Online learning for offloading and autoscaling in renewable-powered mobile edge computing

Jie Xu, Shaolei Ren

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

41 Scopus citations

Abstract

Mobile edge computing (a.k.a. fog computing) has recently emerged to enable in-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in renewable-powered mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run- time performance when compared to standard reinforcement learning algorithms such as Q- learning.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509013289
DOIs
StatePublished - Feb 2 2017
Event59th IEEE Global Communications Conference, GLOBECOM 2016 - Washington, United States
Duration: Dec 4 2016Dec 8 2016

Other

Other59th IEEE Global Communications Conference, GLOBECOM 2016
CountryUnited States
CityWashington
Period12/4/1612/8/16

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

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