Machine learning-based cloud resource allocation algorithms: a comprehensive comparative review
📝 Abstract
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.
💡 Analysis
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.
📄 Content
Cloud computing has transformed the modern computing landscape with the global market reaching $912.77 billion in 2025 and projected to grow at a compound annual growth rate of 21.20% through 2034 (Precedence Research, 2025). This explosive growth reflects the critical role cloud infrastructure plays in supporting digital transformation initiatives across industries, as organizations increasingly rely on cloud services for scalability, flexibility, and cost optimization (Buyya et al., 2009;Armbrust et al., 2010). However, this rapid expansion has introduced challenges in resource allocation and management.
The complexity of cloud environments has grown exponentially as businesses adopt hybrid and multi-cloud strategies to meet operational requirements. Traditional resource allocation approaches based on heuristic algorithms and static provisioning models (Holland, 1992;Kennedy and Eberhart, 1995;Dorigo et al., 1996) have proven inadequate for handling the dynamic, heterogeneous, and multi-tenant nature of cloud infrastructures (Buyya et al., 2009; OPEN ACCESS EDITED BY Xu Zheng, University of Electronic Science and Technology of China, China 2 Related work and background
Cloud resource allocation requires the systematic assignment of computational resources including CPU, memory, storage, and network bandwidth among competing user requests to optimize system performance while maintaining service level agreements. The fundamental challenge lies in efficiently mapping heterogeneous user workloads to distributed physical resources while satisfying multiple conflicting objectives such as minimizing execution time, reducing energy consumption, and maximizing resource utilization.
Traditional resource allocation approaches in cloud computing environments primarily rely on heuristic algorithms, meta-heuristic techniques, and hybrid methods. Heuristic algorithms, including First-Fit, Best-Fit, and Greedy algorithms (Zhang et al., 2010), provide intuitive solutions based on empirical construction with lower computational complexity and predictable worst-case performance. These methods typically use simple rules such as selecting the first available resource that meets minimum requirements or choosing resources with the smallest remaining capacity after allocation.
Meta-heuristic approaches, including Genetic Algorithm (GA) (Holland, 1992), Particle Swarm Optimization (PSO) (Kennedy and Eberhart, 1995), and Ant Colony Optimization (ACO) (Dorigo et al., 1996), have gained prominence for addressing the NP-hard nature of resource allocation problems. These algorithms employ populationbased search mechanisms to explore solution spaces more comprehensively than heuristic methods, often achieving superior optimization results at the cost of increased computational complexity. Hybrid approaches combine multiple optimization techniques, leveraging the strengths of different algorithms to address specific aspects of the resource allocation problem.
Performance evaluation in cloud resource allocation relies on Quality of Service (QoS) metrics (Garg et al., 2013;Zhang et al., 2010) that capture various aspects of system behavior and user experience. Critical performance indicators include response time, throughput, resource utilization, availability, and cost efficiency. Response time measures the latency between request submission and completion, while throughput quantifies the system’s capacity to process requests within specific time periods. Resource utilization metrics assess the efficiency of hardware usage, preventing both over-provisioning and under-utilization scenarios that lead to economic inefficiencies (Beloglazov and Buyya, 2012;Li et al., 2013).
Advanced performance evaluation frameworks incorporate multidimensional metrics that address the complexity of modern cloud environments. These include scalability measures that evaluate system behavior under varying loads, reliability indicators that assess fault tolerance capabilities, and energy efficiency metrics that quantify power consumption relative to computational output. The integration of Service Level Objectives (SLO) and Service Level Agreements (SLA) (Buyya et al., 2009) provides contractual frameworks for performance measurement, establishing measurable targets for system behavior. 10.3389/fcomp.2025.1678976 Frontiers in Computer Science 03 frontiersin.org
The paradigm shift from reactive to predictive resource allocation represents a fundamental transformation in cloud computing management strategies. Traditional reactive approaches respond to resource demands after they occur, leading to suboptimal performance during peak loads and resource waste during low-demand periods. In contrast, AI/ML-enabled predictive allocation systems analyze historical patterns, workload characteristics, and system behaviors to anticipate future resource requirements, enabling proactive resource provisioning and optimization.
Machine learning techniques have demonstra
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