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

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...

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Bibliographic Details
Published in:International journal of cognitive informatics & natural intelligence Vol. 15; no. 3; pp. 58 - 73
Main Authors: Luhach, Ashish Kumar, Ramasubbareddy, Somula, Swetha, Evakattu, Srinivas, T Aditya Sai
Format: Journal Article
Language:English
Published: Hershey IGI Global 01.07.2021
Subjects:
Algorithms
Analysis
Applications programs
Bandwidths
Cloud computing
Energy consumption
Failure rates
Genetic algorithms
Informatics
Load
Load balancing
Memory
Mobile applications
Mobile computing
Multiple objective analysis
New technology
Open access publishing
Optimization algorithms
Power consumption
Resource scheduling
Response time
Scheduling
Virtual computer systems
Virtual environments
Wireless telephone software
India
ISSN:1557-3958, 1557-3966
Online Access:Get full text
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Summary: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.
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ISSN:1557-3958
1557-3966
DOI:10.4018/IJCINI.20210701.oa5