Hybridization of the Q-learning and honey bee foraging algorithms for load balancing in cloud environments

Adewale, Adeyinka Ajao and OBIAZI, OGHORCHUKWUYEM ORIEKOSE and Okokpujie, Kennedy O. and Koto, Omiloli (2024) Hybridization of the Q-learning and honey bee foraging algorithms for load balancing in cloud environments. International Journal of Electrical and Computer Engineering (IJECE), 14 (4). pp. 1-15. ISSN 2088-8708

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Abstract

Load balancing (LB) is very critical in cloud computing because it keeps nodes from being overloading while others are idle or underutilized. Maintaining the quality of service (QoS) characteristics like response time, throughput, cost, makespan, resource utilization, and runtime is difficult in cloud computing due to load balancing. A robust resource allocation strategy contributes to the end user receiving high-quality cloud computing services. An effective LB strategy should improve and deliver required user satisfaction by efficiently using the resources of virtual machines (VM). The Q-learning method and the honey bee foraging load balancing algorithm were combined in this study. This hybrid combination of a load balancing algorithm and a machine learning method has reduced the runtime of load balancing activities and makespan, and increased task throughput in a cloud computing environment thereby enhancing routing activities. It achieved this by continuously tracking the usage histories of the VMs and altering the usage matrix to send jobs to the VMs with the best usage histories.

Item Type:	Article
Uncontrolled Keywords:	Cloud computing Honey bee foraging Load balancing Q-learning Throughput
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science Faculty of Engineering, Science and Mathematics > School of Mathematics
Depositing User:	nwokealisi
Date Deposited:	29 Nov 2024 08:26
Last Modified:	29 Nov 2024 08:26
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/18613

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