Autonomous Resource Management in Microservice Systems via Reinforcement Learning

2025-07-17

AI Summary

To address resource imbalance, high latency, low throughput, and challenges in coordinated scheduling of multi-dimensional resources (e.g., CPU, memory, storage) in microservice systems under scale expansion and dynamic workloads, this paper proposes a reinforcement learning–based multi-objective adaptive resource scheduling framework. The framework integrates dynamic workload awareness with joint modeling of heterogeneous resources, enabling continuous policy optimization through environment interaction to achieve fine-grained, real-time, and scenario-adaptive autonomous resource management. Experimental results demonstrate that, compared to static and heuristic approaches, the proposed method reduces average response latency by 32.7%, increases throughput by 28.4%, improves resource utilization by 21.5%, and decreases energy consumption by 19.3% across both high- and low-load scenarios. Moreover, it supports simultaneous optimization of latency, throughput, and energy efficiency—achieving true multi-objective coordination.

Technology Category

• Machine Learning: Scalability of ML Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper proposes a reinforcement learning-based method for microservice resource scheduling and optimization, aiming to address issues such as uneven resource allocation, high latency, and insufficient throughput in traditional microservice architectures. In microservice systems, as the number of services and the load increase, efficiently scheduling and allocating resources such as computing power, memory, and storage becomes a critical research challenge. To address this, the paper employs an intelligent scheduling algorithm based on reinforcement learning. Through the interaction between the agent and the environment, the resource allocation strategy is continuously optimized. In the experiments, the paper considers different resource conditions and load scenarios, evaluating the proposed method across multiple dimensions, including response time, throughput, resource utilization, and cost efficiency. The experimental results show that the reinforcement learning-based scheduling method significantly improves system response speed and throughput under low load and high concurrency conditions, while also optimizing resource utilization and reducing energy consumption. Under multi-dimensional resource conditions, the proposed method can consider multiple objectives and achieve optimized resource scheduling. Compared to traditional static resource allocation methods, the reinforcement learning model demonstrates stronger adaptability and optimization capability. It can adjust resource allocation strategies in real time, thereby maintaining good system performance in dynamically changing load and resource environments.

Problem

• Optimize microservice resource allocation using reinforcement learning
• Improve system performance under varying load conditions
• Reduce latency and energy consumption in dynamic environments

Innovation

• Reinforcement learning optimizes microservice resource allocation
• Intelligent scheduling adapts to dynamic load conditions
• Multi-objective optimization improves performance and efficiency