Journal of Guangdong University of Technology ›› 2014, Vol. 31 ›› Issue (3): 77-82.doi: 10.3969/j.issn.1007-7162.2014.03.014

• Comprehensive Studies • Previous Articles     Next Articles

Task Scheduling Algorithm Based on Simulated Annealing Ant Colony Algorithm in Cloud Computing Environment

Zhang Hao-rong1,Chen Ping-hua1,Xiong Jian-bin2,3   

  1. 1.School of Computers，Guangdong University of Technology, Guangzhou 510006, China；
    2. School of Electronic Information and Computer，Guangdong University of Petrochemical Technology,Maoming 525000, China;
    3. Guangdong Key Laboratory of Petrochemical Equipment Fault Diagnosis, Maoming 525000, China
  • Received:2013-11-06 Online:2014-09-30 Published:2014-09-30

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Abstract: It studies the task scheduling in cloud computing, and proposes a hybrid scheduling algorithm(ACOSA) combined with ant colony algorithm and simulated annealing algorithm for the MapReduce programming framework of cloud computing. This algorithm aims at minimizing the scheduling time and introduces the task and resource matching factors and load balance. Firstly, the ant colony algorithm was used to get the optimal solution to a set of tasks and resources. Then, the path was optimized, and the pheromone of solution was updated by  the simulated annealing algorithm. Lastly, they were recompiled by extending Cloudsim cloud computing simulation platform, and the ACOSA algorithm was achieved. The experimental results show that the algorithm has a good performance in scheduling time and load balancing.

Key words: cloud computing, simulated annealing, ant colony algorithm

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