Learning driven computation offloading for asymmetrically informed edge computing

Miao Hu, Lei Zhuang, Di Wu*, Yipeng Zhou, Xu Chen, Liang Xiao

*Corresponding author for this work

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Edge computing emerges as a promising paradigm to decentralize computation power to the edge of the network and thus improve user experience by task offloading. A user can perfectly schedule his tasks to be executed on edge servers if the execution time of all tasks can be known beforehand. However, it is difficult to know the task execution time (TET) before performing actual offloading, which normally varies on edge servers with different software and hardware configurations. Moreover, such configuration information is not always available to end users due to security concerns. In this paper, we first propose a learning-driven algorithm to accurately predict TETs of all tasks in such an asymmetrically informed edge computing environment. The basic idea is to predict unknown TETs using only a small sampled set of TETs by exploiting the underlying correlation between TETs and edge server configurations. Next, we formulate the problem of task offloading into a constrained optimization problem, which is unfortunately proved to be NP-hard. To address the above challenge, we design a task offloading algorithm, called Maximum Efficiency First Ordered (MEFO), to achieve near-optimal efficiency. Field measurements and experiments have been conducted to demonstrate that our proposed learning-driven algorithm can predict TETs more accurately than other algorithms as long as the fraction of sampled TETs is larger than a small predefined threshold, and our proposed MEFO algorithm achieves a much higher success rate of task offloading and a shorter processing delay with very limited information of edge servers.

Original languageEnglish
Pages (from-to)1802-1815
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Volume30
Issue number8
DOIs
Publication statusPublished - Aug 2019

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Keywords

  • Asymmetric information
  • edge computing
  • low rank learning
  • task execution time (TET)
  • task offloading

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