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Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment

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  • Mohammed Abdullahi
  • Md Asri Ngadi

Abstract

Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

Suggested Citation

  • Mohammed Abdullahi & Md Asri Ngadi, 2016. "Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-29, June.
  • Handle: RePEc:plo:pone00:0158229
    DOI: 10.1371/journal.pone.0158229
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    Citations

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    Cited by:

    1. Hui Zhai & Jia Wang, 2021. "Automatic deployment system of computer program application based on cloud computing," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 731-740, August.
    2. Mohit Agarwal & Gur Mauj Saran Srivastava, 2018. "Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1237-1267, July.
    3. Jianguo Zheng & Yilin Wang, 2021. "A Hybrid Multi-Objective Bat Algorithm for Solving Cloud Computing Resource Scheduling Problems," Sustainability, MDPI, vol. 13(14), pages 1-25, July.
    4. Muhammad Sulaiman & Ashfaq Ahmad & Asfandyar Khan & Shakoor Muhammad, 2018. "Hybridized Symbiotic Organism Search Algorithm for the Optimal Operation of Directional Overcurrent Relays," Complexity, Hindawi, vol. 2018, pages 1-11, January.
    5. Yan Zeng & Wei Wang & Yong Ding & Jilin Zhang & Yongjian Ren & Guangzheng Yi, 2022. "Adaptive Distributed Parallel Training Method for a Deep Learning Model Based on Dynamic Critical Paths of DAG," Mathematics, MDPI, vol. 10(24), pages 1-21, December.
    6. Muhammad Shuaib Qureshi & Muhammad Bilal Qureshi & Muhammad Fayaz & Wali Khan Mashwani & Samir Brahim Belhaouari & Saima Hassan & Asadullah Shah, 2020. "A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems," International Journal of Distributed Sensor Networks, , vol. 16(8), pages 15501477209, August.
    7. Syed Hamid Hussain Madni & Muhammad Shafie Abd Latiff & Mohammed Abdullahi & Shafi’i Muhammad Abdulhamid & Mohammed Joda Usman, 2017. "Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-26, May.

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