Energy-efficient workload allocation in fog-cloud based services of intelligent transportation systems using a learning classifier system

Mahdi Abbasi*, Mina Yaghoobikia, Milad Rafiee, Alireza Jolfaei, Mohammad R. Khosravi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Nowadays, renewable energies have been considered as one of the important sources of energy supply in delay-sensitive fog computations in intelligent transportation systems due to their cheapness and availability. This study addresses the challenges of using renewable power supplies in delay-sensitive fogs and proposes an efficient workload allocation method based on a learning classifier system. The system dynamically learns the workload allocation policies between the cloud and the fog servers and then converges on the optimal allocation that fulfils the energy and delay requirements in the overall transportation system. Simulation results confirm that the proposed algorithm reduces the long-term costs of the system including service delay and operating costs. Also, compared to some other techniques, when the proposed method presents the most successful solution for reducing the average delay of the workloads and converging on the minimum value as well as retaining or even increasing the battery levels of fog nodes up to 100%. The lowest cost of the delay is 5 among other available methods, whereas in the proposed method, this value approaches 4.5.

Original languageEnglish
Pages (from-to)1484-1490
Number of pages7
JournalIET Intelligent Transport Systems
Volume14
Issue number11
DOIs
Publication statusPublished - Nov 2020

Bibliographical note

Publisher Copyright:
© The Institution of Engineering and Technology 2020

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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