一种异构云环境下的负载均衡算法
|
摘要
针对动态负载均衡算法在异构云环境中的任务迁移次数过多的问题,提出了一种最小化任务迁移次数的动态负载均衡(MMLB)算法。MMLB算法通过自适应阈值对虚拟机进行分组、任务选择算法最小化任务迁移的次数、任务调度算法优化任务分配实现了任务的再分配。将MMLB与WRR、HBBLB、LBF算法进行实验对比分析,MMLB算法在makespan、平均任务响应时间、负载不均衡度等评价指标上表现更优,并且有效降低了任务迁移的次数。实验结果验证了MMLB算法的可行性和有效性。
For the problem of excessive task migration times in the dynamic load balancing algorithm,this paper presented a dynamic load balancing( MMLB) algorithm to minimize task scheduling times. MMLB algorithm throughed the adaptive threshold to group the virtual machine,task selection algorithm to minimize migrated tasks' times,task scheduling algorithm to optimize the task allocation process to achieve the task of redistribution. The MMLB algorithm compared with WRR,HBBLB and LBF algorithm. MMLB algorithm's makespan,average task response time and degree of imbalance are better than other algorithm,even effectively reducing the number of tasks migration. The experimental results verify the feasibility and effectiveness of the MMLB algorithm.
For the problem of excessive task migration times in the dynamic load balancing algorithm,this paper presented a dynamic load balancing( MMLB) algorithm to minimize task scheduling times. MMLB algorithm throughed the adaptive threshold to group the virtual machine,task selection algorithm to minimize migrated tasks' times,task scheduling algorithm to optimize the task allocation process to achieve the task of redistribution. The MMLB algorithm compared with WRR,HBBLB and LBF algorithm. MMLB algorithm's makespan,average task response time and degree of imbalance are better than other algorithm,even effectively reducing the number of tasks migration. The experimental results verify the feasibility and effectiveness of the MMLB algorithm.
引文
[1] Chang B R,Tsai H F,Chen C M. Evaluation of virtual machine performance and virtualized consolidation ratio in cloud computing system[J]. Journal of Information Hiding and Multimedia Signal Processing,2013,4(3):192-200.
[2] Yagoubi B,Medebber M. A load balancing model for grid environment:computer and information sciences[C]//Proc of the 22nd International Symposium on Computer and Information Sciences. Piscataway,NJ:IEEE Press,2007:1-7.
[3]黄伟建,郭芳.基于烟花算法的云计算多目标任务调度[J].计算机应用研究,2017,34(6):1718-1720,1731.
[4]宁彬,谷琼,吴钊,等.云计算环境下的混沌萤火虫的资源负载均衡算法[J].计算机应用研究,2014,31(11):3397-3400.
[5] Agarwal M,Srivastava G M S. A genetic algorithm inspired task scheduling in cloud computing[C]//Proc of International Conference on Computing,Communication and Automation. Piscataway,NJ:IEEE Press,2016:364-367.
[6] Zomaya A Y,Teh Y H. Observations on using genetic algorithms for dynamic load-balancing[J]. IEEE Trans on Parallel&Distributed Systems,2001,12(9):899-911.
[7] Dhinesh B L D,Krishna P V. Honey bee behavior inspired load balancing of tasks in cloud computing environments[J]. Applied Soft Computing,2013,13(5):2292-2303.
[8] Ramezani F,Lu Jie,Hussain F K. Task-based system load balancing in cloud computing using particle swarm optimization[J]. International Journal of Parallel Programming,2014,42(5):739-754.
[9] Reddy G N,Kumar S P. Review of load balancing techniques in cloud computing environment:challenges and algorithms[J]. International Journal of Advanced Research in Computer Science,2014,5(4):157-162.
[10]Weiss A. Computing in the clouds[J]. Networker,2007,11(4):16-25.
[11]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&Experience,2011,41(1):23-50.
[12]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&Computation Practice&Experience,2012,24(13):1397-1420.
[2] Yagoubi B,Medebber M. A load balancing model for grid environment:computer and information sciences[C]//Proc of the 22nd International Symposium on Computer and Information Sciences. Piscataway,NJ:IEEE Press,2007:1-7.
[3]黄伟建,郭芳.基于烟花算法的云计算多目标任务调度[J].计算机应用研究,2017,34(6):1718-1720,1731.
[4]宁彬,谷琼,吴钊,等.云计算环境下的混沌萤火虫的资源负载均衡算法[J].计算机应用研究,2014,31(11):3397-3400.
[5] Agarwal M,Srivastava G M S. A genetic algorithm inspired task scheduling in cloud computing[C]//Proc of International Conference on Computing,Communication and Automation. Piscataway,NJ:IEEE Press,2016:364-367.
[6] Zomaya A Y,Teh Y H. Observations on using genetic algorithms for dynamic load-balancing[J]. IEEE Trans on Parallel&Distributed Systems,2001,12(9):899-911.
[7] Dhinesh B L D,Krishna P V. Honey bee behavior inspired load balancing of tasks in cloud computing environments[J]. Applied Soft Computing,2013,13(5):2292-2303.
[8] Ramezani F,Lu Jie,Hussain F K. Task-based system load balancing in cloud computing using particle swarm optimization[J]. International Journal of Parallel Programming,2014,42(5):739-754.
[9] Reddy G N,Kumar S P. Review of load balancing techniques in cloud computing environment:challenges and algorithms[J]. International Journal of Advanced Research in Computer Science,2014,5(4):157-162.
[10]Weiss A. Computing in the clouds[J]. Networker,2007,11(4):16-25.
[11]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&Experience,2011,41(1):23-50.
[12]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&Computation Practice&Experience,2012,24(13):1397-1420.