Dependency-Aware Computation Offloading for Mobile Edge Computing with Edge-Cloud Cooperation

Long CHEN, Jigang WU, Jun ZHANG, Hong Ning DAI, Xin LONG, Mianyang YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Citation (Scopus)

Abstract

Most of existing mobile edge computing (MEC) studies consider the remote cloud server as a special edge server, the opportunity of edge-cloud collaboration has not been well exploited. We propose a dependency-aware offloading scheme in MEC with edge-cloud cooperation under task dependency constraints. Each mobile device has a limited budget and has to determine which sub-task should be computed locally or should be sent to the edge or remote cloud. We formulate two NP-hard task finishing time minimization sub-problems. We then devise one greedy algorithm with approximation ratio of <formula><tex>${1+\epsilon}$</tex></formula> for the first mode with edge-cloud cooperation without edge-edge cooperation. Then we design an efficient greedy algorithm for the second mode, considering both edge-cloud and edge-edge cooperations. Extensive simulation results show that for the first mode, the proposed greedy algorithm achieves near optimal performance for typical task topologies. On average, it outperforms the modified Hermes benchmark algorithm by about 23%~43.6% in terms of application finishing time with given budgets. By further exploiting collaborations among edge servers in the second cooperation mode, the proposed algorithm helps to achieve over 20.3% average performance gain on the application finishing time over the first mode under various scenarios. Real-world experiments comply with simulation results.

Original languageEnglish
JournalIEEE Transactions on Cloud Computing
DOIs
Publication statusE-pub ahead of print - 11 Nov 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Cooperation
  • Graph
  • Mobile Edge Computing
  • Offloading
  • Task Dependency

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