改進粒子群算法在云計算負載均衡中的應用研究
發(fā)布時間:2018-08-17 10:34
【摘要】:云計算系統(tǒng)采用虛擬化技術可以更加靈活和高效地分配運算資源,便于管理員根據(jù)用戶任務需求按需分配云計算資源;但虛擬化后的云計算中心存在種類多樣、數(shù)量龐大的虛擬機資源,難以將虛擬機合理地放置到物理主機集群上并達到較好的負載均衡;為此,給出了云計算中心虛擬機放置到物理主機的負載均衡模型,采用改進后的粒子群算法(PSO)來求解最優(yōu)解;最后通過和常用虛擬機放置算法的仿真對比實驗,驗證了所提云計算負載均衡優(yōu)化算法的有效性。
[Abstract]:Using virtualization technology, cloud computing systems can allocate computing resources more flexibly and efficiently, which is convenient for administrators to allocate cloud computing resources according to the needs of users, but there are various types of cloud computing centers after virtualization. It is difficult to put the virtual machine on the physical host cluster reasonably and achieve better load balance because of the huge amount of virtual machine resources. Therefore, a load balancing model of virtual machine in cloud computing center is given. The improved particle swarm optimization (PSO) algorithm is used to solve the optimal solution. Finally, the effectiveness of the proposed algorithm is verified by comparing the simulation results with the common virtual machine placement algorithms.
【作者單位】: 四川大學電子信息學院;
【基金】:國家自然科學基金項目(61172181)
【分類號】:TP18;TP302
本文編號:2187333
[Abstract]:Using virtualization technology, cloud computing systems can allocate computing resources more flexibly and efficiently, which is convenient for administrators to allocate cloud computing resources according to the needs of users, but there are various types of cloud computing centers after virtualization. It is difficult to put the virtual machine on the physical host cluster reasonably and achieve better load balance because of the huge amount of virtual machine resources. Therefore, a load balancing model of virtual machine in cloud computing center is given. The improved particle swarm optimization (PSO) algorithm is used to solve the optimal solution. Finally, the effectiveness of the proposed algorithm is verified by comparing the simulation results with the common virtual machine placement algorithms.
【作者單位】: 四川大學電子信息學院;
【基金】:國家自然科學基金項目(61172181)
【分類號】:TP18;TP302
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