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REVIEW article

Front. Comput. Sci.

Sec. Theoretical Computer Science

Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1678976

Machine Learning-Based Cloud Resource Allocation Algorithms: A Comprehensive Comparative Review

Provisionally accepted
Deep  BodraDeep Bodra1*Sushil  KhairnarSushil Khairnar2
  • 1Harrisburg University of Science and Technology, Harrisburg, United States
  • 2Virginia Polytechnic Institute and State University, Blacksburg, United States

The final, formatted version of the article will be published soon.

Cloud resource allocation has emerged as a major challenge in modern computing environments, with organizations struggling to manage complex, dynamic workloads while optimizing performance and cost efficiency. Traditional heuristic approaches prove inadequate for handling the multi-objective optimization demands of existing cloud infrastructures. This paper presents a comparative analysis of state-of-the-art artificial intelligence and machine learning algorithms for resource allocation. We systematically evaluate ten algorithms across four categories: Deep Reinforcement Learning approaches, Neural Network architectures, Traditional Machine Learning enhanced methods, and Multi-Agent systems. Analysis of published results demonstrates significant performance improvements across multiple metrics including makespan reduction, cost optimization, and energy efficiency gains compared to traditional methods. The findings reveal that hybrid architectures combining multiple artificial intelligence and machine learning techniques consistently outperform single-method approaches, with edge computing environments showing the highest deployment readiness. Our analysis provides critical insights for both academic researchers and industry practitioners seeking to implement next-generation cloud resource allocation strategies in increasingly complex and dynamic computing environments.

Keywords: Cloud computing, Resource Allocation, deep reinforcement learning, Neuralnetworks, multi-agent systems, Performance optimization, energy efficiency

Received: 03 Aug 2025; Accepted: 06 Oct 2025.

Copyright: © 2025 Bodra and Khairnar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Deep Bodra, dbodra@my.harrisburgu.edu

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.