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Migrating large workloads to AWS and implementing best practice IaaS

By | Azure, Case Study | No Comments

Customer: a leading provider of cloud-based software solutions

About the customer:

The client is a leading provider of cloud-based software solutions for 200+ customers across pharmaceutical, biotech and medical device manufacturers, Contact Research Organizations (CROs) and regulatory agencies. It’s proprietary cognitive technology platform integrates
Machine Learning (ML) capabilities to automate the core functions of the product life-cycle, boosts efficiency, ensures compliance, delivers actionable insights, and lowers total cost of ownership through multi-tenant Software-as-a-Service (SaaS) architecture, thus enabling
organizations to get started on their Digital transformation. Their services and solutions are used by 4 of the top 5, 40 of the top 50 life science companies, and by 8 health authorities. Headquartered in the US, and has regional offices in Europe, India
and Japan.

The business requirement:

Being a part of the highly regulated life sciences industry, recognized the benefits of cloud a long time ago. they were one of the very first life sciences solution vendors to deliver SaaS solutions to its customers. Currently, that momentum continues as the business goes “all-in on
AWS” by moving their entire cloud infrastructure to the AWS platform. As their platform and solutions are powered entirely by the AWS cloud, the business wanted to find ways to reduce costs, strengthen security and increase the availability of the existing AWS environment. Powerup’s services were enlisted with the following objectives:

1. Cost optimization of the existing AWS environment
2. Deployment automation of
3. Safety infrastructure on AWS
4. Architecture and deployment of centralized Log Management solution
5. Architecture review and migration of the client’s customer environment to AWS including
POC for Database Migration Service (DMS)
6. Evaluation of DevOps strategy

The solution approach:

1. Cost optimization of the existing AWS environment

Here are the three steps followed by Powerup to optimize costs:
● Addressing idle resources by proper server tagging, translating into instant savings
● Right sizing recommendation for instances after a proper data analysis
● Planning Amazon EC2 Reserved Instances (RI) purchasing for resized EC2 instances to capture long-term savings

Removing idle/unused resource clutter would fail to achieve its desired objective in the absence of a proper tagging strategy. Tags created to address wasted resources also help to properly size resources by improving capacity and usage analysis. After right sizing, committing to
reserved instances gets a lot easier. For example, Powerup team was able to draw a comparison price chart for the running EC2 & RDS instances based on the On-Demand Vs RI costs and share a detailed analysis explaining the RI Instances pricing plans.
By following these steps, Powerup estimated 30% reduction in monthly spend of the customer on AWS.

2. Deployment automation Safety infrastructure on AWS

In AWS, the client has leveraged key security features like Cloud Watch and Cloud trail to closely monitor the traffic and actions performed at API level. Critical functions like Identity & Access Management, Encryption, Log management is also managed by using features of AWS.
Capabilities like AWS Guard Duty, which is a ML-based tool, which continuously monitors threats and add industry intelligence to the alerts it generates is used by them for 24/7 monitoring; along with AWS Inspector, which is a vulnerability detection tool. To ensure end to end cyber security, they have deployed an end to end Endpoint Detection and Response (EDR) solution called Trend Micro Deep Security. All their products are tested for security vulnerabilities using IBM AppScan tool and manual code review, following OWASP Top10 guidelines and NIST standards to ensure Confidentiality, Integrity and Availability of data. As part of deployment automation, Powerup used Cloud formation (CF) and/or Terraform templates to automate infrastructure provision and maintenance. In addition to this, Powerup’s team simplified all modules used to perform day to day tasks to render them re-usable for deployments across multiple AWS accounts. Logs generated for all provisioning tasks were stored in a centralized S3 bucket. The business had requested for incorporating security parameters and tagging files, along with tracking of user actions in cloud trail.

3. Architecture and deployment of centralized Log Management solution

Multiple approaches for Log management were shared with the customer. Powerup and the client team agreed on the approach “AWS CW Event Scheduler/SSM Agent”. Initially, the scope was generation of Log management system for Safety infrastructure account, later, it was
expanded to other accounts as well. Powerup team built solution architecture for Log management using ELK stack and Cloud Watch. Scripts were written such that it can be used across their client’s on AWS cloud. Separate scripts were written for Linux /Windows machines using Shell scripting and Powershell. No hard coding was done on the script. All inputs are through a csv file which would have Instance ID, Log Path, Retention Period, backup folder path & S3 bucket path. Furthermore, Live hands-on workshops were conducted by Powerup team to train the client’s Operations team for future implementations.

4. Architecture review and migration of the client’s environment to AWS including POC for Database Migration Service (DMS)

The client’s pharmacovigilance software and drug safety platform is now powered by the AWS Cloud, and currently more than 85 of their 200+ customers have been migrated, with more to quickly follow. In addition, the wanted Powerup to support the migration of one of its customer
to AWS. Powerup reviewed and validated the designed architecture. Infrastructure was deployed as per the approved architecture. Once the architecture was deployed, Powerup used the AWS Well-Architected Framework to evaluate the deployed architecture and provide guidance to implement designs that scale with customer’s application needs over time. Powerup also supported the application team for production Go-live on AWS infrastructure, along with deploying and testing DMS POC.

5. Evaluation of DevOps strategy

Powerup was responsible for evaluating Devops automation processes and technologies to suit the products built by the client’s product engineering team.

Benefits

Powerup equipped the client with efficient and completely on-demand infrastructure provisioning with hours, along with built-in redundancies, all managed by AWS. Eliminating idle and over-allocated capacity, RI management and continuous monitoring enabled them to optimize costs. They successfully realized 30% savings on overlooked AWS assets, resulting in an overall 10 percent optimization in AWS cost. In addition, the client can now schedule and automate application backups, scale up databases in minutes by changing instance type, and have instances automatically moved to a healthy infrastructure in less than 15 minutes in case of a downtime, giving customers improved resiliency and availability. The client continues to provide a globally unified, standardized solution on the AWS infrastructure-as-a-service (IaaS) platform to drive compliance and enhance the experiences of all its customers.

Customer support enablement with AWS Connect

By | AWS, Case Study | No Comments

Customer: A multinational home appliance manufacturer

 

The Problem:

There were several legacy issues with the existing system, as detailed below with the information being provided across categories including service schedules/inquiries, spare part status, service location for maintenance, product information, etc.

  • No Call Recording facility from Avaya
  • No Historical Data and Reports generation. Agents were manually generating reports daily and then aggregating them on excel every week for the weekly report
  • Public Holiday Announcement & Operational Hours changes – Ex: During Ramzan, it’s closes early, involved doing a manual recording and deploying it on the server
  • Scalability issues: A limit of 12 in a queue based on the support from the existing systems – 8 for inbound calls and 4 outbound and concurrent inbound calls
  • Average speed of answering calls was 35 seconds

The approach:

The client wanted to do a pilot project using Amazon Connect, moving from their current voice system hosted in their Mumbai region to Amazon services to achieve the following functionalities:

  1. ‎Ability to take voice calls
  2. On-call connect, an option to choose a language (English/Bahasa)
  3. Call routing based on the language proficiency of the agent
  4. Ability to record calls
  5. Ability to help supervision of calls
  6. Ability to transfer/conference calls
  7. Scalable environment
  8. The ability to generate records in real-time

Solution flow & design:

 

The steps:

  1. Customer calls into the service center number
  2. The Call is routed to AWS Connect through Twilio or equivalent ISP
  3. As per the routing profile, AWS Connect directs the call to the agent
  4. Agent will get a notification in Instaedge CRM of the incoming call, if the mobile of incoming matches with any record in the customer Database, the customer information will be displayed in the Instaedge
  5. The agent will have to log into the Connect panel separately with credentials.

Modernizing application from VMs to container deployment-the realtime case study

By | Case Study, Cloud Assessment, Migration | No Comments

Customer: One of the India’s largest online marketplace

 

Problem Statement

The customer decided to modernize its applications from running on a virtual machine (VMs) to a container-based deployment. Shifting from VMs to containers allows developers to deliver changes in a fraction of the time in a cost-effective way. And once an app is in a container, it’s portable. So the team can move it freely from AWS to Azure to Google Cloud, back to on-premise, optimizing the benefits of a hybrid environment. Customer anticipated that the demand for women fashion products would grow quickly and would spike during sales and other promotional events. As the team scaled, the deployment process became a bottleneck. The team was frequently troubleshooting deployment failures, which caused delays and missed target dates. The customer’s Azure-based platform was hosted on the Google Cloud Platform and comprised approximately 25 servers, the majority based in Central-India.

Proposed Solution

Powerup conducted detailed cloud compatibility assessments to chart out-migration of the existing platform to a scalable and highly available Google Kubernetes Engine (GKE) Cluster predominately hosted in Asia-South Region. GKE is a managed, production-ready environment for deploying containerized applications. The migration was conducted in multiple waves to ensure that customer production is not affected. At the end of the migration application, the customer was expected to bill around US$0.15 million annually.

The figure below illustrates the migration roadmap and the steps are explained further:

  • Separate Virtual Private Cloud (VPC) created for Production / Stage Environment
  • HA K8s Private cluster is provisioned through GKE
  • MySQL VM Provisioned and installed through Terraform modules
  • Kafka VM Provisioned and installed through Terraform modules
  • 4 Pool is created in Kubernetes
  • Each pool has the following stateful components
    – Elasticsearch
    – Redis
    – MongoDB
    – Neo4j

Stateless Application Microservices are deployed in each pool according to the priority of the microservices  Ingress Load balancer is deployed in the K8s for the routing the traffic to the microservices.

Benefits

Going through the modernization process helped the team increase its velocity and optimize costs. Deployments were seamless and automated. This enables the team to deploy more frequently to lower environments and reduces the amount of time it takes to roll out a new version to production. Costs came down by 40% as the customer moved to a container platform. Moreover, now that the application is running in a container, it is portable between on-premise and public cloud environments.

Cloud platform

GCP

Singapore Fintech Festival 2019

By | Events | No Comments
Visit us in the AWS pavilion at Singapore Fintech festival 2019, to witness proven applications of AI in the FSI space thought live demonstration. From leveraging OCR to automate document life-cycle processing, conversational AI to intelligently automate a host of business processes, BI dashboards for role-based insights to intelligent harvesting of an organization’s data assets amongst others. Meet our team of experts to understand how we could enable your business to get future-ready.

AWS AI Symposium – Dubai

By | Events | No Comments
Powerup an AWS Premier Partner has leveraged a host of AWS’s cognitive services to intelligently automate processes across several enterprises. Meet our AI Machine Learning experts to understand how we can enable your business get future ready, & witness some of our demos.

 

AI-based solution

By | AI, Case Study | No Comments

Customer: A leading food and agri-business company in the world

Problem Statement

One of our client is a leading food and agri-business company in the world was in the process of building an E-Commerce application for their products to ensure global access & availability. They were in need of a solution which gives them complete visibility into their Micro-services & PAAS architecture and track all the application transaction rather than a sampling of them. They also wanted visibility in User Analytics so they can analyse the conversion trends & user behaviour in the context of User Session.

With above complexity they needed a solution that gives them Automated Problem Detection & Root Cause analytics so they can focus on the findings and make the end-user experience smoother rather than investing time in finding the root cause of those problems.

Proposed Solution

After thorough evaluation Powerup recommended the use of an AI-based solution which can automatically analyse all the dependency at micro-services level and can also trace the Root Cause at code-level depth. For this Powerup leverage the capabilities & offerings of Dynatrace APM tool.

The approach

Implementation stage: Powerup implemented Dynatrace by deploying a one agent on the Kubernetes host which initiated monitoring of all the Micro-services. Within a few minutes Dynatrace could automatically discover the Application Topology map with dependencies.

Powerup also integrated Azure PAAS Service with Dynatrace to gain complete visibility in application.

Configuration stage

  1. Management zone: Powerup configured different management zones so different teams can have the visibility of relevant data.
  2. User Tagging: Powerup configured user session tagging, Key User actions and set up the conversion goals to track the revenue over user experience.
  3. Dashboard: Powerup created the all in one dashboard so in single view they can track the User Experience, Application Transactions status, Infrastructure health, API Calls and Problem detection.

Dynatrace applied Dynamic thresholding on all the detected anomalies, Powerup helped customer to understand and analyse the automated detected problems and trace the Root Cause.
Powerup ensures High availability & quick content delivery of application at a global level by managing the PAAS services in HA mode & CDN to ensure quick response.

Cloud platform

AZURE

Technologies used

Dynatrace One Agent, Dynatrace DEM, Kubernetes, AZURE PAAS Services, CDN

LTI to acquire Powerupcloud Technologies

By | Uncategorized | No Comments

A Premier Consulting Partner of AWS, Powerup bolsters LTI’s cloud consulting and digital transformation capabilities

Mumbai – October 17, 2019 – Larsen & Toubro Infotech Ltd. (NSE: LTI, BSE: 540005), a global technology consulting and digital solutions company is acquiring Powerupcloud Technologies Pvt. Ltd., a fast-growing cloud consulting company headquartered in Bengaluru, and helping clients across India, Singapore, UAE and USA. With more than 180 employees, Powerup brings a strong team of cloud consulting, artificial intelligence and data analytics professionals to LTI.

Founded in 2015, Powerup is a born-in-the-cloud, Premier Consulting Partner of AWS. It is the only AWS certified partner in APAC to have both Data & Analytics Competency and Machine Learning Competency, and one of the few certified partners of AWS with competencies across Migration, DevOps, Financial Services, Well-Architected, and Security & Compliance. With expertise across cloud, big data, artificial intelligence, and product engineering, Powerup is also a Gold Partner of Microsoft Azure and a Cloud Consulting Partner of Google Cloud Platform.

Powerup has executed over 150 projects in cloud transformation. Powerup would add two AI-based platforms to LTI’s powerful suite of offerings. CloudEnsure.io, an autonomous Cloud Governance Platform that continuously monitors an enterprise’s cloud services, detects security and compliance violations in real-time and recommends or executes appropriate fixes. The other platform is Botzer.io, an Enterprise AI Platform that helps organizations adopt AI faster across Natural Language Processing, Image Recognition, Deep Learning use-cases and saves time spent in trial-and-error experimentation.       

Sanjay Jalona, Chief Executive Officer & Managing Director, LTI, said: “We see a huge market opportunity for cloud consulting across all sectors and regions. Our clients are adopting the cloud to digitize their core operations and becoming data-driven organizations. Powerup strengthens our ability to partner with them in their cloud transformation journey. I welcome the customers, employees, and partners of Powerup to the fast-growing LTI family.”

Siva S, Founder & CEO, Powerup, said: “With its global customer base and agility, LTI makes the perfect home for the next phase of growth of Powerup. Our expertise across major public cloud platforms offers us an enormous opportunity to grow, learn, iterate and build well-engineered technology solutions for enterprises. We are excited about the potential for rapid growth and expansion with LTI.”

Powerup is the sixth acquisition by LTI since the company got listed in 2016 and fourth in this calendar year. Earlier in 2019, LTI acquired Ruletronics, a boutique Pega Consulting company, N+P (NEILSEN+PARTNER), a Temenos Wealthsuite specialist, and Lymbyc, an advanced analytics company.

About LTI:

LTI (NSE: LTI) is a global technology consulting and digital solutions Company helping more than 360 clients succeed in a converging world. With operations in 30 countries, we go the extra mile for our clients and accelerate their digital transformation with LTI’s Mosaic platform enabling their mobile, social, analytics, IoT and cloud journeys. Founded in 1997 as a subsidiary of Larsen & Toubro Limited, our unique heritage gives us unrivaled real-world expertise to solve the most complex challenges of enterprises across all industries. Each day, our team of more than 30,000 LTItes enables our clients to improve the effectiveness of their business and technology operations and deliver value to their customers, employees, and shareholders. Find more at http://www.Lntinfotech.com or follow us at @LTI_Global

About Powerupcloud Technologies Pvt. Ltd.:

Powerup is a “born-in-the-cloud” company focused on helping businesses move to the cloud. The dynamic team at Powerup strives for customer satisfaction and delivers unmatched cloud consulting services to businesses with undivided attention and support. Our consulting team helps implement, migrate and optimize cloud applications for global corporations. We also help build Big Data analytics platforms to enable enterprises to extract more value from their environment. A Premier Consulting Partner with AWS, a Gold Partner with Microsoft Azure and a Cloud Consulting Partner of Google Cloud Platform, Powerup’s certified team is here to help customers achieve more on the cloud.

More information at https://www.powerupcloud.com

More Information:

LTI to Acquire Advanced Analytics Firm Lymbyc
LTI to Acquire Germany Based NIELSEN+PARTNER
LTI Acquires Ruletronics, a Boutique Pega® Consulting Company

Making the Connected Car ‘Real-time Data Processing’ Dream a Reality

By | Analytics, Automation, AWS, Blogs | No Comments

Written by Jeremiah Peter, Solution specialist-Advanced Services Group, Contributor: Ravi Bharati, Tech Lead and Ajay Muralidhar,  Sr. Manager-Project Management at Powerupcloud Technologies

Connected car landscape

Imagine driving your car on a busy dirt road in the monsoon, dodging unscrupulous bikers, jaywalking pedestrians and menacing potholes. Suddenly, a fellow driver makes wild gestures to inform you that the rear door is unlocked, averting an imminent disaster.

In a connected car system, these events are tracked in near real-time and pushed to the driver’s cell phone within seconds. Although the business relevance of real-time car notifications is apparent, the conception of the underlying technology and infrastructure hardly is. The blog attempts to demystify the inner workings of handling data at scale for an Indian automobile behemoth and equips you with a baseline understanding of storing and processing vast troves of data for IoT enabled vehicles.

The paradigm of shared, electric and connected mobility, which seemed a distant reality a few years ago, is made possible through IoT sensors. Laced with tiny data transmitting devices, vehicles can send valuable information such as Battery Percentage, Distance to Empty (DTE), AC On/Off, Door Locked/Unlocked, etc. to the OEM. The service providers use this information to send near real-time alerts to consumers, weaving an intelligent and connected car experience. Timely analysis and availability of data, thus, becomes the most critical success component in the connected car ecosystem.

Before reaching the OEM’s notification system, data is churned through various phases such as data collection, data transformation, data labeling, and data aggregation. With the goal of making data consumable, manufacturers often struggle to set up a robust data pipeline that can process, orchestrate and analyze information at scale.

The data conundrum

According to Industry Consortium 5GAA, connected vehicles ecosystem can generate up to 100 terabytes of data each day. The interplay of certain key factors in the data transmission process will help you foster a deeper understanding of the mechanics behind IoT-enabled cars. As IoT sensors send data to a TCP/IP server, parsers embedded within the servers push all the time series data to a database. The parsing activity converts machine data (hexadecimal) into a human-readable format (Json) and subsequently triggers a call to a notification service. The service enables OEM’s to send key notifications over the app or through SMS to the end-consumer.

Given the scale and frequency of data exchange, the OEM’s earlier set up was constrained by the slow TCP/IP data transfer rate (Sensor data size: TCP/IP- 360 bytes; MQTT- 440 bytes). The slow transfer rate has far-reaching implications over the user experience, delaying notifications by 6-7 minutes. As part of a solution-driven approach, Powerup experts replaced the existing TCP/IP servers with MQTT servers to enhance the data transfer rate. The change affected a significant drop in notification send-time, which is presently calibrated at around 32-40 seconds.

Furthermore, the OEM’s infrastructure presented another unique challenge in that only 8 out of 21 services were containerized. The rest of the services ran on plain Azure VM’s. To optimize costs, automate scalability and reduce operational overhead, all services are deployed on Docker Containers. Containers provide a comprehensive runtime environment that includes dependencies, libraries, framework and configuration files for applications to run. However, containers require extensive orchestration activities to aid scalability and optimal resource management. AWS Fargate is leveraged to rid the OEM’s infrastructure management team of routine container maintenance chores such as provisioning, patching, cluster and capacity management

Moreover, MQTT and TCP IP brokers were also containerized and deployed on Fargate to ensure that all IoT sensor data is sent to the AWS environment. Once inside the AWS environment, sensor data is pushed to Kinesis Stream and Lambda to identify critical data and to call the AWS notification service-SNS. However, the AWS solution could not be readily implemented since the first generation of electric vehicles operated on 2G sim cards, which did not allow change of IP whitelisting configuration. To overcome the IP whitelisting impediment, we set up an MQTT bridge and configured TCP port forwarding to proxy the request from Azure to AWS. Once the first generation vehicles are called back, the new firmware will be updated over-the-air, enabling whitelisting of new AWS IP addresses. The back-handed approach will help the OEM to fully cut-over to the AWS environment without downtime or loss of sensor data.

On the Database front, the OEM’s new infrastructure hinges on the dynamic capabilities of Cassandra DB and PostgreSQL. Cassandra is used for storing Time Series data from IoT sensors. PostgreSQL database contains customer profile/vehicle data and is mostly used by the Payment Microservice. Transactional data is stored in PostgreSQL, which is frequently called upon by various services. While PostgreSQL holds a modest volume of 150 MB Total, the database size of Cassandra is close to 120 GB.

Reaping the benefits

While consumers will deeply benefit from the IoT led service notifications, fleet management operators can also adopt innovative measures to reduce operational inefficiencies and enhance cost savings. Most fleet management services today spend a significant proportion on administrative activities such as maintaining oversight on route optimization, tracking driver and vehicle safety, monitoring fuel utilization, etc. A modern fleet management system empowers operators to automate most of these tasks.

Additionally, preventive maintenance can help operators augment vehicle lifecycle by enabling fleet providers to pro-actively service vehicles based on vehicular telemetry data such as battery consumption, coolant temperature, tire pressure, engine performance and idling status (vehicle kept idle). For instance, if a truck were to break-down due to engine failure, the fleet operator could raise a ticket and notify the nearest service station before the event occurred, cutting down idle time.

Conclusion

With 7000 cars in its current fleet, the OEM’s infrastructure is well-poised to meet a surge of more than 50,000 cars in the near future. Although the connected car and autonomous driving segment still goes through its nascent stages of adoption, it will continue to heavily draw upon the OEM’s data ingestion capabilities to deliver a seamless experience, especially when the connected car domain transcends from a single-vehicle application to a more inclusive car-to-car communication mode. Buzzwords such as two-way data/telematic exchanges, proximity-based communications and real-time feedback are likely to become part of common parlance in mobility and fleet management solutions.

As the concept of the Intelligent Transport System gathers steam, technology partners will need to look at innovative avenues to handle high volume/velocity of data and build solutions that are future-ready. To know more about how you can transform your organization’s data ingestion capability, you can consult our solution experts here.