用强化学习动态调度微服务任务，提升系统稳定性和资源利用率。
Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning
- 基于A3C算法构建马尔可夫决策模型，实现细粒度资源分配。
- 在真实数据集上，任务延迟降低23%，资源利用率提升18%。
- 适合高并发场景下的智能调度系统研发人员参考。
针对微服务系统中资源动态性强、任务并发密集的挑战，本文提出一种基于A3C强化学习算法的自适应资源调度方法。将调度问题建模为马尔可夫决策过程，通过联合优化策略网络与价值网络，在不同负载条件下实现细粒度资源分配。采用异步多线程学习机制，多个智能体并行采样并同步更新全局网络参数，提升了策略收敛效率与模型稳定性。实验部分使用真实世界数据集构建调度场景，与多种典型方法在任务延迟、调度成功率、资源利用率及收敛速度等指标上进行对比。结果表明，该方法在多任务并发环境下表现优异，有效缓解了传统方法在重负载下的资源分配瓶颈，展现出在微服务系统智能调度中的实际应用价值。
原文摘要 · Abstract (English)
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.
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