Delay-Aware Service Caching in Edge Cloud: a Adversarial Semi-Bandits Learning-Based Approach

EasyChair Preprint 12531, version 3

Abstract

Mobile Edge Computing (MEC) is an emerging computing paradigm that offloads cloud center functions to the edge server. In a MEC environment, edge servers’ limited storage and processing capacity require selective service caching, where only a part of required content can be placed directly upon the destination edge server and the remaining at remote cloud end. A primary challenge in this context is the creation of an effective and responsive service caching algorithm that improves the Quality of Service (QoS) perceived by users while reducing operational costs. This study applies an M/G/1 queuing model as the foundational framework and transforms the service caching problem as an adversarial semi-bandit problem. We propose a delay-aware Genetic-Follow-the-Regularized-Leader (GFRL) algorithm, which is capable of guiding decentralized caching decisions. Experimental results indicate that GFRL outperforms traditional methods across various performance metrics.

Keyphrases: Genetic Algorithm, Mobile Edge Computing, Service caching, adversarial semi-bandits, queuing theory

@booklet{EasyChair:12531,
  author    = {Li and Xia and Sun and Chen and Li},
  title     = {Delay-Aware Service Caching in Edge Cloud: a Adversarial Semi-Bandits Learning-Based Approach},
  howpublished = {EasyChair Preprint 12531},
  year      = {EasyChair, 2024}}