Why AI is your friend when it comes to cloud migration
FYI, this story is more than a year old
Article by Dynatrace APAC CTO Rafi Katanasho
Migrating to the cloud isn’t a quick, cheap or easy process.
But the drawbacks of not making the investment to rebuild your legacy apps for the cloud means technological debt, competitive disadvantages in agility and frustrated customers left suffering poor user experiences.
Organisations need to decide which applications to move to the cloud and which to keep on-premise. Then, they must decide how to refactor those apps with cloud-native technologies or create a hybrid-cloud setup - it’s a complicated process.
Successful cloud migrations and transformation rely on automating continuous builds, integration and delivery as well as automating performance monitoring, root-cause analysis and remediation. Together with this ‘automate everything’ approach is leveraging AI.
Modern web-scale cloud applications are simply too complex to be operated by humans alone. Software intelligence builds on strong AI to oversee the health of the entire system.
Every organisation’s cloud native journey is different.
But the following are three steps every enterprise needs to take to go cloud native.
1. Set a vision for your cloud strategy.
Consider the needs of your customers, now and into the future.
What role does the cloud setup play for DevOps and your delivery pipeline?
What’s your cloud platform of choice: Public or private?
Single-cloud, multi-cloud or hybrid cloud?
These might seem like no-brainer questions, but the answers form your cloud-native building blocks.
2. Have a thorough understanding of your existing systems.
Profile applications to ensure you know how they work and baseline their performance so you can later compare them to how they’re performing in the cloud — and guarantee that they’re running better.
Or, if not, knowing just how and where they’re falling short of those baselines can be just as important.
Monitoring plays a key role - everything from creating topology maps of your entire technology stack, to mapping out interdependencies between systems, to automated performance baselining, to full stress tests.
This is all necessary to ensure you’re getting a comprehensive snapshot of your existing system architecture, service flow and performance.
3. Define the migration and transformation strategy.
Which applications are you going to retain or retire, and of those that are retained, which to lift and shift to the cloud, re-platform or refactor?
Each method has its own strengths and drawbacks.
Lifting and shifting applications is the quickest, as it requires no code modification – though the downside to that is, you’re essentially preserving its on-premise architecture, meaning the app won’t fully take advantage of its new cloud environment.
Refactoring is the most resource-intensive, as it calls for rearchitecting the app from the ground up.
Typically, this involves breaking up a monolithic application with millions of code lines into multiple, more dynamic microservices that are easy to maintain and scale.
Because this process results in an application that is purpose-built for the cloud, it also reaps the greatest ROI, with more long-term operational and cost advantages compared to lifting and shifting.
Your answers lie in automation and software intelligence.
After you’ve laid out your cloud-migration vision, profiled your legacy applications and defined your migration strategies, next comes the nitty-gritty work of the actual migration itself.
It’s a process potentially fraught with technical challenges and substantial organisational changes and this is where AI and automation are crucial.
Software intelligence and automation create visibility and actionable insights.
This empowers software engineers to have full ownership over the entire value chain: from initial coding to the deployment of the final product.
AI can also be utilised for further improving CI/CD pipelines to meet migration deadlines and ensure excellent software quality.
Software intelligence helps to close existing automation gaps like manual approval steps at decision gates or build validation.
It also provides valuable performance signatures to test new builds against production scenarios.So, are you ready?
Embarking on a cloud strategy requires serious, substantial organisational changes.
AI and automation provide the tools to make that journey as navigable and seamless as possible.
By automating performance monitoring, remediation, CI/CD pipelines, root-cause analysis, stress testing, system configurations and many more steps, AI saves IT teams a ton of tedious manual legwork and the costs and headaches that go with it.
But, more than that, AI and automation help to lay the foundation for a culture that lives and breathes DevOps and AIOps.
A fully-formed, agile DevOps culture — facilitated by AI and automation — is the key to every successful cloud transformation journey.