A Deep Reinforcement Learning Approach to Multi-component Job Scheduling in Edge Computing
This addresses scheduling challenges in edge computing for improved job performance, but it is incremental as it applies an existing DRL method to a specific domain.
The paper tackles the problem of scheduling multi-component application jobs in edge computing systems with geo-distributed nodes and dynamic conditions, using a Deep Reinforcement Learning actor-critic algorithm to minimize average job slowdown, and demonstrates through simulations that it outperforms existing algorithms on synthetic and Google cloud data.
We are interested in the optimal scheduling of a collection of multi-component application jobs in an edge computing system that consists of geo-distributed edge computing nodes connected through a wide area network. The scheduling and placement of application jobs in an edge system is challenging due to the interdependence of multiple components of each job, and the communication delays between the geographically distributed data sources and edge nodes and their dynamic availability. In this paper we explore the feasibility of applying Deep Reinforcement Learning (DRL) based design to address these challenges. We introduce a DRL actor-critic algorithm that aims to find an optimal scheduling policy to minimize average job slowdown in the edge system. We have demonstrated through simulations that our design outperforms a few existing algorithms, based on both synthetic data and a Google cloud data trace.