Scalable Reinforcement Learning for Sustainable Resource Optimization in Distributed Systems

The modern digital infrastructure is built on the abstract-Distributed systems and serves the purposes of high-scale applications like smart grids, intelligent transportation, industrial automation, and data-driven services. Nonetheless, the growing size and diversity of distributed resources, dynamic, uncertain workloads pose great obstacles to the sustainable allocation of resources, energy management, and scalability of the system. Conventional optimization methods tend to be incapable of adapting to real-time changes and do not have the ability to reconcile long-term sustainability goals and short term performance demands. In this paper, a Scalable Reinforcement Learning-based Sustainable Resource Optimization (SRL-SRO) framework of distributed systems is proposed. The suggested solution uses deep reinforcement learning to dynamically train the policies of optimal resource allocation that optimize the energy efficiency, resource utilization, time taken to complete tasks, and carbon conscious operational measures. A distributed multi-agent learning system is proposed to allow the process of determining the decisions in a scale-out fashion on heterogeneous nodes and ensure the global coordination. It has the framework that has adaptive reward modeling, workload-sensitive state representation, and hierarchical policy learning thus maintaining stability and scalability in large-scale settings. It has been shown through simulation that the suggested SRL-SRO framework consumes much less energy and costs less to operate than conventional heuristic and static optimization methods, and is more effective in terms of resource use and system throughput. The model offers a scalable, intelligent, and eco-friendly model in distribution infrastructures of the next generation.