New Industry Report: Cloud Migration and Optimization Analysis and Insights

Over the last few years, companies have increasingly migrated their infrastructures to the cloud. In fact, Gartner predicts that by 2020, more compute power will be sold via the cloud than what is deployed in customers’ on-premises data centers. But the cloud is complex. That’s why sufficiently realizing its best benefits is dependent on having deep visibility into your infrastructure, and using this insight to choose (and actively manage) your best-fit cloud configuration - which includes instance types, storage options, availability zones, and pricing plans.  The challenge is that there are over 25 million cloud configurations available to choose from. And on top of this, these configurations are constantly changing. Unfortunately, this complexity all too often makes it difficult for companies to fully take advantage of the cloud and experience the performance and cost benefits they’re capable of.

This is where in-depth data analysis can make a huge difference for companies migrating to the cloud and optimizing their cloud environments once there. This past spring-summer, we analyzed 10,000 on-premise nodes that were migrating to either Azure or AWS. For each, we ran a detailed performance analysis on compute, storage, and network resources that included metrics such as observed peak CPU utilization, storage capacity, and IOPS. Based on this data, we identified the optimal instances to migrate to for each application on these nodes, in addition to cost savings opportunities.

We recently completed aggregating this data analysis into an industry report that yields helpful insights for successfully migrating to the cloud, and that reveals what opportunities exist for improving performance and cost savings once in the cloud. This report provides key insights such as:

  • The most common Operating Systems migrating from on-premise to AWS and Azure
  • The top instances mapped to each OS when planning for migration to AWS and Azure
  • Cost savings realized for mapping each OS on a specific instance on AWS and Azure
  • The provisioning distribution of instances by OS and in aggregate on both AWS and Azure and what this means for opportunities to improve
  • Windows vs. Linux cost savings when migrating to the cloud
  • Average cost savings when migrating to AWS vs. Azure

The following charts provide a snapshot of the results. For each, you’ll see the optimal instance types for each OS when mapped to AWS and Azure, and the cost savings realized by mapping the OS to a specific instance. Identifying the optimal instance, however, is only the first step.  Once you migrate to the cloud, you have to actively provision the capacity you purchase on the instance. Therefore, each chart also shows the provisioning distribution for the OS, which is the percentage of the instances that would be over-provisioned, under-provisioned, optimal, and idle if they were not actively provisioned.

(Mapping to the right instances in the cloud and right-sizing them on an ongoing basis for the best performance and cost benefits requires in-depth infrastructure analytics. See here for more information on what type of automated data analysis Cloudamize provides.)










Some of the key takeaways in this report include:

  • Mapping these OS types to these instances results in an average of 18% cost savings on AWS vs. 14% cost savings on Azure.
  • Companies can save more by actively provisioning the capacity they purchase on each instance on an ongoing basis: 33% of the instances on AWS are over-provisioned, while 33% and 19% of the instances on AWS and Azure respectively are idle.
  • 9% of the instances on both AWS and Azure are underprovisioned, meaning they will exceed their capacity and suffer performance issues.

See the full report for more analysis, insights, and takeaways that can help you improve the success of your cloud migration and ongoing cloud management.

Cloud Migration & Optimization Industry Report

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