云计算中基于Shapley值改进遗传算法的虚拟机调度模型

doi:10.11959/j.issn.1000-0801.2022281

Abstract

Abstract:

Cloud computing system has the characteristics of large-scale servers and a wide range of users.However, it also consumes a huge number of energy, resulting in high operating costs of cloud providers and high carbon emissions issue.Cloud computing is highly virtualized.How to allocate and manage the virtual resources to ensure efficient physical resource utilization and energy consumption control is a multi-parameter game problem, and it is also a research hotspot in this field.A virtual machine scheduling model and the corresponding SV-GA were proposed, which could calculate the contribution value of the physical machine participating in the work through the Shapley value, and modify the probability parameter of the mutation step in the genetic algorithm through the contribution value, so as to complete the task of virtual machine scheduling.The experimental results show that during the comparison with Max-Min, LrMmt and DE, the SV-GA shows its excellent performance in the multi-parameter game including migration time, times, SLA violation rate and energy consumption in the virtual machine scheduling process.

Key words: cloud computing, multi-parameter game, virtual machine scheduling, Shapley value, genetic algorithm

CLC Number:

TP393

Kun MA, Lingyu XU, Xiaoping SHEN, Zhicheng GONG, Jianping LAN, Shuangxi CHEN, Jun QIAN. Virtual machine scheduling model based on Shapley value modified genetic algorithm in cloud computing[J]. Telecommunications Science, 2022, 38(12): 1-10.

Figures/Tables 13

对比项	Max-Min	LrMmt	DE	SV-GA
SLA违背率	20.54%	30.64%	3.70%	0
迁移时间	9.02%	$- 21 . 09 %$	2.10%	0
迁移能耗	26.41%	45.76%	3.22%	0
迁移次数	12.74%	$- 11 . 55 %$	3.56%	0

对比项	Max-Min	LrMmt	DE	SV-GA
SLA违背率	10.27%	20.55%	0.92%	0
迁移时间	22.74%	$- 19 . 64 %$	2.58%	0
迁移能耗	37.54%	51.42%	6.94%	0
迁移次数	19.31%	$- 19 . 24 %$	4.43%	0

References 26

[1]	LAILI Y J , TAO F , ZHANG L ,et al. A study of optimal allocation of computing resources in cloud manufacturing systems[J]. The International Journal of Advanced Manufacturing Technology, 2012,63(5-8): 671-690.
[2]	ZHANG Q , CHENG L , BOUTABA R . Cloud computing:state-of-the-art and research challenges[J]. Journal of Internet Services and Applications, 2010,1(1): 7-18.
[3]	吕廷勤, 魏萌 . 基于蚁群优化算法的绿色云计算虚拟机迁移策略[J]. 西南师范大学学报(自然科学版), 2020,45(12): 78-84.
	LYU T Q , WEI M . Migration strategy of green cloud computing virtual machine based on ant colony optimization algorithm[J]. Journal of Southwest China Normal University (Natural Science Edition), 2020,45(12): 78-84.
[4]	YADAV R , ZHANG W Z , CHEN H ,et al. MuMs:energy-aware VM selection scheme for cloud data center[C]// Proceedings of 2017 28th International Workshop on Database and Expert Systems Applications (DEXA). Piscataway:IEEE Press, 2017: 132-136.
[5]	GELENBE E , LENT R . Optimising server energy consumption and response time[J]. Theoretical and Applied Informatics, 2012,24(4).
[6]	YADAV R , ZHANG W Z . MeReg:managing energy-SLA tradeoff for green mobile cloud computing[J]. Wireless Communications and Mobile Computing,2017, 2017:6741972.
[7]	CHAUHAN S S , JOSHI R C . A weighted mean time Min-Min Max-Min selective scheduling strategy for independent tasks on grid[C]// Proceedings of 2010 IEEE 2nd International Advance Computing Conference. Piscataway:IEEE Press, 2010: 4-9.
[8]	刘永, 王新华, 王朕 ,等. 节能及信任驱动的虚拟机资源调度[J]. 计算机应用研究, 2012,29(7): 2479-2483.
	LIU Y , WANG X H , WANG Z ,et al. Energy-aware and trust-driven virtual machine scheduling[J]. Application Re-search of Computers, 2012,29(7): 2479-2483.
[9]	古英汉, 伊鹏 . 多虚拟机动态迁移情景下的服务功能链调整方法[J]. 小型微型计算机系统, 2017,38(5): 1022-1027.
	GU Y H , YI P . Method of service function chain adjustment based on multi-VM live migration[J]. Journal of Chinese Com-puter Systems, 2017,38(5): 1022-1027.
[10]	AJMERA K , TEWARI T K . VMS-MCSA:virtual machine scheduling using modified clonal selection algorithm[J]. Cluster Computing, 2021,24(4): 3531-3549.
[11]	GARG V , JINDAL B . Energy efficient virtual machine migration approach with SLA conservation in cloud computing[J]. Journal of Central South University, 2021,28(3): 760-770.
[12]	PERUMAL V , SUBBIAH S . Power-conservative server consolidation based resource management in cloud[J]. International Journal of Network Management, 2014,24(6): 415-432.
[13]	BELOGLAZOV A , BUYYA R . Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers[J]. Concurrency and Computation:Practice and Experience, 2012,24(13): 1397-1420.
[14]	JALALI MOGHADDAM M , ESMAEILZADEH A , GHAVIPOUR M ,et al. Minimizing virtual machine migration probability in cloud computing environments[J]. Cluster Computing, 2020,23(4): 3029-3038.
[15]	AJAYI O O , BAGULA A B , MA K . Fourth industrial revolution for development:the relevance of cloud federation in healthcare support[J]. IEEE Access, 2019(7): 185322-185337.
[16]	WINTER E . Chapter 53 the shapley value[M]// Handbook of game theory with economic applications. Amsterdam: Elsevier, 2002: 2025-2054.
[17]	MICHALAK T P , AADITHYA K V , SZCZEPANSKI P L ,et al. Efficient computation of the shapley value for game-theoretic network centrality[J]. Journal of Artificial Intelligence Research, 2013,46: 607-650.
[18]	MATHEW T V . Genetic algorithm[R]. 2012.
[19]	ZHAO X C , GAO X S , HU Z C . Evolutionary programming based on non-uniform mutation[J]. Applied Mathematics and Computation, 2007,192(1): 1-11.
[20]	CALHEIROS R N , RANJAN R , BELOGLAZOV A ,et al. CloudSim:a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms[J]. Software:Practice and Experience, 2011,41(1): 23-50.
[21]	HIEU N T , FRANCESCO M D , YL?-J??SKI A , . Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers[J]. IEEE Transactions on Services Computing, 2020,13(1): 186-199.
[22]	AJAYI O O , OLADEJI F A , UWADIA C O . Multi-class load balancing scheme for QoS and energy conservation in cloud computing[J]. West African Journal of Industrial and Academic Research, 2017,17(1): 28-36.
[23]	PARK K , PAI V S . CoMon:a mostly-scalable monitoring system for PlanetLab[J]. ACM SIGOPS Operating Systems Review, 2016,40(1): 65-74.
[24]	KIM M H , LEE J Y , RAZA SHAH S A ,et al. Min-Max exclusive virtual machine placement in cloud computing for scientific data environment[J]. Journal of Cloud Computing, 2021(10): 2.
[25]	NAGMA , SINGH J , SIDHU J . Comparative analysis of VM consolidation algorithms for cloud computing[J]. Procedia Computer Science, 2020(167): 1390-1399.
[26]	SUN J Y , ZHANG Q F , TSANG E P K . DE/EDA:a new evolutionary algorithm for global optimization[J]. Information Sciences, 2005,169(3-4): 249-262.

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