A Multi-Dimensional Resource Crowdsourcing Framework for Mobile Edge Computing

Abstract

Mobile Edge Computing (MEC) is a promising solution to tackle the upcoming computing tsunami in 5G era, by effectively utilizing the idle resource at the mobile edge. In this work, we study such an MEC scenario, where mobile devices at edge share their heterogeneous resources with each other, hence forming a multi-dimensional resource crowdsourcing (sharing) framework. We are interested in the problem of how to optimally offload tasks to mobile devices under this framework, aiming at minimizing the total energy cost and maximizing the overall task completion. To study the problem, we first propose a general task model, where each task is divided into multiple sequential subtasks according to their functionalities as well as resource requirements. Then, based on the task model, we propose a Joint Energy Consumption and Task Failure Probability Minimization Problem, which decides when and where each subtask will be offloaded to. The problem is challenging to solve, mainly due to the inherent constraints between the scheduling of different subtasks. Therefore, we propose several linearization methods to relax the constraints, and convert the original problem into an integer linear programming (ILP), which can be solved by many classic methods effectively. We further perform simulations, which show that our proposed solution outperforms the existing solutions (with indivisible tasks or without resource sharing) in terms of both the total cost and the task failure probability. Precisely, our proposed solution can reduce the total cost by $25\%\sim 85\%$ and the task failure probability by $10\%\sim 35\%$.