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A Multi-Objective Genetic Algorithm-Based Resource Scheduling in Mobile Cloud Computing

Author

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  • Somula Ramasubbareddy

    (Department of Information Technology, VNRVJIET, Hyderabad, India)

  • Evakattu Swetha

    (SV College of Engineering, Tirupati, India)

  • Ashish Kumar Luhach

    (The PNG University of Technology, Papua New Guinea)

  • T. Aditya Sai Srinivas

    (G. Pullaiah College of Engineering and Technology, Kurnool, India)

Abstract

Mobile cloud computing is an emerging technology in recent years. This technology reduces battery consumption and execution time by executing mobile applications in remote cloud server. The virtual machine (VM) load balancing among cloudlets in MCC improves the performance of application in terms of response time. Genetic algorithm (GA) is popular for providing optimal solution for load balancing problems. GA can perform well in both homogeneous and heterogeneous environments. In this paper, the authors consider multi-objective genetic algorithm for load balancing in MCC (MOGALMCC) environment. In MOGALMCC, they consider distance, bandwidth, memory, and cloudlet server load to find optimal cloudlet before scheduling VM in another cloudlet. The framework MOGALMCC aims to improve response time as well as minimizes VM failure rate. The experiment result shows that proposed model performed well by reducing execution time and task waiting time at server.

Suggested Citation

  • Somula Ramasubbareddy & Evakattu Swetha & Ashish Kumar Luhach & T. Aditya Sai Srinivas, 2021. "A Multi-Objective Genetic Algorithm-Based Resource Scheduling in Mobile Cloud Computing," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(3), pages 58-73, July.
    • Handle: RePEc:igg:jcini0:v:15:y:2021:i:3:p:58-73
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    1. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
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    1. Honghuan Chen & Keming Wang, 2023. "Fusing DCN and BBAV for Remote Sensing Image Object Detection," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 17(1), pages 1-16, January.

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