Multi-objective Ant Colony Optimization Algorithm Based on Load Balance

被引：6

作者：

Zhu, Liwen ^{[1
]}

Tang, Ruichun ^{[1
]}

Tao, Ye ^{[2
]}

Ren, Meiling ^{[1
]}

Xue, Lulu ^{[1
]}

机构：

[1] Ocean Univ China, Coll Informat Sci & Engn, Qingdao 266100, Peoples R China

[2] Qingdao Univ Sci & Technol, Qingdao 266061, Peoples R China

来源：

CLOUD COMPUTING AND SECURITY, ICCCS 2016, PT I | 2016年 / 10039卷

关键词：

Cloud data center; VM placement; Multi-objective optimization; Ant colony algorithm; PAF;

D O I：

10.1007/978-3-319-48671-0_18

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Virtual machine (VM) placement is a process of mapping VMs to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony optimization algorithm based on load balance (MACOLB) for the VM placement problem. Firstly, the algorithm for a multi-objective context is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. Secondly, the pheromone adjustment factor (PAF) is given according to the load of physical machine (PM) and the pheromone update rule is transformed correspondingly. Finally, the effectiveness of the proposed algorithm is evaluated by the simulation.

引用

页码：193 / 205

页数：13

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