Lifetime Maximization in Mobile Edge Computing Networks

Sabyasachi Gupta, Jacob Chakareski

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Mobile edge computing has emerged as a promising technology to augment the computational capabilities of mobile devices. For a multi-user network in which its users periodically compute their tasks with the help of an edge cloud, we investigate the network lifetime maximization problem based on present user task information. We pursue this objective via a minimum energy efficiency maximization (MEEM) strategy that jointly optimizes the fraction of user task computations offloaded to the cloud and the respective allocation of edge computing and network communication resources across the users. We also investigate the network lifetime maximization problem for the case when the user task information is available for all future time slots, as well. This setting represents an upper bound for the MEEM strategy. Optimal solutions for both investigated strategies are formulated via feasibility testing and geometric programming. We show that MEEM can achieve a 70% lifetime improvement over the state-of-the-art and 460% lifetime improvement over the case of local user task computation only. We also show that for a high value of the maximum tolerable delay for completing the computation tasks of the users, MEEM achieves the globally optimal network lifetime performance. Finally, we show that MEEM achieves a significant reduction (3X) in variation of enabled network lifetime over diverse network topologies, relative to the state-of-the-art.

Original languageEnglish (US)
Article number8955967
Pages (from-to)3310-3321
Number of pages12
JournalIEEE Transactions on Vehicular Technology
Volume69
Issue number3
DOIs
StatePublished - Mar 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Mobile-edge computing
  • energy efficiency
  • lifetime maximization
  • resource allocation

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