Abstract
As enterprises increasingly adopt the cloud paradigm, there is a need for tools that can help cloud service providers (SPs) decide on service level agreements with customers. Existing approaches have not considered several important challenges such as workload burstiness, workload uncertainty, and scalability that need to be addressed to realize such tools. This paper describes a trace-based framework developed to consider these issues. We present simulation experiments that show that our framework is scalable and provides more accurate planning insights to SPs than those that ignore complex workload behaviour such as burstiness. Furthermore, the results also illustrate how SPs can exploit our framework to assess the risks of workload uncertainty.
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