Research for the Task Scheduling Algorithm Optimization based on Hybrid PSO and ACO for Cloud Computing

被引：4

作者：

Ju, JieHui ^{[1
,2
]}

Bao, WeiZheng ^{[3
]}

Wang, ZhongYou ^{[4
]}

Wang, Ya ^{[5
]}

Li, WenJuan ^{[6
]}

机构：

[1] Zhejiang Univ Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China

[2] Univ Colorado, Boulder, CO 80309 USA

[3] Jinhua Planning Bur, Surveying & Mapping Management Off, Jinhua 321000, Zhejiang, Peoples R China

[4] Zhejiang Commun Ind Serv Co Ltd, Technol R&D Ctr, Hangzhou 310050, Zhejiang, Peoples R China

[5] Second Surveying & Mapping Inst Zhejiang Prov, Surveying & Mapping, Hangzhou 310012, Zhejiang, Peoples R China

[6] Hangzhou Normal Univ, Key Lab Business, Hangzhou 310036, Zhejiang, Peoples R China

来源：

INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING | 2014年 / 7卷 / 05期

关键词：

Cloud Computing; Task Scheduling; Particle Swarm Optimization (PSO); Ant Colony Optimization (ACO);

D O I：

10.14257/ijgdc.2014.7.5.08

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

In cloud computing environment, there are a large number of users which lead to huge amount of tasks to be processed by system. In order to make the system complete the service requests efficiently, how to schedule the tasks becomes the focus of cloud computing Research. A task scheduling algorithm based on PSO and ACO for cloud computing is presented in this paper. First, the algorithm uses particle swarm optimization algorithm to get the initial solution quickly, and then according to this scheduling result the initial pheromone distribution of ant colony algorithm is generated. Finally, the ant colony algorithm is used to get the optimal solution of task scheduling. The experiment simulated on CloudSim platform shows that the algorithm has good effect in real-time performance and optimization capability. It is an effective task scheduling algorithm.

引用

页码：87 / 96

页数：10

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