• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Improved Hybrid Symbiotic Organism Search Task-Scheduling Algorithm for Cloud Computing


Abstract

Task scheduling is one of the most challenging aspects of cloud computing nowadays, and it plays an important role in improving overall performance in, and services from, the cloud, such as response time, cost, makespan, and throughput. A recent cloud taskÔÇôscheduling algorithm based on the symbiotic organisms search (SOS) algorithm not only has fewer specific parameters, but also incurs time complexity. SOS is a newly developed metaheuristic optimization technique for solving numerical optimization problems. In this paper, the basic SOS algorithm is reduced, and chaotic local search (CLS) is integrated into the reduced SOS to improve the convergence rate. Simulated annealing (SA) is also added to help the SOS algorithm avoid being trapped in a local minimum. The performance of the proposed SA-CLS-SOS algorithm is evaluated by extensive simulation using the Matlab framework, and is compared with SOS, SA-SOS, and CLS-SOS algorithms. Simulation results show that the improved hybrid SOS performs better than SOS, SA-SOS, and CLS-SOS in terms of convergence speed and makespan.


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Cite this article

[IEEE Style]
Songil Choe, Bo Li, IlNam Ri, ChangSu Paek, JuSong Rim and SuBom Yun, "Improved Hybrid Symbiotic Organism Search Task-Scheduling Algorithm for Cloud Computing," KSII Transactions on Internet and Information Systems, vol. 12, no. 8, pp. 3516-3541, 2018. DOI: 10.3837/tiis.2018.08.001

[ACM Style]
Choe, S., Li, B., Ri, I., Paek, C., Rim, J., and Yun, S. 2018. Improved Hybrid Symbiotic Organism Search Task-Scheduling Algorithm for Cloud Computing. KSII Transactions on Internet and Information Systems, 12, 8, (2018), 3516-3541. DOI: 10.3837/tiis.2018.08.001