Review of Task Scheduling Algorithm Research in Cloud Computing

Author:

Huang Mei Geng1,Ou Zhi Qi1

Affiliation:

1. Chongqing University of Posts and Telecommunications

Abstract

The cloud computing task scheduling field representative algorithms was introduced and analyzed : genetic algorithm, particle swarm optimization, ant colony algorithm. Parallelism and global search solution space is the characteristic of genetic algorithm, genetic iterations difficult to proceed when genetic individuals are very similar; Particle swarm optimization in the initial stage is fast, slow convergence speed in the later stage ; Ant colony algorithm optimization ability is good, slow convergence speed in its first stage; Finally, the summary and prospect the future research direction.

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Reference8 articles.

1. QiuXi Zhong, Tao Xie, HouWang Chen. Task Allocation & Scheduling by Computational Model of Coevolution. Chinese J Computers. 24(3). 2001: 308-314.

2. Michael Rinehart, Vida Kianzad, and Shuvra S. Bhattacharyya. A Modular Genetic Algorithm for Scheduling Task Graphs[C]. Technical Report UMIACS-TR-2003-66, (2003).

3. Kenney J. Eberhart R. Particle Swarm Optimization [c]/Proc. of IEEE International Conf. on Neural Networks. Perth , USA: [ s . n. ] , (1995).

4. Sahoo R K, Sivasubramaniam A, Squillante M S, et al, Failure Data Analysis of a Large-scale Heterogeneous Server Environment[C]/Proc. Of DSN' 04. Florence, Italy: [s. n. ], (2004).

5. Http: /http: /baike. baidu. com/link?url=Aq6oqHHC665rnPcrYyy17Re5EM_UED9kLoF2lXa MRAPsIzEKvyMr_hVX76Q0X4zV2PYNYWWVApsm1PryrUt8Dq.

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