An Online Fog Computing Task Offloading Algorithm Based on Robust Evolutionary Optimization

Chu Ge Wu*, Yuanqing Xia

*Corresponding author for this work

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

Abstract

With the rapid development of the Internet of Things (IoI), a large amount of raw data produced by IoT devices must be processed promptly. However, due to the limited resources of IoT devices, fog computing, and cloud computing platforms are adopted to handle the on-demand requirements through task offloading strategies. Collecting the status of all computing nodes consumes many resources, making it challenging to generate a globally optimal solution online. In this work, we propose a scheme that combines offline uncertain optimization and online scheduling to produce a high-performance online schedule based on the offline uncertain 'IoT-Fog-Cloud' task scheduling solutions. We define the offline uncertain task scheduling and online scheduling sub-problems and design an online strategy to optimize the overall utilities of tasks based on the existing fuzzy scheduler. The simulation results demonstrate that our algorithm performs better on task utilities and its robustness index compared to the heuristic method.

Original languageEnglish
Title of host publication2023 42nd Chinese Control Conference, CCC 2023
PublisherIEEE Computer Society
Pages1749-1754
Number of pages6
ISBN (Electronic)9789887581543
DOIs
Publication statusPublished - 2023
Event42nd Chinese Control Conference, CCC 2023 - Tianjin, China
Duration: 24 Jul 202326 Jul 2023

Publication series

NameChinese Control Conference, CCC
Volume2023-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference42nd Chinese Control Conference, CCC 2023
Country/TerritoryChina
CityTianjin
Period24/07/2326/07/23

Keywords

  • Evolutionary Computation
  • Fog Computing
  • Fuzzy Logic
  • Internet of Things
  • Online Scheduling
  • Resource Management

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