Deep Reinforcement Learning for Intelligent Resource Allocation in Dynamic Cloud Computing Environments

William Turner

Deep Reinforcement Learning for Intelligent Resource Allocation in Dynamic Cloud Computing Environments

Author(s)	William Turner
Country	United Kingdom
Abstract	Cloud computing environments support dynamically changing computational workloads through shared processing, memory, storage, and network resources. Efficient resource allocation remains challenging because incoming tasks vary in computational demand, execution duration, priority, and service-level requirements. Conventional rule-based and heuristic allocation procedures can respond effectively under predictable operating conditions but may become less suitable when workloads change rapidly. Deep reinforcement learning offers a methodological alternative in which an intelligent scheduling agent learns resource-allocation policies through repeated interaction with a simulated cloud environment. This hypothetical methodological study examines deep reinforcement learning for intelligent resource allocation under dynamic cloud workloads. The proposed framework represents cloud allocation as a sequential decision problem containing system states, scheduling actions, and reward functions. A conceptual workload consisting of 5,000 scheduling intervals is used to compare three hypothetical allocation strategies: static threshold allocation, heuristic adaptive allocation, and a deep reinforcement learning policy. Resource utilization, service-level agreement violations, average job waiting time, and normalized energy consumption are used as evaluation indicators. The illustrative analysis assigns the strongest overall performance to the deep reinforcement learning configuration, with higher average utilization and lower job waiting time than the comparison strategies. A second analysis examines the same hypothetical policy under low, medium, high, and volatile workloads. Performance becomes weaker as environmental uncertainty increases, illustrating why a policy trained under one workload condition should not automatically be assumed to generalize to another. The study concludes that deep reinforcement learning provides a useful methodological framework for adaptive resource management because it can represent sequential decisions and delayed performance consequences. However, practical deployment requires stable training, safe exploration, reproducible simulation, workload generalization, and comparison with strong conventional allocation approaches. The numerical results reported in this manuscript are illustrative rather than experimentally validated.
Keywords	Deep Reinforcement Learning, Cloud Computing, Resource Allocation, Dynamic Workloads, Deep Q-Network, Resource Scheduling, Cloud Resource Management, Virtualization, Intelligent Scheduling, Service-Level Agreement.
Field	Engineering
Published In	Volume 1, Issue 4, September-October 2025
Published On	2025-09-02

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Deep Reinforcement Learning for Intelligent Resource Allocation in Dynamic Cloud Computing Environments

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