Infosys TechCompass #9 - Cloud

October 19, 2022

Shifting to the cloud involves numerous difficulties. Many firms spend too much, too soon, on cloud, without performing enough research on their specific requirements. And that leads to cloud burnout. Also, the sheer number of cloud solutions often thwarts cloud migration, and many organizations revert to adopting cloud in pockets, running up the cost. This is where good migration, management, and operations tools come to the fore, with automation and embedded governance becoming the key to cloud success.

Infosys Knowledge Institute discovers capabilities and trends across migration, management, and operations in the cloud space

In recent years, enterprises have shifted from single to multicloud setups, X as a code, and policy-driven strategies. In addition, the focus on governance and compliance has resulted in specialized solutions for embedded governance from cloud vendors. Automated management of cloud systems will become mainstream in the coming years. The focus will significantly increase on intelligent workload-centric orchestration, enhanced developer empowerment, and integrated monitoring. We expect intelligent and synergized orchestration, involving the integration of enterprise workloads orchestration and network orchestration, to become popular. Likewise, solutions will emerge to enhance developer productivity through automatic service deployments using reusable templates, unified AI platforms to build and scale ML models faster, and an integrated observability platform for a unified and real-time view of services, resources, software, etc.?

Trend 3 – Cloud-neutral observability stack becomes prevalent

Enterprises are increasingly adopting multicloud environments. This gives them access to on-premises clouds and multicloud vendors to avoid vendor lock-in, add capacity, and leverage unique capabilities such as surfacing enterprise data for ML use cases. In fact, top performers in the recent cloud research by the Infosys Knowledge Institute use more than three cloud vendors, with a bias toward hybrid multicloud. This, however, leads to some challenges in observability, including vendor-specific tools, lack of all information in one place, high monitoring costs, multiple standards, etc. To monitor such complex systems, enterprises need a single pane of glass to monitor various cloud components, including applications, services, infrastructure, microservices, etc., cutting across multicloud vendors/multiclusters/multiregional deployments. Enterprises are adopting cloud-native and third-party tools like the Elastic Observability suite, Datadog multicloud monitoring suite, and LogicMonitor multicloud monitoring tool, etc., to reduce complexities in multicloud environments. These tools measure the health of application performance and behavior using vast amounts of collected telemetry data such as metrics, logs, and incidents. This lets those developing applications understand what’s wrong with a system, what’s slow or broken, why an issue occurred, and how it will affect the whole cloud ecosystem.?

A large multinational financial advisory service company is working with Infosys to implement an observability toolset and a site reliability engineering model that provides convergence of metrics, logs, traces, and AI-powered insights in a multicloud environment. Infosys is also helping a technology company implement a cloud-neutral observability stack for multicloud operations.?

Trend 4 – Automation usage rises sharply in cloud platform engineering

Automation is increasingly contributing to improved developer productivity. Multicloud vendors offer a multitude of technologies, platforms, and tools to help developers in their day-to-day tasks. Currently, developers need end-to-end frameworks and tools to manage all services and related resources such as infrastructure, deployments, pipelines, etc., in an integrated fashion. One way to achieve this is to have an integrated developer portal for a unified view of services, resources, software, etc. Developers also need a fully managed application delivery service that can automate the deployment of all types of cloud-native workloads such as containers, serverless, etc. This is done through standard and automated service templates across multicloud environments. Developers and data scientists working on AI and ML models also need a unified AI platform to build and scale ML models faster. Enterprises are looking for ready-to-use frameworks or tools that can enhance developer productivity. Some examples include AWS Proton, Google Vertex for unified AI, and Backstage. Google Vertex is out in front, with the ability to use the same dataset for different models, significantly reducing costs and errors. The model training pipeline is also automated, with a series of containerized steps that helps with generalization, reproducibility, and auditability.?

A large Japanese auto company is working with Infosys to build a cloud services consumption platform for its US business unit. Developers can use this platform to develop applications on the cloud and provide services across the application engineering lifecycle, eliminating the friction with the platform team. This enables the company to embrace agile with the DevSecOps model, which increases speed to market and developer productivity.??

Find more about these trends and their use cases here.

Read our 2022 Cloud TechCompass to know more about the key trends.

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