Performance Evaluation of Virtualization in Cloud Data Center
Abstract: Amazon Elastic Compute Cloud (EC2) has been adopted by a large number of small and medium enterprises (SMEs), e.g. foursquare, Monster World, and Netflix, to provide various kinds of services. There has been some existing work in the current literature investigating the variation and unpredictability of cloud services. These work demonstrated interesting observations regarding cloud offerings. However, they failed to reveal the underlying essence of the various appearances for the cloud services. In this thesis, we looked into the underlying scheduling mechanisms, and hardware configurations, of Amazon EC2, and investigated their impact on the performance of virtual machine instances running atop. Specifically, several instances with the standard and high-CPU instance families are covered to shed light on the hardware upgrade and replacement of Amazon EC2. Then large instance from the standard family is selected to conduct focus analysis. To better understand the various behaviors of the instances, a local cluster environment is set up, which consists of two Intel Xeon servers, using different scheduling algorithms. Through a series of benchmark measurements, we observed the following findings: (1) Amazon utilizes highly diversified hardware to provision different instances. It results in significant performance variation, which can reach up to 30%. (2) Two different scheduling mechanisms were observed, one is similar to Simple Earliest Deadline Fist (SEDF) scheduler, whilst the other one analogies Credit scheduler in Xen hypervisor. These two scheduling mechanisms also arouse variations in performance. (3) By applying a simple "trial-and-failure" instance selection strategy, the cost saving is surprisingly significant. Given certain distribution of fast-instances and slow-instances, the achievable cost saving can reach 30%, which is attractive to SMEs which use Amazon EC2 platform.
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