Bayesian Optimization for Online Management in Dynamic Mobile Edge Computing

Jia Yan, Qin Lu, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

Abstract

Recent years have witnessed the emergence of mobile edge computing (MEC), on the premise of a cost-effective enhancement in the computational ability of hardware-constrained wireless devices (WDs) comprising the Internet of Things (IoT). In a general multi-server multi-user MEC system, each WD has a computational task to execute and has to select binary (off)loading decisions, along with the analog-amplitude resource allocation variables in an online manner, with the goal of minimizing the overall energy-delay cost (EDC) with dynamic system states. While past works typically rely on the explicit expression of the EDC function, the present contribution considers a practical setting, where in lieu of system state information, the EDC function is not available in analytical form, and instead only the function values at queried points are revealed. Towards tackling such a challenging online combinatorial problem with only bandit information, novel Bayesian optimization (BO) based approaches are put forth by leveraging the multi-armed bandit (MAB) framework. Per time slot, the discrete offloading decisions are first obtained via the MAB method, and the analog resource allocation variables are subsequently optimized using the BO selection rule. By exploiting both temporal and contextual information, two novel BO approaches, termed time-varying BO and contextual time-varying BO, are developed. Numerical tests validate the merits of the proposed BO approaches compared with contemporary benchmarks under different MEC network sizes.

Original languageEnglish (US)
Pages (from-to)3425-3436
Number of pages12
JournalIEEE Transactions on Wireless Communications
Volume23
Issue number4
DOIs
StatePublished - Apr 1 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2002-2012 IEEE.

Keywords

  • Bayesian optimization
  • Internet of Things
  • Mobile edge computing
  • online learning
  • resource allocation
  • task offloading

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