改进型云计算VM资源调度方法  

Improved VM Resource Scheduling Method for Cloud Computing

作　　者：刘丰年 LIU Feng-nian(College of Applied Engineering,Henan University of Science and Technology,Sanmenxia 472000,China;Sanmenxia Polytechnic,Sanmenxia 472000,China)

机构地区：[1]河南科技大学应用工程学院,三门峡472000 [2]三门峡职业技术学院,三门峡472000

出　　处：《北京服装学院学报（自然科学版）》2021年第2期64-70,共7页Journal of Beijing Institute of Fashion Technology：Natural Science Edition

基　　金：河南省高等学校重点科研项目(17A413010);河南省科技攻关项目(182102210479);河南省教育科学“十三五”规划项目(2020YB0632);河南省高等职业学校青年骨干教师培养计划项目“云计算环境智能化运维关键技术研究”(2020GGJS041)。

摘　　要：针对云计算框架中VM资源调度问题,提出一种改进型云计算VM资源调度方法。首先,将直觉模糊机制和Canopy算法融入传统的模糊c均值聚类算法,设计一种改进型直觉模糊c均值聚类算法,用以完成对用户任务请求进行聚类;其次,设计一种改进型粒子群算法,对云计算中的VM资源进行分配。通过在Cloudsim平台上的仿真实验结果表明,本文方法无论在资源调度效率,还是收敛性效果方面均优于经典调度方法,预期可以作为云计算框架中一种有效的资源调度方案。Aiming at the VM resource scheduling problem in cloud computing framework, an improved VM resource scheduling method for cloud computing was proposed. Firstly, the intuitionistic fuzzy mechanism and Canopy algorithm were integrated into the traditional fuzzy c-means clustering algorithm, and a new intuitionistic fuzzy c-means clustering algorithm was designed to cluster the user’s task requests. Secondly, an improved particle swarm optimization algorithm was designed to allocate VM resources in cloud computing. The simulation results on Cloudsim platform show that the proposed method is superior to the classical scheduling methods in both resource scheduling efficiency and convergence effect, and it is expected to be an effective resource scheduling scheme in cloud computing framework.

关 键 词：资源调度 直觉模糊c均值聚类 粒子群优化 任务聚类 适应度 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]