Cloud Migration, Challenges and Solutions

Cloud migration challenges and solutions

Insights into the top challenges and their mitigations in the cloud journey.




    Selecting an appropriate path for an application or a portfolio of applications is one of the most critical decision points in a cloud journey. Assessing the nature and criticality of an existing application is usually the starting place. Another critical factor to consider is the implementation (migration) cost and time for each path to cloud. The four cloud adoption options are re-host, re-platform, re-factor and re-write in the order of increasing cost, effort, cloud benefits, and TCO reduction. Out of these, re-host usually does not involve code change and is relatively simple.

    Mapping cloud operating metrics into a 3x3 matrix is a good starting point on planning for a cloud journey. In this matrix, the cloud operating metrics would move to the right if they are critical for customer intelligence applications; that would be an X factor. Another critical dimension while planning cloud migration is identifying the interface dependencies between selected application(s) and others – both inbound and outbound. These could be synchronous, asynchronous or batch.

    Understanding the application architecture, its internal organization, and inter-dependencies are critical before migration. This can be a very complex and labor-intensive task if done manually and can be error prone. Not fully understanding the existing code can lead to issues related to transactions, data corruption, session handling, and performance.

    To read more on the top challenges and their mitigations in the cloud journey, download the perspective paper here.

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      Containerized microservices optimize infrastructure

      Containerized microservices optimize infrastructure

      Migration of legacy interfaces from Oracle SOA to AWS EKS increases scalability, availability and maintainability and establishes a single version of truth across systems and functions for a global publishing house.




        A leading global publishing house with significant operations in the U.S. had multiple Systems of Record (SOR) with a point-to-point integration between them and various operational and analytics marts built on legacy technologies. This led to the divergence of critical operational data across functions, delays in month-end and quarter-end processing as well as scalability and performance issues.

        Iris Software’s team collaborated with the client to accelerate the migration of their legacy interfaces and services to AWS EKS through a phased approach that was best suited to meet the client’s need for a much faster deployment time to market. It involved:

        • Defining the microservices and containerization technology stack for the migration of services.
        • Developing containerized microservices using Spring Boot with externalized configuration to deploy into AWS EKS.
        • Registering services hosted on AWS EKS with Kong API Gateway, enabling service discovery, auto-scaling and self-healing, as well as consumption of materialized views and response preparation.
        • The migration from Oracle SOA to a loosely coupled set of capabilities with microservices architecture on AWS EKS enabled the delivery of a single version of truth to various consuming applications and channels, resulting in reduced operational issues and maintenance costs. It also ensures that the business can meet future demand spikes, minimize downtime, and maintain optimal performance.

        Learn more by downloading the full success story here.

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          API platform migration increases capacity by 6x

          Platform migration increases API capacity by 6x

          Migration of legacy API platform to Apigee and GCP and API productizing led to 6x higher capacity and 70% reduction in support tickets, enhancing business growth and customer satisfaction for a global logistics firm.




            Our client, a leader in truck transportation and logistics services with more than 50,000 customers across 33 countries, had developed customer-facing APIs using earlier generations of API platforms. These APIs connected their transportation management system with several other critical systems such as GPS tracking, warehouse management, and real-time customer portals. Scalability and reliability issues were plaguing the client’s API management system due to the legacy infrastructure and increasing numbers of APIs, leading to poor customer satisfaction and decreased competitiveness.

            The client sought a technology partner who could understand the complex business logic within the existing API structure and execute a seamless migration and modernization that would improve the performance and scalability of the APIs for its customers. A team of Apigee experts at Iris Software addressed the challenges with a comprehensive, customized four-step approach that consisted of:

            1. Outlining the migration strategy to move 18+ APIs to a more robust API Gateway
            2. Automating the migration from the legacy API Gateway to Apigee to ease customer transitions
            3. Balancing internal system loads to increase scalability and throughput
            4. Implementing Apigee analytics for improved traceability and faster mitigation of issues

            Iris’ solution provided multi-market, multi-channel and multi-partner integration as well as other positive outcomes for the client:

            • 70% reduction in support tickets related to shipment delays
            • 6x increase in API throughput
            • New revenue streams from the creation of four new API products

            Learn more by downloading the full success story here.

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              Succeeding in ML Operations journeys

              Succeeding in ML operations journeys

              As machine learning becomes increasingly prevalent in the business world, more and more enterprises are considering the cloud as a way to scale their machine learning efforts.




                The promise of AI and ML to deliver significant competitive advantages and contribute to the growth of the organization is now well established. Organizations, big and small, are adopting these to drive their strategic business goals.

                However, moving machine learning workloads to cloud has its own challenges. Organizations are realizing that operational and support requirements increase rapidly as data science and modeling teams adopt emerging AI/ML platforms on cloud.

                Machine Learning and Operations, or MLOps for short, is significantly different from traditional software development practices and requires a different way of thinking about how machine learning models are developed, deployed, and maintained. This shift can be difficult for various teams involved in governance, enablement, and support of public cloud-based capabilities that are used to more traditional approaches, and requires a change in culture and mindset.

                The power of AI/ML models and platforms and the potential for deriving significant value from them is accelerating very fast. It is increasingly becoming a critical imperative for firms to adopt these rapidly to ensure they remain competitive. However, successfully adopting these at scale would require the use of public cloud technologies. Public cloud adoption in many industries is still in the early stages, with many internal groups to work with and evolving processes and standards. By establishing clear ownership for MLOps & DataOps and simplifying adoption through the use of templates and automation, firms can overcome these challenges and scale AI/ML on cloud while ensuring overall agility and cost-effectiveness.

                To learn more about the key challenges and our learnings and best practices, download the perspective paper.

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                  Navigating distributed ledger technologies

                  Navigating distributed ledger technologies

                  Distributed ledger technology (DLT) strengthens data security, promotes transparency in transactions and can potentially revolutionize industries.




                    Today’s enterprises rely heavily on information systems to enable their business processes, which are usually managed and controlled by the respective enterprises. However, there are a lot more multilateral transactions in the modern business value cycle. These span cross-enterprise and require faster, reliable access to the latest, comprehensive information about the transactions to make them more effective and, eventually, lead to better collaboration among enterprises.

                    The reality, however, is that with decentralized information systems and each participant managing their version of truth, enterprises end up having an opaque information architecture resulting in information discrepancies, countless reconciliations, unproductive person-hours spent resolving these, increased operational risk, weakened trust, and increased cost.

                    Decentralization by way of DLT is a step towards addressing these issues, enabling companies to jointly manage, operate and use a platform to maintain a single version of truth across participants and strong cryptography to create trust and immutability, which helps reduce the issues mentioned above. The objective is to deliver tamper-proof data and transparency to all network participants in a consensually-agreed manner.

                    Distributed ledger technologies have evolved and matured over the last few years. While it came about with cryptocurrencies, the application of this technology in alternative use cases can benefit enterprises, and adoption of DLT is on the rise across industries.

                    This perspective paper addresses the evolution and application of DLT in enterprises, and how it can be further embraced to realize potential across multilateral solutions. To learn more about the pillars and eminent platforms of DLT, key challenges and industry use cases, download the perspective paper. 

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                      Release automation reduces testing time by 80%

                      PROFESSIONAL SERVICES

                      Release automation reduces testing time by 80%

                      DevOps implementation and release automation improved testing time, product quality, and global reach for a leading multi-level marketing company.

                      Client
                      A leading multi-level marketing company
                      Goal
                      Shorten the release cycle and improve product quality
                      Tools and Technologies
                      Amazon CloudWatch, Elasticsearch, Bitbucket, Jenkins, Amazon ECR, Docker, and Kubernetes
                      Business Challenge

                      The client's Commercial-off-the-shelf (COTS) applications were built using substandard code branching methods, causing product quality issues. The absence of a release process and a manual integration and deployment process were elongating release cycles. Manual configuration and setup of these applications were also leading to extended downtime. Missing functional, smoke, and regression test cases were adding to the unstable development environment. The database migration process was manual, resulting in delays, data quality issues, and higher costs.

                      Solution
                      • Code branching and integration strategy for defects / hotfixes in major and minor releases​
                      • Single-click application deployment, including environment creation, approval and deployment activities​
                      • Global DevOps platform implementation with a launch pad for applications to onboard other countries​
                      • Automated configuration and deployment of COTS applications and databases​
                      • Automation suite with 90% coverage of smoke and regression test cases​
                      • Static and dynamic analysis implementations to ensure code quality and address configuration issues​
                      Outcomes

                      Automation of release cycles delivered the following benefits to the client:

                      • Release cycle shortened from once a month to once per week
                      • MTTR reduced by 6 hrs
                      • Downtime decreased to <4 hours from 8 hours
                      • Product quality and defect leakage improved by 75%
                      • Testing time reduced by 80%
                      • Reach expanded to global geographies
                      • Availability, scalability, and fault tolerance enhanced for microservices-based applications
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                      DevOps solution improves scalability by 5x

                      LIFE SCIENCES

                      Automated app & infra deployment improves scalability

                      Automated app and infra deployment with DevOps implementation help a leading medical company launch applications in new geographies, improve time-to-market, and reduce the total cost of ownership.

                      Client
                      North America-based fertility and genomics company
                      Goal
                      Expand business reach, reduce time-to-market, and support critical compliance
                      Tools and Technologies
                      .NET 5, Vue.js, AWS Secrets Manager, AWS Transfer Family, Amazon RDS, Amazon EKS, Amazon Route 53, Amazon CloudFront, Terraform, GitLab
                      Business Challenge

                      The client wanted to expand its reach to Canada, Europe, and APAC regions to meet the requirements for a 10x increase in their user base. Legacy application infrastructure and code built on the old tech stack, with high technical debt, were slowing down the rollout of new features, making the client less competitive. The infra-deployment process was only partially automated, stretching the time-to-market to three months. The total cost of ownership was relatively high. HIPPA and PII compliance were also not supported.

                      Solution

                      Iris modernized the application into microservices, built the infrastructure using Terraform and automated its provisioning and configuration.

                      • Application developed using .NET 5 and Vue.js
                      • Architecture transformed into cloud-native
                      • AWS Managed Services, including Secrets Manager, AWS Transfer Family, RDS, EKS, Route 53, CloudFront, and S3, configured using Terraform
                      • EKS Cluster and associated components provisioned via Terraform
                      • App pushed to container registry using GitLab pipeline
                      • Secrets (API keys, database connection strings, etc.) and app images moved to EKS Cluster using S3 Bucket Helm
                      • Static code analysis, coverage and vulnerability scans integrated to ensure code quality and reduce configuration issues
                      Outcomes

                      Our DevOps solution enabled the client to achieve significant benefits, including:

                      • Application launch in Canada and Europe; Asia Pacific release in the pipeline
                      • HIPPA and PII compliance
                      • 5x scalability improvement from weekly average usage
                      • Time-to-market reduced from three months to 3 weeks
                      • Total cost of ownership lowered by 50%
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                      Order management platform transformation

                      PROFESSIONAL SERVICES

                      New platform transforms transactions processes

                      Platform transformation and multi-cloud integration improve multinational publishing company's order management, time-to-market and performance.

                      Client
                      Multinational publishing, media, and educational company
                      Goal
                      Improve order management and transaction processing capabilities
                      Technology Tools
                      AWS EKS, Kong, Salesforce Commerce Cloud (SFCC), Salesforce CRM, Jenkins, Sumo Logic, Datadog
                      Business Challenge

                      The client's order management platform was complex and had scalability issues, causing poor customer experience and loss of revenue. The platform was hosted on Oracle cloud, with data stored in different repositories. Services were also hosted in the Oracle cloud, which used the BICC extract to fetch information about order details from Oracle databases. The low performance of customer-facing applications was causing latency and very high transaction processing time.

                      Solution

                      Team Iris transformed Oracle-based SOA services into six microservices and migrated them to AWS EKS for autoscaling with self-healing and monitoring capabilities.

                      We developed services for publishing data to Salesforce CRM for quick order processing and conversions. The BICC system for diversified information and order history was enabled with real-time integration between Oracle Fusion and materialized views for data consumption.

                      Post migration, these services were registered in Kong for discovery, and a CI/CD pipeline was created for deployment using Jenkins. Sumo Logic was used for monitoring the logs, and Datadog was used to observe latency, anomalies and other metrics.

                      Outcomes

                      The order management platform transformation delivered the following benefits to the client:

                      • System performance improved by 70%
                      • Transaction processing capability increased by 4x
                      • Order processing capabilities were enhanced by 200%
                      • Total cost of ownership (TCO) was reduced by 30%
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                      API migration benefits leading logistics company

                      TRANSPORTATION AND LOGISTICS

                      Phased API migration benefits leading logistics company

                      Phased migration of Boomi APIs to Apigee helps a leading logistics company improve performance and scale without business disruption.

                      Client
                      A leader in truck transportation and logistics services
                      Goal
                      Migrate Boomi-based logistics APIs to improve performance and scalability
                      Technology Tools
                      Apigee, Boomi, Swagger, JMeter, Postman, GCP
                      Business Challenge

                      The client's existing Boomi Atom platform with logistics APIs had lifecycle and monitoring issues, with frequent and elongated downtimes, causing customer experience challenges.

                      The system did not support the logging of events, and API transactions were untraceable. Identifying the number of customers facing issues and incidents when APIs were not working was difficult. The absence of alerting mechanisms, scalability concerns, and the Boomi platform's high licensing costs were other critical challenges.

                      An optimized API governance system was required to provide an abstraction for the backend services, security, and efficiencies around rate limiting, quotas, and analytics.

                      Solution

                      Iris strategized the smooth transition of 250+ Boomi APIs, starting with 20 in the pilot phase. The entire migration was planned to occur in four waves.

                      First, the pseudocode of Boomi APIs was documented and reviewed. The team then developed proxies in Apigee X following a TDD (Test-driven Development) approach. A well-defined logging framework was provided to the client for capturing appropriate parameters for tracking API calls.

                      Seamless migration of API keys from Boomi API Management (APIM) to Apigee X apps was performed. Network routing at F5 for the individual proxies was implemented to transfer the traffic from Boomi to Apigee post migration in each wave. Process metering, monitoring, and adherence/compliance hooks were inserted into the system.

                      Outcomes

                      Our API migration solution delivered the following outcomes:

                      • Improved performance and scalability by 30%
                      • Centralized logging and alerting for both APIM and backend systems resulting in 40% MTTR (Mean Time for Ticket Resolution)
                      • Apigee analytics enablement for API traffic, request latency, response time, target errors, and transaction revenue analysis
                      • Enablement of API discovery, monetization, registration, partner onboarding, and governance
                      • Ability to integrate the system into the Apigee developer portal
                      • Eliminated Boomi licensing cost
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                      Custom analytics enable faster business decisions

                      Asset Management

                      Custom analytics enable faster business decisions

                      Optimized data and on-demand analytics deliver faster business insights and better user experience for asset management firm.

                      Client
                      U.S.-based asset management company
                      Goal
                      Streamline and improve data and analytics capabilities for enhanced user experiences
                      Technology Tools
                      Java, React JS, MS SQL Server, Spring Boot, GitHub, Jenkins
                      Business Challenge

                      The client captures voluminous data from multiple internal and external sources. The absence of quick, on-demand capabilities for business users was inefficient in generating customized portfolio analytics on attributes such as average quality, yield to maturity, average coupon, etc.

                      The client teams were spending enormous amounts of manual effort and elapsed time (approximately 12-15 hours) to respond to requests for proposals from their respective clients.

                      Solution

                      Iris implemented a data acquisition and analytics system with pre-processing capabilities for grouping, classifying, and handling historical data.

                      A data dictionary was established for key concepts, such as asset classes and industry classifications, enabling end users to access data for analytical computation. The analytics engine was refactored, optimized, and integrated into the streamlined investment performance data infrastructure.

                      The team developed an interactive self-service capability, allowing business users to track data availability, perform advanced searches, generate custom analytics, visualize information, and utilize the insights for decision-making.

                      Outcomes

                      The solution brought several benefits to the client, including:

                      • Simplified data access to generate custom analytics for end users
                      • Eliminated manual processing and the need for complex queries
                      • Enhanced the stakeholder experience
                      • Reduced response time to client RFPs by over 50%
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