Machine learning-based cloud resource allocation algorithms: a comprehensive comparative review
It addresses the challenge of optimizing performance and cost in dynamic cloud environments for organizations, but it is incremental as it reviews and compares existing methods rather than introducing new ones.
This paper conducted a comparative review of 10 AI and machine learning algorithms for cloud resource allocation, finding that hybrid architectures consistently outperform traditional methods with improvements in metrics like makespan reduction, cost optimization, and energy efficiency.
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 10 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.