Joint Optimization of Offloading and Resource Allocation Scheme for Mobile Edge Computing
Boutheina Dab, Nadjib Aitsaadi, Rami Langar · 2019
The high proliferation of mobile devices, deploying a myriad of application, entails an explosion of mobile traffic. Due to their resource-limitation constraint, mobile devices resort to offload computational tasks on Cloud servers and improve, hence, resource usage. Unfortunately, the conventional Mobile Cloud Computing (MCC) solution involves high transmission latency. Inspired by the visions of IoT and 5G communications, recently Mobile Edge Computing (MEC) promises a great latency reduction by pushing mobile computing and storage to the network edge (i.e., base stations and access points). The key challenge of MEC solution is to find an efficient assignment of tasks with local or remote devices while minimizing energy consumption and latency. In this paper, we propose a new joint task assignment and resource allocation approach in a multi-user WiFi-based MEC architecture. The main novelty of our work is that optimal offloading decision is jointly performed with the radio resource allocation. The objective of our scheme is to minimize the energy consumption on the mobile terminal side under the application latency constraint. To do so, we first formulate our problem as a new Integer Program (IP) while considering both delay and device computation constraints. Then, we propose a new strategy named Joint Offloading and Resource allocation in WiFi-based MEC architecture (JOR-MEC) to solve it. Based on extensive network simulations conducted with NS3 simulator while considering real input traces, we show that our proposal outperforms the related prominent baseline strategies in terms of: i) energy consumption and ii) completion delay.