##  Chapter 4: Managing Cloud Workloads and Services 

## 37 

✓ Test/development workloads:  Many organizations leverage the cloud as a platform for testing and development workloads. Using cloud services can make the process of creating and then testing applications much more cost effective and efficient. In this way, developers have access to a set of common confirmations and development tools. Testing can be accomplished in a more efficient way within a cloud environment. 

Of course, some workloads are simply not suited for a cloud implementation. One example may be a workload that needs high performance network storage. Because these workloads may need to be accessed very quickly, they may not be suited for the cloud (say in an IaaS model) where you’re dependent on the Internet for network speed. It makes sense to do a cost-benefit analysis that looks at your particular workload and what it costs you to migrate it to the cloud versus the expected benefit of that move (check out Chapter 3 for more information). 

## Looking at Workload Use Cases 

This section gives you two use cases that illustrate the kinds of workloads described in this chapter. 

## Analytics workload 

A major maker of life-science tools and integrated systems for large-scale analysis of genetic variation and functions needed a cost-effective computer infrastructure. It wanted to expand to meet growing demand for processing related to genome research without scaling its IT investment and staffing. 

A public cloud solution let this company scale operations in parallel, so it could leverage multiple virtual infrastructures to different work groups at the same time. This permitted it to offer genome processing as a service at a competitive cost per processing run. Its genome analytics application involves truly huge amounts of data, and provides a wide range of analysis in the form of outcome-focused reports and analytics. 

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