A DISCRETE PARTICLE SWARM OPTIMIZATION APPROACH FOR GRID JOB SCHEDULING

被引:0
作者
Izakian, Hesam [1 ,2 ]
Ladani, Behrouz Tork [2 ]
Abraham, Ajith [1 ]
Snasel, Vaclav [3 ]
机构
[1] Machine Intelligence Res Labs MIR Labs, Auburn, WA 98071 USA
[2] Univ Isfahan, Dept Comp Engn, Esfahan, Iran
[3] VSB Tech Univ Ostrava, Fac Elect Engn & Comp Sci, Ostrava, Czech Republic
来源
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2010年 / 6卷 / 09期
关键词
Grid computing; Scheduling; Makespan; Flowtime; Particle swarm optimization; TASKS; HEURISTICS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Scheduling is one of the core steps to efficiently exploit the capabilities of emergent computational systems such as grid. Grid environment is a dynamic, heterogeneous and unpredictable one sharing different services among many different users. Because of heterogeneous and dynamic nature of grid, the methods used in traditional systems could not be applied to grid scheduling and therefore new methods should be looked for. This paper represents a discrete Particle Swarm Optimization (DPSO) approach for grid job scheduling. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or near-optimal solutions. In this paper, the scheduler aims at minimizing makespan and flowtime simultaneously in grid environment. Experimental studies illustrate that the proposed method is more efficient and surpasses those of reported meta-heuristic algorithms for this problem.
引用
收藏
页码:4219 / 4233
页数:15
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