For businesses transitioning to the cloud, migrating existing applications to an Infrastructure as a Service (IaaS) platform via a “lift-and-shift” approach is a common first step. The lift-and-shift cloud migration model involves moving the underlying infrastructure to run on virtual servers in the cloud and then replicating existing applications to run on the (public or private) cloud platform, without redesigning them.
Migrating resource-intensive applications (data-crunching, media processing, modeling, simulation) running on mainframes can introduce performance or latency issues. It may be less expensive to leave such applications where they are.
Applications that rely on local third-party services also might not be good candidates for migration, because it might not be possible to (or the business might not be licensed to) run the third-party services in the cloud.
Starting small and migrating a single application (or part of an application) at a time is a better practice than trying to migrate everything at once. Understanding all the dependencies between applications, services, and cloud components will help you determine which part to migrate first, and whether other parts should be migrated at the same time.
Before you can lift and shift anything, you need a rock-solid understanding of how everything works together. Dynatrace lets you know what you don’t know by giving you a full picture of your entire application environment and all dependencies. With its unique Smartscape application topology discovery and mapping technology, Dynatrace automatically
To make sure you’re getting the benefits you expected from your lift-and-shift, monitor your service-level agreement (SLA) performance metrics before, during, and after migration. Dynatrace discovers all your application services before migration and automatically links them to the new equivalent cloud services—so you don’t lose historic SLA and performance data!
Monitoring application performance in the cloud can be more complex than humans can manage by themselves—there are simply too many changes happening too fast. Dynatrace self-learning capabilities and artificial intelligence–powered big data analytics were built to automate smarter monitoring in today’s dynamic environments.