An adaptive overload threshold selection process using Markov decision processes of virtual machine in cloud data center

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Abstract

In Cloud Computing (CC), the cost for computation and energy is less by current cloud data centers because it exploits virtualization for an effective resource management. The Virtual Machine (VM) migration authorizes virtualization because it mitigates the difficulties of dynamic workload by repositioning VMs within cloud data centers. Through VM migration many goals of resource management are attained like load balancing, power management, fault tolerance, and system maintenance. The overload threshold is one of the key criterions to determine whether a host is overloaded or not. Achieving desired balance in guaranteeing quality of service, improving resource utilization and degrading energy consumption in data centers is the expected results of any overload threshold selection strategies. But, it is difficult due to the stochastic resource demands of VMs. In this paper, to address this problem, the overload threshold selection is modelled as a Markov decision process. With the solution of the improved Bellman optimality equation by the value iteration method, the optimization model is resolved, and the optimum overload threshold is adaptively selected. The hybrid processes are summarized as the Markov decision processes based adaptive overload threshold selection algorithm. Validations and comparisons are performed to illustrate its effectiveness and efficiency.

Keywords

Host overload detection Markov decision processes Overload threshold Optimization model Value iteration method 

Notes

Acknowledgements

This work is supported by the Future Research Projects Funds for the Science and Technology Department of Jiangsu Province (Grant No. BY2013015-23) and the Fundamental Research Funds for the Ministry of Education (Grant No. JUSRP211A 41).

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Technology, School of Internet of Things EngineeringJiangnan UniversityWuxiChina

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