Effective Task Scheduling in Cloud Computing Based on Improved Social Learning Optimization Algorithm

Zhizhong Liu, Jingxuan Qin, Weiping Peng, Hao Chao


For the typical optimal problem of task scheduling in cloud computing, this paper proposes a novel resource scheduling algorithm based on Social Learning Optimization Algorithm (SLO). SLO is a new swarm intelligence algorithm which is proposed by simulating the evolution process of human intelligence and has better optimization mechanism and optimization performance. This paper proposes two learning operators for task scheduling in cloud computing after analyzing the characteristics of the problem of task scheduling; then, by introducing the Small Position Value (SPV) method, the two learning operators with continuous nature essence are enabled to solve the problem of task scheduling, and then the improved SLO is employed to solve the problem of cloud resource optimal scheduling. Finally, the performance of improved SLO is compared with existing research work on the CloudSim platform. Experimental results show that the approach proposed in this paper has better global optimization ability and convergence speed.


Cloud Computing; Task Scheduling; Social Learning Optimization Algorithm; Service of Quality

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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