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An Effective Correlation-Aware VM Placement Scheme for SLA Violation Reduction in Data Centers

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9532))

Abstract

Correlation-aware virtual machine placement (CA-VMP) has been demonstrated as one of the most effective energy saving methodologies. Uncorrelated applications are placed together, which based on the off-peak values for application demand, can reduce the number of required servers significantly while meeting the requirements of service level agreement (SLA) at the same time. However, previous works using insufficient constraints for CA-VMP can’t provide a good guarantee of SLA performance. In this paper, a detailed analysis is first conducted on the reasons of performance degradation of SLA. To optimize SLA performance, we present a set of placement constraints on the placement capacity of server. We proposed an optimal placement algorithm SSP to achieve a tradeoff between SLA performance and energy cost. According to the simulation experiment results, compared with existing correlation based placement methods, our proposed algorithm provides up to more than five times SLA performance improvement with negligible cost increase.

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Notes

  1. 1.

    In this paper, the congestion probability of application demand is also called as the violation probability of SLA.

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Acknowledgments

This work was partially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDA06010401, by NSFC under Grant No. 61202056, No. 61331008 and No. 61221062, and by Huawei Research Programm YBCB2011030.

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Correspondence to Binzhang Fu .

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© 2015 Springer International Publishing Switzerland

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Xu, S., Fu, B., Chen, M., Zhang, L. (2015). An Effective Correlation-Aware VM Placement Scheme for SLA Violation Reduction in Data Centers. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9532. Springer, Cham. https://doi.org/10.1007/978-3-319-27161-3_56

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  • DOI: https://doi.org/10.1007/978-3-319-27161-3_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27160-6

  • Online ISBN: 978-3-319-27161-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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