Enhancing Cloud Resource Allocation with a Multi-Objective SARSA Whale Optimization Algorithm (MO_SAWOA)

Section: Articles
Issue
Vol. 20 No. 1 (2026): Vol. 20 No. 1 (2026): Volume 20 Issue 1
Pages
115-123

Abstract

As we know, cloud computing has become an increasingly popular solution to deliver scalable and effective solutions to the needs of today's modern applications. One of the greatest problems in this context is the efficient allocation of resources to the different needs of numerous users and applications. Reinforcement learning (RL) is an approach that can adapt dynamically to changing environments and is a promising solution. In this paper, the SARSA (state action reward state action) RL algorithm is combined with the whale optimization algorithm (WOA) to propose a new resource allocation approach for cloud computing based on a multi objective optimization. We are going to refer to this method as MO SAWOA. It assists the SARSA algorithm to faster converge and prevents it from being trapped in the local optima. The proposed system handles complex multi objective resource allocation issues by improving load balancing, reducing makespan, saving costs, and enhancing user experience in dynamic cloud environments. It can also be used for real time systems (RTS) like public clouds like Amazon Web Services (AWS).

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Authors

Hiba Qasim

College Of Computer Science, Mustansiriyah University, Baghdad, Iraq

Huda Abd Alali
Soukaena Hassan

Identifiers

DOI: 10.33899/rjcsm.v20i1.60674

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Enhancing Cloud Resource Allocation with a Multi-Objective SARSA Whale Optimization Algorithm (MO_SAWOA). (2026). AL-Rafidain Journal of Computer Sciences and Mathematics, 20(1), 115-123. https://doi.org/10.33899/rjcsm.v20i1.60674
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How to Cite

Enhancing Cloud Resource Allocation with a Multi-Objective SARSA Whale Optimization Algorithm (MO_SAWOA). (2026). AL-Rafidain Journal of Computer Sciences and Mathematics, 20(1), 115-123. https://doi.org/10.33899/rjcsm.v20i1.60674