Abstract: Cloud computing has emerged as a transformative technology, offering remote access to various computing resources. However, efficiently managing these resources while curbing escalating energy consumption remains a critical challenge. In response, this paper presents the Micro-Genetic Algorithm with Cuckoo Search (MG-CS), a novel approach for enhancing cloud computing efficiency. MG-CS optimizes load balancing and power reduction and significantly contributes to reducing operational costs, ensuring compliance with service level agreements, and enhancing overall service quality. Our experiments showcase MG-CS's versatility in achieving a well-balanced distribution of workloads, resource optimization, and substantial energy savings. This multifaceted approach redefines cloud resource management, offering an environmentally sustainable and cost-effective solution. By introducing MG-CS, this research addresses the pressing challenges in cloud computing, aligning it with environmental responsibility and economic efficiency.

Jun ZHOU and Youyou Li, “MG-CS: Micro-Genetic and Cuckoo Search Algorithms for Load-Balancing and Power Minimization in Cloud Computing” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01411117

@article{ZHOU2023,
title = {MG-CS: Micro-Genetic and Cuckoo Search Algorithms for Load-Balancing and Power Minimization in Cloud Computing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01411117},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01411117},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {Jun ZHOU and Youyou Li}
}