SATSS: A Self-Adaptive Task Scheduling Scheme for Mobile Edge Computing

Mobile edge computing (MEC) is an emerging paradigm that supports low-latency applications in resource-constrained scenarios, such as the Internet of Things (IoT) and vehicular networks. MEC makes it feasible to process and handle massive amounts of data and service requests generated by mobile end users or IoT devices and to deliver timely responses or interventions. However, the computers forming an MEC system typically have limited computing resources, which must be shared by multiple tasks and many simultaneous service requests. How to dispatch and schedule computational tasks from end users in an MEC system is a challenging problem, especially for latency-sensitive applications. In this paper, we propose a self-adaptive task dispatching and scheduling scheme to deliver low-latency service responses in a resource-efficient way. The proposed approach prioritizes computational tasks based on their attributes (e.g., CPU and RAM requirements, priority level, and expiration time) and solves the scheduling problem using a reinforcement learning approach. The feasibility and effectiveness of the proposed scheme are verified using simulation and a small-scale case study on an MEC testbed, demonstrating that the proposed scheme is effective and efficient.