A step-by-step strategy to speed up Cloud migration

Stages of Cloud Migration

The stages of a typical Cloud migration journey are as follows:

Step 1: Locate Apps

Determine which of your company applications are most suited for cloud deployment. Enlist your operations team’s assistance in determining the most cost-effective, cloud-worthy applications to run on the cloud without disrupting your business. Prioritizing cloud-ready apps with the most significant business impact and the smallest amount of migration work above those with the most negligible business impact and the most considerable amount of effort could be part of the selection process.

Step 2: Transfer to the Cloud

Begin the migration according to your cloud strategy (e.g., rehosting, re-platforming, refactoring, and so on). Depending on the complexity of the application services, this stage could take anywhere from a few days to a few weeks.

Step 3: Verify

You’re now ready to verify the cloud migration success one step at a time until the migration is complete. This stage will expose what isn’t working—issues that impact your end-users and business—and it’s here that firms frequently make the mistake of not seeking the support of an application performance management (APM) solution.

Step 4: Evaluate and Improve

Measure and optimize the quality of your applications, as well as their availability, cloud resources, and spending. Continue to evolve cloud-based application code by implementing cloud-native services throughout this stage. Many clients that start with a rehosting migration approach rework their code to take advantage of cloud services in parts and pieces.

​​

The Power of Cognition Engine

Machine learning (ML) algorithms power Enteros, giving you the ability to:

• Anomaly detection and root cause investigation can be automated (RCA)
• Ensure that intelligent alerting and computerized actions are in place.
• Reduce the meantime to repair (MTTR) through gaining knowledge.

Our AI/ML-based RCA can automatically discover abnormalities and inform you when performance difficulties lead baselined measurements to diverge.

After the agent has instrumented your apps, you’ll see a dynamic Application Flow Map with various interactions across different services, as seen in the example below for our fake company, NextGen Financial. If you use App Services to access cloud-native services like AWS Lambda or Azure Functions, you’ll be able to see the upstream and downstream interactions between them.

​​Three graphs at the bottom of the Program Flow Map display the total Load, response time, and several errors for the entire application over a specific period. This is a beautiful place to start when looking for spikes, trends, or patterns. For a particular period, a dotted line denotes the dynamic metric baseline.

We’re inside the baseline limitations for Load (left) and Response Time (right) in the example above (middle). If these metrics show an increase or reduction, you’ll get notified via several notification channels, such as email, Slack, or PagerDuty.

With a 1.6 percent error rate, the Errors threshold (right) is higher than the baseline. We may see a list of problems that have been automatically captured by clicking “Errors” in the Transaction Scorecard. We connect one of the snapshots, /web-API/quoteService, to see a flow map view of the exact error.

Enteros

About Enteros

Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of RDBMS, NoSQL, and machine learning database platforms.