- Gain Visibility Across Your Infrastructure
- Optimize Cloud Cost Management
- Increase Resource Utilization
- Identify Cloud Risks
- Migrate to the Cloud
- Manage Hybrid and Multi-Cloud Environments
- Scale Your Cloud More Efficiently
- Centralize Cloud Governance
- Looking for a solution specific to your industry?
- Contact Us
3 Common Strategies for Migrating Workloads to the Cloud
Determining which application workloads to move to the cloud and the extent to which they should be modified is a key step in the migration process. ‘Low-hanging fruit’ workloads include those with the fewest dependencies, over-provisioned infrastructure, and the least critical workloads. Preparing applications for cloud migration entails a trade-off between a quick, low-cost deployment, and having optimal performance and operating efficiency:
"Preparing applications for cloud migration entails a trade-off between a quick, low-cost deployment, and having optimal performance and operating efficiency."
1. Lift and Shift:
A lift and shift strategy keeps applications mostly as is, with the ability to make any minor adjustments needed. This approach enables faster migration and deployment. However, it fails to take advantage of a cloud platform in the same manner made possible by building a cloud-native application.
2. Partial Refactor:
Most application migrations entail at least some refactoring. In a partial refactor, certain components of an application are modified while others are left intact. This is less time and labor intensive than a full refactor, but only partially realizes the operational benefits of running an application in the cloud.
3. Full Refactor:
Though the most time consuming approach, full refactors are considered a best practice because the rebuilt applications ultimately perform better and can operate at a lower cost. Moreover, a full refactor can be regarded as an opportunity to break down monolithic applications into microservices.
Ready to Learn More?
Register for the upcoming webinar with CloudHealth Technologies, Cox Automotive, and AWS to learn how Cox Automotive was able to efficiently model their workloads for migration and optimize their infrastructure once they were running on the cloud.