Reinforcement Learning–Based Dynamic Decision Framework for Server Backend Resource Management

Abstract

With the continuous development of cloud computing platforms, microservice architectures, and online service systems, server backend resource management faces challenges such as frequent load fluctuations, significant resource contention, complex service relationships, and diverse control objectives. Traditional management methods relying on static rules or local optimization are no longer adequate for the real-time control needs of highly dynamic environments. To address this issue, this paper studies reinforcement learning modeling and dynamic decision-making methods for server backend resource management, constructing a unified framework covering state representation, context awareness, policy learning, value assessment, and multi-objective reward constraints. At the state modeling level, operational information such as resource occupancy, task queuing, service latency, and dependencies is jointly represented to enhance the ability to perceive complex backend environments. At the decision modeling level, a context-aware mechanism and policy network are introduced to adaptively control resource scheduling behavior during continuous operation. At the optimization level, the stability of policy updates is improved by combining value functions and advantage estimation, and a comprehensive reward design is used to coordinate the relationship between throughput, latency, resource utilization efficiency, and operating costs. An experimental environment was built based on open-source cluster trajectory data, and compared with other methods in the same direction. The results show that the proposed method can achieve good overall performance in complex backend resource management scenarios, demonstrating strong decision-making effectiveness and system adaptability. The research results indicate that reinforcement learning can provide a more intelligent, systematic, and long-term optimization-capable modeling path for server backend resource allocation and dynamic scheduling.