Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning

2025-05-01

AI Summary

To address the challenges of fine-grained resource allocation and high-concurrency task scheduling for microservice systems under dynamic workloads, this paper proposes an A3C-based intelligent scheduling framework that formalizes task scheduling as a Markov Decision Process. The framework introduces an asynchronous multi-threaded agent cooperative training mechanism, enhancing both policy convergence speed and model stability while overcoming resource bottlenecks inherent in conventional static or heuristic approaches under overload conditions. By jointly optimizing policy and value networks, and leveraging distributed parallel sampling with parameter synchronization, the framework achieves significant improvements on real-world datasets: average task latency is substantially reduced; scheduling success rate increases by 28%; resource utilization improves by 22%; convergence speed accelerates by 37% over baseline methods; and system stability rises by 42%.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

To address the challenges of high resource dynamism and intensive task concurrency in microservice systems, this paper proposes an adaptive resource scheduling method based on the A3C reinforcement learning algorithm. The scheduling problem is modeled as a Markov Decision Process, where policy and value networks are jointly optimized to enable fine-grained resource allocation under varying load conditions. The method incorporates an asynchronous multi-threaded learning mechanism, allowing multiple agents to perform parallel sampling and synchronize updates to the global network parameters. This design improves both policy convergence efficiency and model stability. In the experimental section, a real-world dataset is used to construct a scheduling scenario. The proposed method is compared with several typical approaches across multiple evaluation metrics, including task delay, scheduling success rate, resource utilization, and convergence speed. The results show that the proposed method delivers high scheduling performance and system stability in multi-task concurrent environments. It effectively alleviates the resource allocation bottlenecks faced by traditional methods under heavy load, demonstrating its practical value for intelligent scheduling in microservice systems.

Problem

• Addresses high resource dynamism in microservice systems
• Optimizes fine-grained resource allocation under varying loads
• Improves scheduling performance in multi-task concurrent environments

Innovation

• A3C reinforcement learning for microservices
• Markov Decision Process modeling scheduling
• Asynchronous multi-threaded learning mechanism