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Edge Intelligence: Distributed Task Offloading and Service Management under Uncertainty

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers ( IEEE )
Number of pages6
ISBN (Electronic)9781728150895
PublishedJun 2020

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


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    main.pdf, 1.05 MB, application/pdf

    Uploaded date:13 Mar 2020

    Version:Accepted author manuscript

King's Authors


This work deals with the task offloading problem for multiple cellular edge devices in a multi-access edge computing (MEC) infrastructure attached to a base-station (BS). In order to minimize the overall task computing-communication delay through coping with time-varying cost and constraint functions with unknown statistics on-the-go, we propose a novel distributed bandit optimization (DBO) algorithm which runs based on the projected dual gradient iterations and a single broadcast communicating the MEC states to the SDs at the end of each time-slot. To track the performance of the proposed online learning algorithm over time, we define a dynamic regret to assess the closeness of the underlying delay cost of the DBO to a clairvoyant dynamic optimum and an aggregate violation metric to evaluate the asymptotic satisfaction of the constraints. We derive lower and upper bounds for dynamic regret as well as an upper-bound for the aggregate violation and show that the upper- bounds are sub-linear under sub-linear accumulated hindsight variations. The simulation results and comparisons confirm the effectiveness of the proposed algorithm in the long run.

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