用轻量强化学习实现分布式任务调度，提升效率与能效。
Decentralized Task Scheduling in Distributed Systems: A Deep Reinforcement Learning Approach
- 采用去中心化多智能体强化学习，基于部分可观马尔可夫决策过程建模。
- 任务完成时间缩短15.6%，能耗降低15.2%，服务等级协议满足率达82.3%。
- 仅需NumPy等轻量库，适合部署在资源受限的边缘设备上。
大规模分布式系统中的高效任务调度面临动态负载、异构资源和竞争服务质量要求的挑战。传统集中式方法存在可扩展性限制和单点故障问题，而经典启发式算法难以适应变化环境。本文提出一种去中心化多智能体深度强化学习（DRL-MADRL）框架用于异构分布式系统的任务调度。将问题建模为去中心化部分可观马尔可夫决策过程（Dec-POMDP），并设计仅使用NumPy的轻量级演员-评论家架构，可在资源受限的边缘设备上部署，无需重型机器学习框架。基于公开的Google Cluster Trace数据集提取工作负载特征，在100节点异构系统上对每轮1000个任务进行30次实验。结果表明，平均任务完成时间减少15.6%（30.8秒对比随机基线36.5秒），能耗降低15.2%（745.2千瓦时对比878.3千瓦时），服务等级协议满足率提升至82.3%（基线为75.5%），所有改进均具统计显著性（p < 0.001）。轻量实现仅需NumPy、Matplotlib和SciPy。完整源代码与实验数据已开源：https://github.com/danielbenniah/marl-distributed-scheduling。
原文摘要 · Abstract (English)
Efficient task scheduling in large-scale distributed systems presents significant challenges due to dynamic workloads, heterogeneous resources, and competing quality-of-service requirements. Traditional centralized approaches face scalability limitations and single points of failure, while classical heuristics lack adaptability to changing conditions. This paper proposes a decentralized multi-agent deep reinforcement learning (DRL-MADRL) framework for task scheduling in heterogeneous distributed systems. We formulate the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and develop a lightweight actor-critic architecture implemented using only NumPy, enabling deployment on resource-constrained edge devices without heavyweight machine learning frameworks. Using workload characteristics derived from the publicly available Google Cluster Trace dataset, we evaluate our approach on a 100-node heterogeneous system processing 1,000 tasks per episode over 30 experimental runs. Experimental results demonstrate 15.6% improvement in average task completion time (30.8s vs 36.5s for random baseline), 15.2% energy efficiency gain (745.2 kWh vs 878.3 kWh), and 82.3% SLA satisfaction compared to 75.5% for baselines, with all improvements statistically significant (p < 0.001). The lightweight implementation requires only NumPy, Matplotlib, and SciPy. Complete source code and experimental data are provided for full reproducibility at https://github.com/danielbenniah/marl-distributed-scheduling.
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