is highly challenging for them to perceive where all their applications and data on cloud are. 

With third-party infrastructure predominantly handling enterprise cloud environments, there is a loss of control over who, from which device, and how their data is being accessed or shared resulting in low visibility of operations.

Even with organizations and cloud providers customarily sharing responsibilities for cloud security, organizations often have low insight on how cloud providers are storing and securing their data even though sophisticated security measures are set in place.

Besides, multiple cloud providers offer varied capabilities that can cause inconsistent cloud data protection and security in addition to other security issues like breaches, malware, loss, or theft of sensitive data or application vulnerabilities. 

A recent survey reveals that 67% of cybersecurity professionals are concerned about protecting data from loss and leakage, 61% worry about threats to data privacy, and 53% of them about breaches of confidentiality. 

Therefore it is hardly surprising that companies are heavily confining to data protection and privacy laws and regulations with the data protection market projected to surpass US$158 billion by 2024. 

Protecting cloud data is much like protecting data within a traditional data center. Authentication and identity, access control, encryption, secure deletion, integrity checking and data masking are all data protection methods that have applicability in cloud computing. 

Authentication and Identity 

Centralized authentication of users based on a combination of authentication factors like a password, a security token, or some intrinsic measurable quality such as a fingerprint is the first step to data safety. It promotes proactive identity and eliminates suspicious user behavior.

While single-factor authentication is based on only one authentication factor, stronger authentication requires two-factor authentication based on additional features like a pin and a fingerprint for instance. 

Access Control

Effective access controls in combination with other security capabilities enable maintenance of complex IT environments by integrating voluntary ownership controls with a set of role-based permission privileges along with an access control list, naming individuals and their access modes to the objects and groups on cloud. Identity-based access controls are required to support organizational access policies where procedures are defined to secure the entire data life cycle. Mechanisms are needed to ascertain that data is accessed appropriately without malicious intent and there is limited exposure of data during backups.

This helps secure applications and data across multiple cloud environments while maintaining complete visibility into all user, folder, and file activities.


Data labeling is an information security technique that has been used widely for classified, sensitive, or confidential information that equally supports non-classified categories. The objective of information identification and categorization is to put in place a centralized framework for controls and data handling through file permissions, encryption, or more sophisticated container approaches.

On the contrary, data sometimes is treated as being equal insensitivity or value leading to sensitive data getting mixed in with non-sensitive data making it vulnerable. This in turn complicates incident resolution and can pose serious issues in case of data subject to regulatory controls.

Encryption of data is essential at the operating system and application levels where the entire set of data directories are encrypted or decrypted, as a container and access to files is through the use of encryption keys. The same method can be used to segregate identical sensitive data or categorize them into directories that are individually encrypted with different keys. File-level encryption caters to encrypting individual files instead of the whole directory or hard drive. Lastly, the application can also manage the encryption and decryption of application-managed data.

 Securing data integrity and confidentiality while data is in motion is of utmost priority and this can be achieved by utilizing encryption combined with authentication to create a secure channel where data can pass to or from the cloud. Thus, in case of a violation, data remains confidential and authentication assures that the parties communicating data are authentic.

Deletion of Data

To delete sensitive data on the cloud, it is necessary to verify if data is hygienic and how it intends to be deleted otherwise the data is at risk of being exposed. Moreover, deleted data can still be accessed from archives or data backup bundles even after it is deleted. For instance, if a subscriber deletes a portion of the data and the cloud provider backs up that data every night and archives tapes for 6 months, that data still exists. Accommodating this in the Information Security Policy when adopting cloud is of prime importance to its integrity.

Data Masking 

Data masking is a technique used to conceal the identity of sensitive information while keeping it operational. It is the process of preserving data privacy by substituting actual data values with keys to an external lookup table that holds the actual data values. Masked data values can be processed with lesser controls than if the original data was still unmasked.

Cloud data protection is certainly crucial as organizations are not only able to secure their cloud set up but also attain enhanced visibility into their compartmentalized centralized data repository. Companies are better placed at defining regulatory policies, governing their cloud, and proactively mitigating risks to prevent data loss and disruption.

It is difficult to predict where technology is headed but it is clear that on-premises DR solutions are now seen as a precursor to cloud-based DR solutions.

Rise of ‘x’Ops

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Compiled by Kiran Kumar, Business analyst at Powerup Cloud Technologies

Contributor Agnel Bankien, Head – Marketing at Powerup Cloud Technologies


Rapid technological advancements and emergence of cloud-based infrastructure and tools have paved way to a more integrated and automated approach to cloud practices in the IT sector. This has given rise to a trending umbrella term called – xOps. In this blog, the first in the series, we look at the four major ops functions of xOps – DevOps, SecOps, FinOps and CloudOps, broadly categorized under cloud management and cloud governance and will also try to analyze whether xOps will replace the current IT operational methods going forward. 


1. Unfolding the term XOps

2. The XOps umbrella

2.1 DevOps

2.2 SecOps

2.3 FinOps

2.4 CloudOps

3. The state of xOps

4. Approach to xOps

5. Will xOps replace IT operations? 

6. Automate and align xOps

Unfolding the term ‘x’Ops

In the present age, with increased work from home circumstances, geographically distributed teams and dominant technological advancements, organizations are obliged to adapt to more contemporary and flexible work culture. 

In the near future, there is an even greater probability of IT industries working remotely due to the rapid emergence of cloud-based infrastructure and tools. Therefore, the distributed teams need to administer means to integrate the way they function. The development, security, network and cloud teams need to work jointly with IT operations to ensure reliability, security, and increased productivity. 

This has paved the way for ‘x’Ops, an upcoming umbrella term being widely used these days to describe how business operations and customer experiences can be improved by getting the teams to communicate and collaborate better while stimulating automation techniques to build an effective IT Ops process.

Organizations are undergoing a massive cultural shift where it narrows down to operations teams adopting clearly defined roles, transparent communication, and cloud embedded functions.

The ‘x’Ops umbrella

Over the last few years, there have been four major ops functions that help run efficient cloud operations. The term ‘x’Ops has been formulated from these very cloud operations that can be broadly classified under two categories: 


What is DevOps?

DevOps is a model that enables IT development and operations to work concurrently across the complete software development life cycle (SDLC). It aims to minimize the application development process while ensuring continuous and high-quality software delivery.

The prime intent of DevOps is to build an agile environment of communication, collaboration, and trust among and between IT teams and application owners.

Need for DevOps

Prior to DevOps application development, discrete teams worked on requirements gathering, coding, testing, and deployment of the software. Deployment teams were further divided into networking and database teams. Each team worked independently, unaware of the inefficiency and roadblocks that occurred due to the silos approach.

How it works

DevOps handles these challenges by establishing collaborative cross-functional teams that are tightly integrated to maintain and run the system, right from development and testing to deployment as well as operations.

The most crucial DevOps practice is to conduct small yet frequent release updates. This is possible through DevOps practices such as continuous integration and continuous delivery processes that help cement the workflows and responsibilities of development and operations. 

Teams adopt new technologies like containers and microservices to improve automation practices via technology stack and tooling. 

This not only helps teams to exclusively complete tasks on their own but also enables the applications to run and mature consistently and swiftly.

Communication across internal and external teams is the fundamental key to aligning all sections of an organization more closely. Monitoring and logging help DevOps teams track the performance of applications to guarantee improved teamwork, greater security, delivery predictability, efficiency, and maintainability. DevOps backs integrated teams to build, validate, deliver, and support their applications and services better.

LTI Canvas DevOpsCanvas DevOps is LTIs Self-service DevSecOps platform for automated enablement and persona-based governance. Automated DevOps Enablement | Continuous Assessment | Value Stream Management | Persona-based Governance.


What is SecOps?

As per recent studies, IT and security teams struggle to collaborate well. 54% of security leaders say they communicate effectively with IT professionals to which only 45% of IT professionals agree. This mismatch needs to be addressed jointly by IT and security teams to prioritize data protection over innovation, speed to market, and cost. 

SecOps is the joint effort between IT security and operations to integrate technology and processes that reduce risk, keep data safe, and improve business agility. 

Need for SecOps

As IT operations stress upon rapid innovation and push new products to market, security teams are weighed down with identifying security vulnerabilities and compliance issues. In case of a security breach, organizations are at a high risk of losing their customers as well as their brand image leading to a sizable financial impact on business. Hence, for substantial and continuous infrastructure security, the SecOps process must integrate security and operations teams to protect business operations by fixing issues while securing the infrastructure. 

How it works

Gartner states that through 2020, “99% of vulnerabilities exploited will continue to be the ones known by security and IT professionals for at least one year.”

Therefore, the most important aspect is to establish security guardrails and monitor the security spectrum on the cloud continuously. Moreover, the SecOps team must ensure to be primarily responsible and accountable towards security incidents with proactive and reactive monitoring of the entire security scope of the organization’s cloud ecosystem.

According to Forrester Research, “Today’s security initiatives are impossible to execute manually. As infrastructure-as-a-code, edge computing, and internet-of-things solutions proliferate, organizations must leverage automation to protect their business technology strategies.”

With digitization on the rise, effective communications tools have to be leveraged to facilitate cross-functional collaboration. Additionally, enterprises that automate core security functions such as vulnerability remediation and compliance enforcement are five times more likely to be sure of their teams communicating effectively. 


What is FinOps?

FinOps, an abbreviation for Cloud Financial Management is the conjunction of finance and operations teams. 

It is the procedure of managing financial operations by linking people, processes, and technology. FinOps endorses a secure framework for managing business operating expenses in the cloud. 

Need for FinOps

The traditional IT financial model worked independently of other teams and lacked the technical modernism of the new efficient cloud-enabled innovative business practices. Limitations in infrastructure adaptability concerning business requirements only inflated the costs making the system slow-moving and expensive. Organizations needed to establish a cost control system for their cloud environments to understand what and from where costs are incurred to keep a check on the cloud spends.

Also, setting up a cost center for all business and application teams would facilitate them to have easy access to the cloud spend data, enforcing rational use of cloud.

How it works

For organizations to gain steady and robust FinOps practices, it is important to follow the three stages of FinOps on cloud: Inform, Optimize, and Operate. 

The first phase assists in the detailed assessment of cloud assets, budget allocations, and understanding industry standards to detect and optimize areas of improvement.

Optimize phase helps set alerts and measures to identify areas that need to spend and redistribute resources. It generates real-time decision-making capacity and recommends application or architecture changes where necessary. 

Operate helps in continuous tracking of costs by instilling proactive cost control measures at the resource level.

This enables distributed teams to drive the business following speed, cost, and quality. 

FinOps brings in flexibility in operations, creates financial accountability to the variable cloud spends, and helps develop best practices in understanding cloud costs better.


What is CloudOps?

CloudOps is the process of identifying and defining the appropriate operational procedures to optimize IT services within the cloud environment. 

When applications migrate to the cloud, they may need assistance to manage all products and services on cloud.

Therefore, cloud operations are a culmination of DevOps and traditional IT operations that allow cloud based platforms, applications, and data to strengthen technically while stringing together the processes and people maintaining the services.

Need for CloudOps

According to a survey conducted by Sirius Decisions, 78% of organizations have already adopted agile methods for product development. However, for organizations to accelerate agility and attain cloud smart status while keeping a check on budget overruns and wasted cloud spends, it is necessary to device cloud computing services. 

Maintaining on-premises data centers, monitoring network, and server performances, and running uninterrupted operations were always a challenge in the traditional set-up. On the other hand, with the adoption of cloud security services, accessibility to data, infrastructure, and applications from any location is safe and effortless, resources can be scaled as required and automation of operations has become elementary. CloudOps makes the system predictive and proactive and helps in enhancing visibility and governance.

How it works

Since cloudOps is an extension of DevOps and IT, it aims at building a cloud operations management suite to direct applications and data on cloud post-migration. According to the Right Scale State of the Cloud Report, 94% of enterprises are using some type of cloud service and the global cloud computing market is expected to grow to $832.1 billion by 2025.

CloudOps comprises governance tools that optimize costs; enhance security and capacity planning. It also promotes continuous monitoring and managing of applications running on cloud with minimal resources. 

Due to the cloud environment’s flexibility, scalability, and the ability to dissociate from the existing infrastructure, the system becomes less prone to errors.

With containers, microservices, and serverless functions on cloud, teams are obliged to equally align their operations without compromising on stability and productivity. 

Built-in automation cloudOps techniques provision for agility, speed, and performance-related metrics. It additionally facilitates smooth handling of service, incident or problem requests to fix cloud infrastructure and application-related issues.

Combining DevOps initiates a faster CI/CD pipeline guaranteeing continuous improvement, greater ROI with minimum risk, and consistent delivery of customer needs.

AIOPS: AI-Led Enterprise IT Operations LTI’s Mosaic AIOps uses contextual AI with asset telemetry information to present a holistic view of the IT Estate & spot issues in real-time, which helps in providing better quality support and efficient planning in IT operations activities.

The state of ‘x’Ops

Data show that a mere 17% of organizations have fully adopted DevOps while the rest are still associated with the comparatively slow-moving agile delivery processes.

The IT industry has come a long way with transitioning from traditional IT practices to adopting agile methodologies in the early 2000s with now making a swift cultural shift towards DevOps practices.  This incremental ascent towards technology and cloud has devised the concept of ‘x’Ops.

Agile to DevOps to DevSecOps

Agile did revolutionize the IT sector two decades ago, enabling teams to work at a faster pace but not necessarily in conjunction. 

With the industry eventually realizing the importance of focusing on people more than tools and processes, DevOps emerged with the intent of making diverse teams like dev, QA, and ops work in collaboration. DevOps is considered as a more streamlined and improvised version than agile with automation as its key driver. 

DevSecOps is not a significant change if organizations have already implemented DevOps practices. DevSecOps, also known as SecDevOps, is incorporating secure development best practices into the development and deployment processes of IT functions with the aid of DevOps. DevSecOps is an evolution of the DevOps concept that, besides automation, addresses the issues of code quality and reliability assurance.  

When security is the primary focus of a DevOps team, the aim is to introduce and develop security-related strategies, processes, and policies from the inception phase of the SDLC. The idea is for everyone to be responsible for security while building the application.

Traditional security validation occurs only post the design phase, which might hamper the speed and accuracy of software deliveries. DevSecOps warrants ongoing flexible coordination among developers and security teams to ensure speedy delivery of secure codes. Security testing is conducted in iterations by strategically placing security checkpoints at different stages of the SDLC. Thus, DevOps and DevSecOps allow development, operations and security teams to balance security and compliance as well as streamline the entire process without compromising on quality or slowing down the delivery cycle.

With the onset of development, security, finance, and cloud operations coming together under one umbrella, IT operations have gained immense competency in cloud-based services giving rise to a trending terminology called ‘x’Ops. 

Approach to ‘x’Ops

To elaborate further, take for instance Powerupcloud’s approach to implementing DevOps practices to a well-renowned fintech company. The objective was to transform the customer’s monolithic application into a complete microservices-based architecture. They wanted to automate the migration process along with a separate cloud account set-up for dev, test, and UAT. 

A primary cloud directory was incorporated by the DevOps team to manage users, groups, and computers as well as support numerous cloud-based third-party applications and services, thus advocating the collaborative work culture. DevOps team generated container modules for multiple resources to make them reusable and modular. 

Application stacks were broken down to make it scalable ensuring easy deployments. 

Debugging and maintenance got simpler for the dev and QA teams while automating processes enhanced code quality. 

Role-based access control on cloud ensured secured authentication, centralized log monitoring systems enabled customers to monitor and view application-specific logs on centralized dashboards, increased overall cost-effectiveness, and improved performance of the application.

In another illustration, a top foreign exchange company wanted to avail of cloud-computing services to increase its share of the global remittance market to more than 10%

For this, the customer decided to modernize its infrastructure on the cloud and run both the traditional and remodeled systems in parallel until the transition was completed. The new platform was to be portable across the cloud and on-premise set up to meet compliance regulations. 

Once the customer environment was understood, best practice architecture for deployment and an appropriate DevOps procedure was agreed upon.

Infrastructure-as-a-code service was provisioned to deploy the application smoothly.

Built-in cloud automated tools were utilized for configuration management, scheduling jobs, and batch processing.

The DevOps team established a CI/CD pipeline to automate the software delivery process and securely deploy new versions of the application while also enabling the infrastructure to run on cloud and on-premise continuously. 

Powerupcloud also supported the customer in identifying cloud equivalent solutions for their on-premise stack in use.

Will ‘x’Ops replace IT operations? 

Cloud has revamped the IT industry to a large extent and with the push to deploy faster with higher volumes at frequent intervals, organizations are taking considerable advantage of multiple cloud services that are being offered. As cloud computing gains momentum globally, IT organizations are embracing modern tooling and automation techniques that are significant components of cloud-native computing.

For example, role-based access control and encryption key management are not new practices to IT and maybe only implemented differently in a cloud environment. Whereas, practices like running containers with non-root privileges, container image scanning, and configuring a service mesh for networking are all new to the software delivery process.

With distributed teams, applications, and infrastructure, there is a lot of data to be safeguarded, which is distinctly possible only through machine learning algorithms. AI and cloud automation tools help analyze real-time system performance and health metrics to detect and prevent vulnerabilities and external threats, which cannot be managed manually. 

It is important to determine the most befitting solution for a given business need. Sometimes, the solution to “lift and shift” a monolithic application on to cloud and package it in containers works, whereas, it may be more feasible to entirely terminate an older application to replace it with a cloud-native system in other cases. 

Likewise, it is difficult to replace or refurbish legacy systems completely as Gartner indicates, “a legacy application is an information system that may be based on outdated technologies, but is critical to day-to-day operations.”

To keep pace with the new digital transformation age, organizations need to modernize their systems by implementing innovative techniques continuously. 

Although cloud computing has more advantages than traditional IT systems, it would be inappropriate to presume that ‘x’Ops would entirely replace IT operations. There is steady progress towards modernization but a good mix of the existing on-premise set-up and cloud-based systems still coexist notably in the current software industry construct.  The ability to absorb new technologies and platforms seamlessly is critical, and the reinvented IT Ops plays a crucial role in today’s times but the IT ops still need to go a long way before it can don the “all under one roof” – ‘x’Ops superset status.

Mitigating the migration bubble

By | CS, Powerlearnings | No Comments

Compiled by Kiran Kumar, Business analyst at Powerup Cloud Technologies

Contributor Agnel Bankien, Head – Marketing at Powerup Cloud Technologies


With the global cloud computing markets growing at such a rapid pace, IT organizations not just have to adapt and accommodate the new exorbitant costs of cloud migration but also manage the ongoing running and maintenance costs of existing on-premise business operations. This has lead to a cloud migration bubble and the blog will elaborate more on the factors that cause it, the necessary steps taken by CIOs in minimizing its impact via cloud as well as implementing best practices to entirely avoid or mitigate the bubble.


  1. What is the “migration bubble”?
  2. What causes it?
  3. What top CIOs have to say about it?
  4. Busting the cloud migration bubble – How the cloud can help?
  5. Examples
  6. Ways to avoid the migration bubble
  7. Conclusion

What is the “Migration Bubble? ”

Organizations today are experiencing a major cultural shift in terms of adding value to their existing business operations to bring in more agility and cost optimization. Fortune Business Insights predict that the global cloud computing market size is anticipated to hit USD 760.98 billion by 2027.

With cloud-computing practices gaining momentum at such a fast pace, it is vital to understand that while reaping cost benefits from it is inevitable, the initial investments are quite significant.

Migrating to the cloud can prove expensive, as organizations need to accommodate new costs incurred from migration while also continuing to finance the running and maintenance costs of their current on-premise infrastructure. This is what is known as the cloud migration bubble or the double bubble.

According to Amazon Web Services (AWS), the peak time and money necessary to migrate to the cloud is the “Cloud Migration Bubble.”

It is pertinent that rise in IT costs is concurrent to business growth and when organizations are in the process of building an efficient migration blueprint, it is important to first understand the current costs of running applications followed by the interim costs. 

For this, organizations need to take into account the amount of time and money spent on the initial planning, assessment, duplicating environments, third party consulting, upskilling existing resources and technology. Additionally, they also have to endure the burden of on-premise data center costs from maintaining servers, storage, network, physical space and labor required to support applications and services until the organization migrates all-in to cloud. 

When the migration is on-going, there may be a few test workloads as well as some duplicate workloads on cloud, that add to the already rising overall costs.

If migration is planned to correspond with hardware retiral, license and maintenance expiration, and other opportunities that would eventually help reduce costs, the savings as well as the process of escaping cost associated with a full all-in migration to cloud will allow enterprises to fund the migration bubble better and may even be able to shorten the duration by applying more resources when required.

What Causes it?

When migrations happen in stages, it is common to have resources sometimes running on-premise as well as on cloud while some are still in queue causing duplication of environments for which organizations end up paying for both, the production environment as well as the new cloud set up. Costs related to licensing of automated tools to speed up the migration process also instigates the bubble to grow.

Secondly, replicating data from source to target cloud followed by testing of the replication progress, which is a time consuming job, inflates the double bubble largely.

Just before the infrastructure is ready to be moved fully on to the cloud, a migration test that ensures the recovery systems are able to support critical business needs to be performed. This is known as a cutover test where the new set up coexists with the old system before its complete withdrawal until an efficient and controlled system is established.

Not taking advantage of the cost governance tools also leads to elevated expenses. It is always advisable to move to cloud in phases rather than move the applications, departments or resources one by one. It is important to evaluate which applications are good enough to move to the cloud, which ones need to be rewritten to benefit from the cloud and applications that need to be terminated for good to keep a check on the bubble.

What Top CIOs have to Say About it?

As per a recent study from SaaS network monitoring service LogicMonitor, close to 40% of business workloads are still running on-premise, whereas Tech Pro Research states that 37% of surveyed businesses are still evaluating hybrid models to help mitigate public cloud-associated risks.

While adaptation to cloud services is definitely on the rise, CIOs across organizations are still cautious when it comes to choosing the most accurate cloud service or capacity as well as taking exhaustive technical decisions owing to the vast availability of complex cloud solutions in the market.

Keeping a strict check on the already rising costs apart from being concerned about security on cloud is another challenge especially when data breaches can be attributed to poor configuration of cloud instances. It is widely believed that such errors are mainly caused by end users and cloud administrators and not by cloud providers where research from Gartner indicates that by 2020, 95% of cloud security failures will be the customer’s fault making the CIOs and CTOs accountable for their decisions. However, Gartner studies also reveal that CIOs are beginning to see cloud computing as the number one technology today and there are signs of more consideration and acceptance towards digitization needs. 

In the long run, the future leaders of technology need to redefine and magnify their existing roles and accept cloud as an efficient, cost-effective tool. They also need to continue adding value to their organizations by strategizing to build innovative solutions and products by merging new technologies with existing tools continually.

Busting the Cloud Migration Bubble – How the Cloud can Help?

Once the costs contributing to the migration bubble are understood, the next step would be to determine cost saving strategies that will drive the cloud migration process faster.

Some Unique Ways to Bust the Bubble

Organizations can opt for third-party providers to outsource their IT maintenance, which is significantly more economical than servicing from Original Equipment Manufacturers (OEMs). The strategy has guaranteed noteworthy savings of almost 50-70%.

Organizations are expected to invest in long-term contracts with OEMs for new hardware in case of breakdown or failure during migration. Instead, if third-party vendors are approached for purchase or lease of certified systems or components, organizations can attract a sizable savings of over 80% as compared to OEM prices.

The idea is for third party vendors to evaluate the organization’s existing IT set up to buy out all the hardware assets at the current fair market price. These equipment and assets can be leased back to the organization until the cloud migration process is on-going, after which, the third party vendor can discard the on-premise hardware without the organizations having to enter into any long-term maintenance contracts. This instantly generates a reasonable amount of capital that can be used to fund new cloud projects, hire consultants or build internal cloud teams.

The applications that would benefit most from cloud cost and efficiencies must be identified based on the “6Rs” — retire, retain, re-host, re-platform, re-purchase, and re-factor. Applications that would guarantee higher ROI while cutting down significantly on operation costs should be prioritized for migration. Optimizing costs helps control the migration bubble.

Cloud providers offer various migration acceleration initiatives like initial buy-ins or sponsors, consulting support, training and free service credits to provide a head start on the migration journey. Additionally, cloud service providers also offer special discounts if enterprises opt for their cloud platforms while also helping them build a robust operational foundation by providing 24/7 support.

Top cloud service providers are known to design innovative pricing techniques that tender customized per second billing, sustained or committed usage discounts, provisioning preemptible VM instances, pay as you go services with no lock-in period and zero termination costs. Enterprises thus enjoy a significant reduction in their cloud infrastructure spend which in turn helps offset the initial cost of migration.

Certified cloud architects and consultants can provide dedicated training to the organization’s resources thus accelerating the cloud migration process while invariably diminishing the migration bubble. 

As a result, though expenses may seem overwhelming while the transition happens, it is essential to understand the immediate savings as well as benefits that will follow. Thus, based on the above factors, the Total Cost of Ownership (TCO) can be calculated to perceive an optimal migration bubble analysis.


For instance, a Swedish media service provider focused on its audio-streaming platform services, had difficulties provisioning and maintaining its in-house data centers. The decision was made to move to cloud in two phases after ample planning and assessment with a dedicated cloud specialist assigned to oversee the migration. This not just helped them minimize the costs and complexity of the cloud migration but also ensured smooth and efficient product development operations while allowing their resources to focus majorly on innovation.

In another illustration, a web based Software Company’s core application that enabled software development teams to collaborate on projects in real time, wanted to improve their performance, reliability and evolution on a massive scale. They decided to migrate from their current cloud service provider to a cloud native container based infrastructure that offered increased reliability. They initiated the move by mirroring their data between both cloud providers, eventually achieving improved performance, scalability and availability post migration. They averaged from 32 minutes of weekly downtime to 5 minutes after the cloud-to-cloud move.

Ways to Avoid the Migration Bubble

The higher the time taken to migrate to cloud, the higher is the cost endured, thus inflating the bubble.  

 Therefore, every organization must build a framework to standardize their architecture, automate deployments and run operations at a low cost. The best way to avoid or keep a tab on the migration bubble is to consolidate and implement best practices from previous migration projects. A well-defined standardized infrastructure can help automate and expedite cloud migration operations. Using such a template warrants an optimized cost structure and organizations can flatten the bubble curve consistently.

Apart from implementing best practices, it is also important to define the pace of migration, anticipate the time needed to transition, identify and test replication of data and applications and define a waiting period while moving from source to target cloud. Communication within and across teams is the key to building the acceptance criteria before organizations can move on to the actual planning and assessment of migration. Re-examining estimates and schedules is a must to control and lessen the double bubble effect.


Many organizations are shifting their business operations to cloud in order to simplify infrastructure management, deploy faster, ensure scalability and availability, increase agility, enhance innovation, and reduce cost. 

With a clear idea of what comprises the existing infrastructure costs, what are the different factors and expenses contributing to the migration bubble and an estimate of the expected savings, organizations will be better placed to arrive at the payback time and projected ROI, consequently mitigating the migration bubble.

Clouding Out Technical Debt

By | CS, Powerlearnings | No Comments

Compiled by Kiran Kumar, Business analyst at Powerup Cloud Technologies

Contributor Agnel Bankien, Head – Marketing at Powerup Cloud Technologies


Technical debt is a metaphor for all software and hardware design choices that are compromised for short term gain with potentially long term pain. This blog walks us through why it is important to manage technical debt, how do we go about quantifying it and the different types of technical debts in business. It is vital to understand how cloud can help alleviate technical debt, thereby helping organizations focus on development and innovation.


1. Introduction

2. Technical Debt

3. Why Managing Technical Debt is important 

4. How do we quantify Technical Debt?

5. Types of technical debt

5.1 Architectural debt

5.2 Build debt

5.3 Code debt

5.4 Defect debt

5.5 Documentation debt

5.6 Infrastructure debt

5.7 People debt

5.8 Requirement debt

5.9 Service debt

5.10 Test Automation debt

6. Is Cloud the way out?

7. Conclusion


Large IT organizations comprising cross-functional teams, multi-products and services are dedicated to supporting and delivering software solutions on a swift and continuous basis. Such technologies and software solutions are omnipresent and undergo constant change, but the need to keep abreast with this dynamic scenario is highly demanding and can sometimes lead to ambiguity and debts if not monitored from time to time. 

Technical debt is considered healthy until it is at reasonable levels. However, If debts aren’t administered in time, businesses may face a larger impact in terms of poor or outdated product design, impaired software maintenance, and delays in delivery leading to demotivated teams and dissatisfied stakeholders.

With cloud computing gaining momentum, organizations are reaping benefits in terms of lower infrastructure deployment, maintenance costs, agility, scalability, business continuity, and increased utilization.

However, organizations must also look at resolving issues arising from technical debts by leveraging services offered by cloud providers. 

According to IDC, by 2023, 75% of G2000 companies would commit to providing technical parity to a workforce that is hybrid by design rather than by circumstance, enabling them to work together separately and in real-time. Through 2023, coping with accumulated technical debt would be the top priority where CIOs would look for opportunities to design next-generation digital platforms that modernize and rationalize infrastructure and applications while delivering flexible capabilities. 

Cloud services have the much-needed capabilities to cater to design, code, and infrastructure components of technical debts that would not just help upgrade and streamline their existing systems but will also help shift focus towards developing and delivering new and innovative products, services, and solutions.

Migrating to the cloud requires a well-coordinated effort between IT operations, infrastructure support, cloud providers, and the organization’s senior management. If an organization plans, coordinates, and executes as effectively, then technical debt can definitely be reduced or settled using cloud computing.

Technical Debt

Technical debt in layman’s terms means a debt arising out of the attempt at achieving short-term gains that actually converts to potential long-term pain. 

Many times, IT teams need to forgo certain development work such as writing clean code, writing concise documentation, or building clean data sources to hit a particular business deadline,” says Scott Ambler, VP and chief scientist of disciplined agile at Project Management Institute.

The additional cost incurred from rework because enterprises couldn’t do it right the first time either due to constraint in time, budgets, or demands, often end up experiencing increased downtime, higher operational costs and cost implied due to additional rework in the long run.

Why Managing Technical Debt is important 

When technical debt is left unchecked, it can limit your organization’s ability to try and adapt to new technologies, restrict organizations from coping with advanced market trends, reduce transparency, and limit timely deliverables.

Studies by CRL have identified that the technical debt of an average-sized application of 300,000 lines of code (LOC) is $1,083,000. This represents an average technical debt per LOC of $3.61. 

With technical and quality debts piling up over a period of time, organizations face a negative impact concerning increased cost and efforts from rework, indefinite delays, and compromise in the brand image or inferior market share.

Here are some typical use cases: 

  • Utilizing less efficient development platforms that unnecessarily increase the length and complexity of code. Studies show that modern platforms can reduce the application development lifecycle by 40-50%.
  • Delay in upgrading your IT infrastructure can have a compounding effect as unsupported hardware and software components become more expensive to maintain and operate.
  • Also, unsupported hardware and software components increase provision time resulting in increased time to market. During complex requirements, continuing to work with limited capabilities and resources can increase team fatigue. 
  • Lack of foresight while designing infrastructure can have an impact on future upgrades and change initiatives risking your entire business set-up.

How do we quantify Technical Debt?

Quantifying your problems can help you make clear decisions. Breaking it down to numbers not only makes it easier to understand, compare, analyze, and track progress but also helps create a plan of action to remediate all the detected issues.   

Technical debt can be computed with a ratio of the cost incurred to fix the system to the cost it takes to build the system; 

Technical Debt Ratio (TDR) = (Remediation Cost / Development Cost) x 100%

TDR is a useful tool that helps track the state of your infrastructure and applications. A low TDR reflects that your application is performing well and doesn’t require any upgrades. A high TDR reflects the system is in a poor state of quality and also depicts the time required to make the upgrades. Higher the TDR ratio, higher the time it takes to upgrade or restore the application.

Remediation cost (RC) is the cyclomatic complexity value of a particular project or application. RC can also be represented in terms of time, which will help determine the time taken to fix issues pertaining to a particular code function.

Development cost [DC] is the cost generated from writing the lines of code. For example, the number of lines of code multiplied by the cost per line of code will give us the total development cost incurred to build that code.

Thus, the solution is to represent technical debt as a ratio rather than a hypothetical number. With being represented as a ratio, the stakeholders are able to quantify the debts objectively and across multiple projects as the calculations contain the number of lines of code, providing an exact best and worst score percentage ratio.

An acceptable thumb rule is that codes with a technical debt ratio of over 10% are considered poor in quality. Once this is determined, the management team is directed towards working along with the development team to define strategies for eliminating the debts.

Types of technical debt

  • Architectural Debt – Architectural debt refer to debts arising out of substandard structural design and implementation that gradually deteriorates the quality of the software.
  • Build Debt – Large and frequent changes in specifications and codebases lead to build debts.
  • Code Debt – Code or design debt, represents the extra development work that arises when mediocre code is implemented in the short run, despite it not being of best quality due to deadline or budget constraints. 
  • Defect Debt – Defect debts refer to the bugs or failures found but not fixed in the current release, because there are either other higher priority defects to be fixed or there are limited resources to fix bugs.
  • Documentation Debt – Documentation debt is a type of technical debt that highlights problems like missing or incomplete information in documents.
  • Infrastructure Debt – IT Infrastructure technical debt is the total cost needed to refurbish  technology such as servers, operating systems, anti-virus tools, databases, networks, etc. in order to upgrade them.
  • People Debt – People debt is debt rising from the resources being less experienced, under skilled and sometimes having to make compromising decisions due to time or budget constraints despite knowing the repercussions of it.
  • Process Debt – Process Debt is the circumstances in which an organization adopts processes that are easy to implement instead of applying the best overall solution that would be beneficial in the long run.
  • Requirement Debt – Requirement debt is the debt incurred during the identification, validation, and implementation of requirements.
  • Service Debt – Service debt is the additional cash that is required to repay the debt for a particular period that includes the outstanding interest as well as principal components.
  • Test Automation Debt – The debt arising out of increased effort to maintain a test code because coding standards weren’t adopted in the first place.

Is Cloud the way out?

There are multiple types of debt, each unique but paying them off may or may not be a priority at a given point in time. A decade ago, there were no alternatives to running your infrastructure on data centers, hence solving interoperability issues or upgrading slower or redundant components efficiently took a lot of time. Therefore, managing technical debts was a massive challenge for almost all IT organizations.

Organizations have constantly been on the lookout for technical debt reversal strategies and the key possibilities of the cloud’s ability to address the various technical debt issues are fast catching up.

A study reveals that organizations end up spending 90% of their time on troubleshooting technical debt issues. The longer you tend to accumulate debts, the longer it takes to resolve it besides incurring higher costs the system sometimes become unfit for daily business operations.

Gartner reports that organizations spend more than 70 % of their IT budget simply operating their technology platforms, as high as 77 % in some industries, thus leaving precious little budget for enhancements and innovation.

Cloud computing has helped unlock an organization’s full potential allowing it to move towards innovative capabilities and sustainable growth while performing audits and checks to keep all kinds of debts at bay. 

Cloud services enable an organization to: 

  • Move from CapEx to OpEx model
  • Use pay-as-you-go services
  • Scale infrastructure 
  • Increase speed and agility of deployments
  • Auto-scale for better optimization and
  • Reduce maintenance cost

To begin with, it is best to opt for a hybrid cloud approach. Hybrid infrastructure is similar to that of a three-tier architecture where the application and its data is split between an on-premise and preferred cloud provider that helps optimize costs and gain increased control over the overall architecture.

In many cases, it makes sense for an organization to maintain their customer data on their data centers while hosting the application on cloud ensuring that their client data is fully secure, this arrangement is cost-effective and enables seamless business operations. In parallel to this, the application takes full advantage of the cloud technology in terms of scaling up or down depending on business needs ascertaining a win-win situation for enterprises. Furthermore, it also offers businesses the flexibility to make changes to the application at any given point in time, as it is essential to keep applications updated and flexible.

If you would like to know more about the Hybrid cloud and its advantages please refer to our earlier blog

Why hybrid is the preferred strategy for all your cloud needs

Let us look at the three most significant IT debts in today’s time and how the cloud acts as a solution to manage and control them.

Infrastructure Debt

Infrastructure must routinely be updated with new software releases to ensure known vulnerabilities are eliminated. When a device and its software are no longer in support, liabilities, as well as disparities, become exceedingly difficult and expensive to mitigate. Cloud helps manage such discrepancies, saves you from having to upgrade and replace your infrastructure periodically and also having to regularly manage software patches, scaling, distribution, and resiliency of the platforms supporting your applications and data unless your business requires complete control over the operating system required to run your applications. Lift and shift is the fastest and easiest way to cloud-based technical debt solutions, however, to derive maximum benefits from the same, organizations need to sometimes opt for PaaS offerings as well. 

Architectural and Design Debt

Cloud can redefine the way software and services are being delivered to your customers with the help of services like, 

Containers and microservices

Containers and microservices are the keys to driving innovation within organizations, especially if you have numerous customer-facing applications and services. The microservice architecture enables hassle-free and continuous software delivery with increased business agility. Containers are a repository that holds your applications, configurations, and OS dependencies in one lightweight bundle that’s easier to deploy and faster to provision. Containers help organizations to efficiently manage their applications with automated techniques. Additionally, the core of container services is open-sourced thus also enabling you to keep your budgets in check.


DNS or domain name system is often not given enough weightage but they play a huge role in aggregating multiple technologies, enabling quick response times and making sure everything runs smoothly all around your infrastructure.

Cloud technologies demand high API call rates, tasks like auto-scaling, spinning up new instances, and traffic automation for optimization. The traditional DNS servers might not be capable of supporting its fast-paced infrastructure. So teams must ensure that the necessary infrastructure requirements are met by these DNS platforms to ensure smoother operations.

Process Debt

Often, overheads in terms of technical and architectural analysis, code reviews, testing, and release management processes are not taken much into account, and these can lead to significant problems in any business environment. These factors can trap organizations in a legacy cycle and restrict them from implementing new processes. 

With cloud solutions, management teams are able to identify what suits them best as per their needs while also comprehending how new remediation processes should be accurately introduced and followed by development teams.

Clear visibility of your IT infrastructure usage patterns would mean that policies are in place which in turn would ensure consistency in monitoring, logging, and tracing activities along with streamlining the performance metrics and process telemetry.

Services like IAC (Infrastructure-as-Code) and configuration management tools help completely automate processes and minimize bottlenecks in your code delivery empowering engineers to focus on delivering business value. 


As per studies, over 95% of organizations plan to increase their cloud spends by the end of 2021 as the need for matured cloud platforms and technologies have become vital. There has been a significant surge in the demand for cloud even if the initial cloud set-up costs seem high and unwarranted. 

Organizations are beginning to understand that initial investment in cloud infrastructure and services, costs incurred from acquiring data center management tools as well as from hiring cloud-specialized resources apart from support and maintenance costs are actually worthwhile as the benefits derived from it are everlasting. 

Despite the heavy investments at the beginning, setting up a cloud environment is still considered the best trade-off and the most economical option of all. This is mainly because it drastically cuts down on daily operational expenses, keeps the systems up-to-date thus improving the uptime and efficiency of business while minimizing technical debts.

Advancements in the cloud space will always be an ongoing process that would call for continuous optimization of systems to enable organizations to progress towards innovation and modernization.