Optimizing Fog Computing Task Scheduling: Selection and Superiority of Differential Evolution Algorithm

Bency, Joan (2023) Optimizing Fog Computing Task Scheduling: Selection and Superiority of Differential Evolution Algorithm. Masters thesis, Dublin, National College of Ireland.

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Abstract

The increase in the number of Internet of Things (IoT) devices raises the amount of data generated every day. Cloud computing provides storage, processing, and analytical capabilities to handle such huge amounts of data. Furthermore, some applications cannot handle the elevated latency and consumption of bandwidth. To solve this, the fog computing paradigm puts cloud services nearer to the network edge. Yet, because fog resources are varied, resource-constrained, and distributed, efficient task scheduling becomes crucial for increasing performance. Response time to application requests, as well as bandwidth and CPU usage, can be decreased with an efficient job scheduling algorithm. This work describes a thorough investigation into task scheduling in fog computing using meta-heuristic techniques. Many evolutionary and swarm-based methodologies were explored and simulated by combining them with the PureEdgeSim simulator, which realistically simulates cloud-fog situations and differential evolution(specifically SADE) emerged as the best option due to its superior performance. Compared to the Bees Algorithm, SADE showed a decrease of 87.9% in failed tasks and compared to BaseGA, it showed a decrease of 15% in execution delay. Also compared to OriginalPSO, SADE exhibited a 5.4% decrease in average bandwidth and a 23.2% decrease in average CPU usage. This research advances the field of efficient fog computing optimisation while emphasising the importance of method selection in tackling real-world difficulties.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Gupta, Punit UNSPECIFIED
Subjects:	T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Tamara Malone
Date Deposited:	10 Aug 2024 13:25
Last Modified:	10 Aug 2024 13:25
URI:	https://norma.ncirl.ie/id/eprint/7044

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