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Volume 2 , Issue 2 , PP: 14-25, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Rokaia M. Zaki 1 *
Doi: https://doi.org/10.54216/MOR.020202
This review reviews metaheuristic optimization algorithms for solving various important issues in cloud computing, such as scheduling, resource provisioning and energy consumption. Specifically, PSO, GA, and DRL are application area-specific intelligent scheduling algorithms that offer high scalability, flexibility, and efficiency in solving NP-hard problems, thereby improving system performance and QoS. The following are some of the key strengths in the study: The energy utilization and the cost utilization as key strengths are presented; the weaknesses are programs and things such as scalability and integration issues that arise when using hybrid systems. The focus for the future lies in combining machine-learning techniques, improving the further development of hybrid approaches, and testing them in real cloud systems to cope with the increasing sophistication of distributed systems. This paper provides an outline of metaheuristic optimization with an emphasis on how this area can contribute to enhancements and further developments in the capacity, recyclability, and dependability of cloud computing.
Metaheuristic optimization , Cloud computing , Task scheduling , Resource allocation , Energy management , Hybrid algorithms
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