基于马尔可夫过程模型的云计算负载均衡策略  

Load Balancing Strategies in Cloud Computing Based on Markov Process Model

作　　者：陈海彬[1] CHEN Hai-bin(School of Information Technology,Anhui Vocational College of Defense Technology,Lu’an 237008,China)

机构地区：[1]安徽国防科技职业学院信息技术学院,安徽六安237008

出　　处：《辽东学院学报(自然科学版)》2024年第3期194-201,共8页Journal of Liaodong University:Natural Science Edition

基　　金：安徽省高等学校自然科学研究计划项目(2022AH052514、2024AH050193);安徽省质量工程项目(2022jyxm236)。

摘　　要：为有效实现云计算负载平衡策略,提高云计算系统性能,提出一种基于马尔可夫过程模型的负载均衡策略。根据平衡状态概率,推算虚拟机预期利用率,以每个虚拟机增加的工作量为基准,公平均衡地为虚拟机分配工作任务。实验结果表明,所提策略在时间跨度、平均响应时间和资源利用率都优于粒子群优化算法策略和蜜蜂觅食算法策略,并且负载失衡程度更低,能够有效提高云计算系统性能。In order to effectively implement load balancing strategies in cloud computing to improve system performance,a load balancing strategy based on Markov process models is proposed.The expected utilisation efficiency of virtual machines is derived based on the probability of equilibrium state,and tasks are allocated among VMs in a fair and balanced manner based on the amount of work added to each VM.The experimental results show that the proposed strategy outperforms particle swarm optimisation algorithm strategy and bee foraging algorithm strategy in terms of time span,average response time,and resource utilisation efficiency.The proposed strategy has lower load imbalance degree and can efficiently improve the performance of the cloud computing system.

关 键 词：负载平衡 云计算 马尔可夫模型 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]