Multi-Agent Reinforcement Learning for Efficient Task Scheduling in Edge-Cloud Systems

This generated a new trend in modern computing, known as the edge-cloud systems, which matrix combines the efficiency of edge computing in terms of latency with the computing power of cloud systems. Task scheduling is a fundamental issue in these systems and its goals involves instructing the computational loads across the edges and the clouds for a desired set of objectives such as low latency, high resource usage, minimal energy consumption, and high throughput among others. Many of the conventional scheduling paradigms are not effective when applied to edge-cloud settings due to their centralized or heuristic nature.

Because scheduling is still in its early stages, there is much opportunity to find novel ways to apply it to edge-cloud systems, as these environments are undervalued now but are expected to see rapid growth soon.

The following challenges are well addressed by Multi-Agent Reinforcement Learning (MARL) which is based on a decentralized decision-making process where several intelligent agents jointly learn and improve scheduling policies. The strength to be gained from the use of MARL lies in the capacity of agents to change their behavior in response to their environment, gain from experience and engagement and make decisions in the light of the context. The techniques like cooperative learning, reward shaping and communication between agents of concurrent tasks make the MARL to provide effective scheduling of tasks in large scale and complex systems besides heterogeneous one.

This article also discusses the rudiments of MARL and the way it is implemented in edge-cloud scheduling of tasks while also pointing out the advantages of the implementation: minimal latency, scalability, heightened system resilience. It also outlines future research areas including how to scale MARL solutions, how agents should be coordinated and the computational resource constraints of the solutions whenever they are to be deployed in practice. Based on the relevant literature and the cases, this paper lays theoretical and practical foundations of the next generation task scheduling defined by the novel MARL framework to enable smarter edge-cloud environments.