Empirical Performance Assessment of Public Clouds Using System Level Benchmarks

Empirical Performance Assessment of Public Clouds Using System Level Benchmarks

Sanjay P. Ahuja (School of Computing, University of North Florida, Jacksonville, FL, USA), Thomas F. Furman (University of North Florida, Jacksonville, FL, USA), Kerwin E. Roslie (University of North Florida, Jacksonville, FL, USA) and Jared T. Wheeler (University of North Florida, Jacksonville, FL, USA)
Copyright: © 2013 |Pages: 11
DOI: 10.4018/ijcac.2013100106
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Abstract

Amazon's Elastic Compute Cloud (EC2) Service is one of the leading public cloud service providers and offers many different levels of service. This paper looks into evaluating the memory, central processing unit (CPU), and input/output I/O performance of two different tiers of hardware offered through Amazon's EC2. Using three distinct types of system benchmarks, the performance of the micro spot instance and the M1 small instance are measured and compared. In order to examine the performance and scalability of the hardware, the virtual machines are set up in a cluster formation ranging from two to eight nodes. The results show that the scalability of the cloud is achieved by increasing resources when applicable. This paper also looks at the economic model and other cloud services offered by Amazon's EC2, Microsoft's Azure, and Google's App Engine.
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With the increasing commercialization and use of the cloud, more research goes towards the benefits of the cloud, such as the operational and financial advantages of the cloud (Ahuja & Rolli, 2011). It has become a greater priority to accurately depict the performance of cloud services. To this end, benchmarking methodologies and benchmark comparisons across clouds has become a more common topic of concern. Another experiment was conducted between Amazon’s EC2 platform and Microsoft’s Windows Azure platform that analyzed the differences between the IaaS and PaaS environments with similar methodologies (Ahuja & Mani, 2013).

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