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

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

Somula Ramasubbareddy, Evakattu Swetha, Ashish Kumar Luhach, T. Aditya Sai Srinivas
Copyright: © 2021 |Volume: 15 |Issue: 3 |Pages: 16
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859840|DOI: 10.4018/IJCINI.20210701.oa5
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MLA

Ramasubbareddy, Somula, et al. "A Multi-Objective Genetic Algorithm-Based Resource Scheduling in Mobile Cloud Computing." IJCINI vol.15, no.3 2021: pp.58-73. http://doi.org/10.4018/IJCINI.20210701.oa5

APA

Ramasubbareddy, S., Swetha, E., Luhach, A. K., & Srinivas, T. A. (2021). A Multi-Objective Genetic Algorithm-Based Resource Scheduling in Mobile Cloud Computing. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(3), 58-73. http://doi.org/10.4018/IJCINI.20210701.oa5

Chicago

Ramasubbareddy, Somula, et al. "A Multi-Objective Genetic Algorithm-Based Resource Scheduling in Mobile Cloud Computing," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.3: 58-73. http://doi.org/10.4018/IJCINI.20210701.oa5

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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.