Optimal Micro-Task Scheduling for Multi-Hop D2D-Enabled Mobile Edge Computing

Mobile Edge Computing (MEC) is a promising solution to tackle the upcoming computing tsunami in 5G/6G era by effectively utilizing the idle resource at the mobile edge. In this work, a multi-hop D2D-enabled MEC scenario was studied, where mobile devices at network edge connect and share resources with each other via multi-hop D2D. The research focus was on the micro-task scheduling problem in the multi-hop D2D-enabled MEC system, where each task was divided into multiple sequential micro-tasks, such as data downloading micro-task, data processing micro-task, and data uploading micro-task, according to their functionalities as well as resource requirements. A joint Task Failure Probability and Energy Consumption Minimization (TFP-ECM) problem was proposed, aiming at minimizing the task failure probability and the energy consumption jointly. To solve the problem, several linearization methods were proposed to relax the constraints and convert the original problem into an integer linear programming (ILP). Simulation results show that the proposed solution outperformed the existing solutions (with indivisible tasks or without resource sharing) in terms of both total cost and task failure probability.