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A Hybrid Binary Bird Swarm Optimization (BSO) and Dragonfly Algorithm (DA) for VM Allocation and Load Balancing in Cloud

Thanwamas Kassanuk (Pibulsongkram Rajabhat University, Thailand) and Khongdet Phasinam (Pibulsongkram Rajabhat University, Thailand)

Source Title: International Journal of Cloud Applications and Computing (IJCAC)13(1)

ISSN: 2156-1834|EISSN: 2156-1826|EISBN13: 9781668479810|DOI: 10.4018/IJCAC.318698

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MLA

Kassanuk, Thanwamas, and Khongdet Phasinam. "A Hybrid Binary Bird Swarm Optimization (BSO) and Dragonfly Algorithm (DA) for VM Allocation and Load Balancing in Cloud." IJCAC vol.13, no.1 2023: pp.1-21. https://doi.org/10.4018/IJCAC.318698

APA

Kassanuk, T. & Phasinam, K. (2023). A Hybrid Binary Bird Swarm Optimization (BSO) and Dragonfly Algorithm (DA) for VM Allocation and Load Balancing in Cloud. International Journal of Cloud Applications and Computing (IJCAC), 13(1), 1-21. https://doi.org/10.4018/IJCAC.318698

Chicago

Kassanuk, Thanwamas, and Khongdet Phasinam. "A Hybrid Binary Bird Swarm Optimization (BSO) and Dragonfly Algorithm (DA) for VM Allocation and Load Balancing in Cloud," International Journal of Cloud Applications and Computing (IJCAC) 13, no.1: 1-21. https://doi.org/10.4018/IJCAC.318698

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Abstract

The cloud platform is becoming one of the fastest-rising environments in human activities, connecting the whole world in the upcoming decades. The three crucial aspects of cloud computing that enhance the quality of service are load balancing, task scheduling, and resource allocation. To address these issues, the research proposed dynamic degree balance with CPU_based VM allocation policy integrated with hybrid bird swarm optimization (BSO) and dragonfly algorithm (DA). The proposed algorithm focuses on improving the overall performance of the system by limiting DoI, execution time, and response time, while also maintaining system balance. In the CloudSim tool, D2B_CPU based BSO-DA is implemented and evaluated. The simulation results, on the other hand, show that the proposed BSO and DA-based load balancing scheme is significantly more effective in balancing load optimally among virtual machines more quickly than existing algorithms. The proposed method's efficiency is evaluated by comparing it to other existing techniques.