KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Improved Hybrid Symbiotic Organism Search Task-Scheduling Algorithm for Cloud Computing

  • Choe, SongIl (College of Information Science, Kim Il Sung University) ;
  • Li, Bo (College of Management and Economics, Tianjin University) ;
  • Ri, IlNam (College of Information Science, Kim Il Sung University) ;
  • Paek, ChangSu (Department of Information Science, HuiChon Industry University) ;
  • Rim, JuSong (Department of Control Science, University of Science) ;
  • Yun, SuBom (Department of Information Science, HuiChon Industry University)
  • Received : 2018.04.08
  • Accepted : 2018.07.04
  • Published : 2018.08.31
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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 task-scheduling 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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References

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