Collaborative Task Scheduling Using Multi-Agent Deep Reinforcement Learning

Managing resources effectively, scalably, and affordably is a multi-faceted online decision-making challenge increasingly encountered in networking and cloud computing. Specifically, task scheduling is a complex challenge essential for the optimal functioning of today’s systems. Traditional heuristic approaches to scheduling are labor-intensive to design and particularly difficult to tune, leading to the proposal of various machine-learning-based methods. Reinforcement Learning (RL) showed great results in similar decision making problems, and many existing approaches employ RL to solve task scheduling problems. Most of these studies either focus on single-agent scenarios, which inherently suffer from scalability issues, or on highly specialised multi-agent applications. We propose a general-purpose multi-agent RL framework that can successfully learn collaborative optimal scheduling policies, making one step further towards clouds and networks that are both scalable and autonomous. Our experiments demonstrate that these agents can collaboratively learn optimal scheduling policies for dynamic workloads.