Reinforcement Learning-Based Resource Optimization for Autonomous Intelligent Computing Platforms

  • Authors

    • Sai Kishore Independent Researcher, USA. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-V9I3P102

    Published 07-05-2026

  • Reinforcement Learning, Resource Optimization, Autonomous Computing, Deep Learning, Adaptive Scheduling, Energy Efficiency, Cloud-Edge Computing

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    To support diverse application workloads in dynamic environments with performance, energy efficiency, reliability and cost constraints, the autonomous intelligent computing platforms must be able to manage workloads that are heterogeneous. In the past, there were several challenges to overcome when using conventional scheduling methods, such as constantly changing workloads, varying hardware set-ups and different service-level targets. In this article, a reinforcement learning (RL) based resource optimization framework is proposed, which combines workload characterization, state estimation, adaptive allocation, energy-aware scheduling, and energy-driven policy improvement. Resource management is expressed as a Markov decision process with processor utilization, memory pressure, availability of accelerators, queue length, workload urgency, energy consumption as well as service-level indicators. A composite reward function is used to guide a deep reinforcement learning agent to decide resource allocation, task placement, container scaling, migration, and prioritization decisions. Constraint-aware action filtering and workload forecasting increases the safety and stability of the operations. Evaluation of the framework is conceptual; it includes the following metrics: throughput, latency, energy consumption, resource utilization, service-level violation and optimization overhead. Comparative numerical values are indicative, and need experimental confirmation. The proposed method lays the groundwork for resource management in intelligent cloud-edge infrastructures that is adaptive, scalable, and reliable.

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