Edge Intelligence: Distributed Task Offloading and Service Management under Uncertainty

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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.
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
Publication statusPublished - Jun 2020

Publication series

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


  • Online learning
  • aggregate violation
  • dynamic regret
  • multi-access edge computing
  • projected dual gradient


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