Deep reinforcement learning and optimization based green mobile edge computing

Yang Yang, Yulin Hu, M. Cenk Gursoy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728197944
DOIs
StatePublished - Jan 9 2021
Event18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021 - Virtual, Las Vegas, United States
Duration: Jan 9 2021Jan 13 2021

Publication series

Name2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021

Conference

Conference18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021
Country/TerritoryUnited States
CityVirtual, Las Vegas
Period1/9/211/13/21

Keywords

  • Data offloading
  • Deep learning
  • Energy consumption
  • Latency
  • Mobile edge computing (MEC)
  • Optimization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'Deep reinforcement learning and optimization based green mobile edge computing'. Together they form a unique fingerprint.

Cite this