| 摘要: |
| 针对Xen虚拟化平台中虚拟机资源分配不合理的问题,提出了两种资源调度优化算法,即细粒度优化算法和粗粒度优化算法.细粒度优化算法主要解决单个物理节点上虚拟机资源分配不合理问题,能够根据物理节点上运行的各虚拟机的资源利用情况来调整资源分配量,适当增加利用率较高的虚拟机的资源,减少资源利用率低的虚拟机的资源,从而优化资源分配,提高资源利用效率,避免不必要的虚拟机迁移.粗粒度优化算法是针对集群中多个物理节点之间虚拟机负载不均衡问题而提出的.该算法结合粒子群优化技术,选择将集群系统中热点物理机上的部分虚拟机迁移到最适合的冷点物理机上,从而避免高载物理机宕机.实验结果表明,这两种资源调度优化算法能够有效解决虚拟机资源分配不合理的问题,具有较好的适用性和应用前景. |
| 关键词: Xen 虚拟化 粒子群 资源调度优化算法 |
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| 基金项目:国家自然科学基金(61073063,61173029,61173030,61100028,61272182);国家海洋公益性项目(201105033);数字海洋国家重点实验室开放基金(KLDO201306) |
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| Resource Scheduling Optimization Algorithm for Xen Virtual Machines |
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QIAO Bai-You1,2, CAI Ren-Han2, CHEN Dong-Hai1, WANG Hong1, CHEN Yang1, WANG Guo-Ren1,2
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1.College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;2.National Marine Data and Information Service, Tianjin 300171, China
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| Abstract: |
| Arming at the irrational resource allocation problem in the Xen virtualization platform, this paper proposes two resource scheduling optimization algorithms: the fine-grained algorithm and the coarse-grained algorithm. The fine grained algorithm is mainly for resource allocation of single physical node, which dynamically adjusts the allocated resource amount of each virtual machine according to its resource utilization, and appropriately increases the resource amount for the virtual machines whose resource utilization are high and reduces the resource amount for those whose resource utilization is low, thus improves resource utilization efficiency and avoids unnecessary virtual machine migrations. The coarse-grained algorithm focuses on the load imbalance problem among multiple physical nodes in a cluster, and applies the particle swarm optimization technique to select some virtual machines on the hot physical machines and then immigrates it to the most suitable cold physical machines in a cluster system, thereby solving load imbalance problem of the cluster system and avoiding high load physical machines downtime. Experiments show that the proposed two scheduling optimization algorithms can effectively solve the resource allocation irrational problem of virtual machines and have better adaptability and application prospects. |
| Key words: Xen virtualization particle swarm optimization resource scheduling optimization algorithm |
