Machine Learning based Timeliness-Guaranteed and Energy-Efficient Task Assignment in Edge Computing Systems

The proliferation in the use of the Internet of Things (IoT) and Machine Learning (ML) techniques in edge computing systems have paved the way of using Intelligent Cognitive Assistants (ICA) for assisting people in working, learning, transportation, healthcare, and other activities. A challenge here is how to schedule application tasks between the three tiers in the edge computing system (i.e., remote cloud, fog and edge devices) according to several considered factors such as latency, energy, and bandwidth consumption. However, the state-of-the-art approaches for this challenge fall short in providing a schedule in real time for critical ICA tasks due to complex calculation phase. In this paper, we propose a novel ReInforcement Learning based Task Assignment approach, RILTA, that ensures the timeliness guaranteed execution of ICA tasks with high energy efficiency. We first formulate the task-scheduling problem in the edge computing systems considering timeliness and energy consumption in ICA applications. We then propose a heuristic for solving the problem and design the reinforcement model based on the output of the proposed heuristic. Our simulation results show that RILTA can reduce the task processing time and energy consumption with higher timeliness guarantee in comparison to other existing methods by 13 - 22% and 1 - 10% respectively.