Deep Reinforcement Learning and Optimization Based Green Mobile Edge Computing

IEEE Consumer Communications & Networking Conference (CCNC), pp. 1–2

Abstract

In mobile edge computing (MEC) networks, by offloading tasks (partially or completely) to the MEC server, it becomes possible to complete computation-intensive and latency-critical applications without communicating with the cloud center, resulting in dramatic reduction both in latency and energy consumption. Performance improvements depend on the offloading decisions at the user equipments (UEs) and computational resource allocation at the MEC server. In this paper, we aim to optimize the UE offloading data ratios and MEC computational resource allocation under delay constraints with the goal to minimize the global energy consumption. Both conventional optimization method and learning-based approach are studied. Simulation results are provided to compare the performances of different schemes.

Authors 3

1. Yang Yang

   Syracuse University

   Affiliation as printed

   Syracuse University,Department of Electrical Engineering and Computer Science,Syracuse,NY,13244

2. Yulin Hu Aachen ISEK Research Area/Lab

   RWTH Aachen University

   Affiliation as printed

   ISEK Research Area/Lab, RWTH Aachen University,Aachen,Germany,52070

3. M. Cenk Gursoy

   Syracuse University

   Affiliation as printed

   Syracuse University,Department of Electrical Engineering and Computer Science,Syracuse,NY,13244