Journal of the Operations Research Society of China >

2020 , Vol. 8 >Issue 4: 561 - 580

DOI: https://doi.org/10.1007/S40305-019-00272-x

Performance Evaluation and Social Optimization of an Energy-Saving Virtual Machine Allocation Scheme Within a Cloud Environment

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  • 1 School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, Shandong, China;
    2 Department of Intelligence and Informatics, Konan University, Kobe 658-8501, Japan;
    3 Graduate School of Informatics, Kyoto University, Kyoto 606-8225, Japan

Received date: 2018-11-27

  Revised date: 2019-09-08

  Online published: 2020-12-29

Supported by

This work was supported in part by the National Natural Science Foundation of China (Nos. 61872311, 61973261, 61472342) and Hebei Provincial Natural Science Foundation (No. F2017203141), China, and was supported in part by MEXT and JSPS KAKENHI (Nos. JP17H01825 and JP26280113), Japan.

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Abstract

Achieving greener cloud computing is non-negligible for the open-source cloud platform. In this paper, we propose a novel virtual machine allocation scheme with a sleep-delay and establish a corresponding mathematical model. Taking into account the number of tasks and the state of the physical machine, we construct a two-dimensional Markov chain and derive the average latency of tasks and the energy-saving degree of the system in the steady state. Moreover, we provide numerical experiments to show the effectiveness of the proposed scheme. Furthermore, we study the Nash equilibrium behavior and the socially optimal behavior of tasks and carry out an improved adaptive genetic algorithm to obtain the socially optimal arrival rate of tasks. Finally, we present a pricing policy for tasks to maximize the social profit when managing the network resource within the cloud environment.

Key words： Cloud computing; Resource allocation scheme; Mathematical analysis; Markov chain; Socially optimization; Genetic algorithm

Cite this article

Xiushuang Wang, Jing Zhu, Shunfu Jin, Wuyi Yue, Yutaka Takahashi . Performance Evaluation and Social Optimization of an Energy-Saving Virtual Machine Allocation Scheme Within a Cloud Environment[J]. Journal of the Operations Research Society of China, 2020 , 8(4) : 561 -580 . DOI: 10.1007/S40305-019-00272-x

References

[1] Hanini, M., Kafhali, S.:Cloud computing performance evaluation under dynamic resource utilization and traffic control. In:International Conference on Big Data, Cloud and Applications, Boston (2017)
[2] Sugumaran, R., Armstrong, M.:Cloud Computing. Wiley, Hoboken (2017)
[3] Andrews, J., Buzzi, S., Choi, W., Hanly, S., Lozano, A., Soong, A., Zhang, J.:What will 5G be?. IEEE J. Sel. Areas Commun. 32, 1065-1082(2014)
[4] Krein, P.:Data center challenges and their power electronics. CPSS Trans. Power Electron. Appl. 2, 39-46(2017)
[5] Fard, S., Ahmadi, M., Adabi, S.:Erratum to:a dynamic VM consolidation technique for QoS and energy consumption in cloud environment. J. Supercomput. 73, 1-4(2017)
[6] Khoshkholghi, M., Derahman, M., Abdullah, A., Subramaniam, S., Othman, M.:Energy-efficient algorithms for dynamic virtual machine consolidation in cloud data centers. IEEE Access 5, 10709-10722(2017)
[7] Cheng, C., Li, J., Wang, Y.:An energy-saving task scheduling strategy based on vacation queueing theory in cloud computing. Tsinghua Sci. Technol. 20, 28-39(2015)
[8] Guo, X., Niu, Z., Zhou, S., Kumar, P.:Delay-constrained energy-optimal base station sleeping control. IEEE J. Sel. Areas Commun. 34, 1073-1085(2016)
[9] Yang, J., Zhang, X., Wang, W.:Two-stage base station sleeping scheme for green cellular networks. J. Commun. Netw. 18, 600-609(2016)
[10] Zhao, Y., Jin, S., Yue, W.:Performance evaluation of the centralized spectrum access strategy with multiple input streams in cognitive radio networks. IEICE Trans. Commun. E97-B, 334-342(2014)
[11] Choi, S., Kim, B., Sohraby, K., Choi, B.:On matrix-geometric solution of nested QBD chains. Queueing Syst. 43, 5-28(2003). https://doi.org/10.1023/A:1021884213344
[12] Kim, S., Kim, M., Kang, C.:Performance of a burst switching scheme for CDMA-based wireless packet data systems. IEICE Trans. Commun. E86-B, 1082-1093(2003)
[13] He,H., Yuan,D.,Hou, Y., Xu,J.:Preconditioned Gauss-Seidel iterative method for linear systems. Int. Forum Inf. Technol. Appl. 1, 382-385(2009)
[14] Ma, Z., Wang, P., Yue, W.:Performance analysis and optimization of a pseudo-fault Geo/Geo/1 repairable queueing system with N-policy, setup time and multiple working vacations. J. Ind. Manag. Optim. 13, 1467-1481(2017)
[15] Bhagat, A., Jain, M.:N-policy for Mx/G/l unreliable retrial G-queue with preemptive resume and multi-services. J. Oper. Res. Soc. China 4, 1-23(2016)
[16] Jin, S., Ma, X., Yue, W.:Energy-saving strategy for green cognitive radio networks with an LTE-advanced structure. J. Commun. Netw. 18, 610-618(2016)
[17] Hassin, R., Haviv, M.:To Queue or not to Queue:Equilibrium Behaviour in Queueing Systems. Springer, Boston (2003)
[18] Jing, H., Aida, H.:A graphical game theoretic approach to optimization of energy efficiency in multihop wireless sensor networks. IEICE Trans. Commun. E99-B, 1789-1798(2016)
[19] Yu, H., Zeng, W., Wu, D.:A Stochastic level-value estimation method for global optimization. J. Oper. Res. Soc. China 1, 1-16(2017)
[20] Portaluri, G., Giordano, S.:Power efficient resource allocation in cloud computing data centers using multi-objective genetic algorithms and simulated annealing. In:IEEE International Conference on Cloud Networking, pp. 319-321(2015)
[21] Salido, M., Escamilla, J., Giret, A., Barber, F.:A genetic algorithm for energy-efficiency in job-shop scheduling. Int. J. Adv. Manuf. Technol. 85, 1-12(2016). https://doi.org/10.1007/s00170-015-7987-0
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