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MO-BMB for Multi-objective Task Offloading Optimization in Fog-Cloud Environment

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Rachel Roux
Sonia Yassa
Olivier Romain
Published in
Proceedings of Tenth International Congress on Information and Communication Technology
Publisher
Springer Nature Singapore

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Abstract

This chapter delves into the challenges of task offloading in fog-cloud environments, driven by the exponential growth of real-time data streams from IoT, autonomous vehicles, and smart cities. It introduces a multi-objective optimization approach using an adapted Monarch Butterfly Algorithm to minimize both cost and delay. The chapter compares two population selection methods, crowding-distance and random, and evaluates their performance against state-of-the-art algorithms like Modified Particle Swarm Optimization and Bee Life Algorithm. The results demonstrate significant improvements in cost optimization and scalability, particularly for large instances. The chapter also discusses the potential enhancements for the algorithm, such as incorporating an adapted Lévy flight algorithm and combining random and crowding-distance techniques. The findings highlight the effectiveness of the proposed approach in providing a set of Pareto-optimal solutions, offering users more flexibility in decision-making. The chapter concludes with prospects for further research and improvements in the algorithm.
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Title
MO-BMB for Multi-objective Task Offloading Optimization in Fog-Cloud Environment
Authors
Rachel Roux
Sonia Yassa
Olivier Romain
Copyright Year
2025
Publisher
Springer Nature Singapore
Book
Proceedings of Tenth International Congress on Information and Communication Technology

Print ISBN: 978-981-9669-28-8

Electronic ISBN: 978-981-9669-29-5

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-981-96-6929-5_40
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