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An Analytical Model for Estimating Cloud Resources of Elastic Services

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Abstract

In the cloud, ensuring proper elasticity for hosted applications and services is a challenging problem and far from being solved. To achieve proper elasticity, the minimal number of cloud resources that are needed to satisfy a particular service level objective (SLO) requirement has to be determined. In this paper, we present an analytical model based on Markov chains to predict the number of cloud instances or virtual machines (VMs) needed to satisfy a given SLO performance requirement such as response time, throughput, or request loss probability. For the estimation of these SLO performance metrics, our analytical model takes the offered workload, the number of VM instances as an input, and the capacity of each VM instance. The correctness of the model has been verified using discrete-event simulation. Our model has also been validated using experimental measurements conducted on the Amazon Web Services cloud platform.

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Acknowledgments

We would like to acknowledge the reviewers for their invaluable comments and feedback that tremendously enhanced the quality of our work. Moreover, the experimental work in this paper was supported by a generous research Grant provided by Amazon AWS in Education [56].

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, Khalifa University of Science, Technology and Research (KUSTAR), Abu Dhabi, UAE

    Khaled Salah

  2. School of Computing, DePaul University, Chicago, IL, USA

    Khalid Elbadawi

  3. David R. Cheriton School of CS, University of Waterloo, Waterloo, Canada

    Raouf Boutaba

  4. Division of IT Convergence Engineering, POSTECH, Pohang, Korea

    Raouf Boutaba

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  1. Khaled Salah
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  2. Khalid Elbadawi
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  3. Raouf Boutaba
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Correspondence to Khaled Salah.

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Salah, K., Elbadawi, K. & Boutaba, R. An Analytical Model for Estimating Cloud Resources of Elastic Services. J Netw Syst Manage 24, 285–308 (2016). https://doi.org/10.1007/s10922-015-9352-x

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