Optimization of Cloud Computing Resource Allocation Through Artificial Intelligence-Based Scheduling Algorithms

Nesan Ryan; Scott L. Taylor; Derran Anderson

Authors

Nesan Ryan Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA. Author
Scott L. Taylor Department of Computer Science, University of New Hampshire, Durham, NH, USA. Author
Derran Anderson Department of Computer Science, University of Alabama at Birmingham, Birmingham, AL, USA. Author

Keywords:

cloud computing, resource allocation, artificial intelligence, scheduling algorithms, reinforcement learning, governance, robustness, sustainability, fairness

Abstract

Cloud computing has become a foundational infrastructure for modern digital services, yet the allocation of its computational, storage, and network resources remains a persistent system-level challenge. Conventional scheduling policies often rely on static thresholds, heuristic placement rules, and reactive adjustments that struggle to handle heterogeneous workloads, rapid demand fluctuations, and long-term operational objectives. Artificial intelligence-based scheduling algorithms have emerged as a promising alternative because they can learn workload patterns, infer performance interference, and generate allocation decisions that are difficult to express through hand-crafted policies. This paper presents a system-oriented analysis of AI-driven resource allocation rather than a narrowly algorithmic contribution. It examines the architectural context of cloud scheduling, the main families of AI-based scheduling methods, structural trade-offs between centralized and decentralized control, and the governance, fairness, robustness, deployment, sustainability, and policy dimensions that shape real-world adoption. The discussion draws on simulation frameworks, production cluster management systems, and foundational learning methods to identify where AI-based schedulers provide leverage and where they introduce new operational risks. The paper argues that effective AI-based resource allocation requires not only accurate predictive models and stable learning algorithms, but also careful integration with organizational controls, transparency mechanisms, energy management, and accountability structures. Future research should address robustness under data drift, explainability for human operators, cross-layer coordination, and the sociotechnical conditions under which learned scheduling policies can be safely deployed in large-scale cloud infrastructures.

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Optimization of Cloud Computing Resource Allocation Through Artificial Intelligence-Based Scheduling Algorithms

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