Abstract-Task scheduling is one of the most challenging aspects in cloud computing nowadays, which plays an important role to improve the overall performance and services of the cloud such as response time, cost, makespan, throughput etc. Recently, a cloud task scheduling algorithm based on the Symbiotic Organisms Search (SOS) not only have fewer specific parameters, but also take a little time complexity. Symbiotic Organism Search (SOS) is a newly developed metaheuristic optimization technique for solving numerical optimization problems. In this paper, the basic SOS algorithm is reduced and a chaotic local search(CLS) is integrated into the reduced SOS to improve the convergence rate of the basic SOS algorithm. Also, Simulated Annealing (SA) is combined in order to asist the SOS in avoiding being trapped into local minimum. The performance of the proposed SA-CLS-SOS algorithm is evaluated by extensive simulation using MATLAB simulation framework and compared with SOS, SA-SOS and CLS-SOS. Results of simulation showed that improved hybrid SOS performs better than SOS, SA-SOS and CLS-SOS in terms of convergence speed and makespan time.
Comments: 18 Pages.
[v1] 2018-03-26 09:19:32
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