🤖 AI Summary
This work addresses the poor scalability and single-point-of-failure limitations of traditional centralized scheduling approaches in large-scale heterogeneous distributed systems, where dynamic workloads, resource heterogeneity, and competition for quality-of-service guarantees pose significant challenges. To overcome these issues, the authors propose DRL-MADRL, a fully decentralized multi-agent deep reinforcement learning framework that formulates task scheduling as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). Leveraging a lightweight Actor-Critic architecture and relying solely on foundational libraries such as NumPy, the framework is efficiently deployable on resource-constrained edge devices. Experimental results on a 100-node system demonstrate that DRL-MADRL reduces average task completion time by 15.6% (30.8s vs. 36.5s), lowers energy consumption by 15.2% (745.2 vs. 878.3 kWh), and achieves an 82.3% SLA compliance rate (p < 0.001). The implementation is fully open-sourced to ensure reproducibility.
📝 Abstract
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.