Autonomous Resource Management in Microservice Systems via Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning approach for microservice resource management that dynamically optimizes resource allocation, improving response time, throughput, and energy efficiency in diverse load scenarios.

Contribution

It presents a novel reinforcement learning-based scheduling algorithm that adapts in real-time to changing microservice environments, outperforming traditional static methods.

Findings

Significantly improves response speed and throughput under high concurrency.

Optimizes resource utilization and reduces energy consumption.

Demonstrates strong adaptability in dynamic load conditions.

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.