Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments

Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments

Bibliographische Detailangaben
Parent link:Future Generation Computer Systems
Vol. 124.— 2021.— [P. 142-154]
1. Verfasser: Mokhamed Elsaed A. M. Akhmed Mokhamed
Körperschaft: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий
Weitere Verfasser: Abualigah L. Laith, Attiya I. Ibrahim
Zusammenfassung:Title screen
Cloud-fog computing frameworks are emerging paradigms developed to add benefits to the current Internet of Things (IoT) architectures. In such frameworks, task scheduling plays a key role, and the optimized schedule of IoT task requests can improve system performance and productivity. In this paper, we developed an alternative task scheduling technique for IoT requests in a cloud-fog environment based on a modified artificial ecosystem-based optimization (AEO), called AEOSSA. This modification is developed using the operators of the Salp Swarm Algorithm (SSA) in an attempt to enhance the exploitation ability of AEO during the process of finding the optimal solution for the problem under consideration. The performance of the designed AEOSSA approach to tackling the task scheduling problem is evaluated using different synthetic and real-world datasets of different sizes. In addition, a comparison is conducted between AEOSSA and other well-known metaheuristic methods for performance investigation. The experimental results demonstrate the high ability of AEOSSA to tackle the task scheduling problem and perform better than other methods according to the performance metrics such as makespan time and throughput.
Sprache:Englisch
Veröffentlicht: 2021
Schlagworte:
труды учёных ТПУ
электронный ресурс
Internet of things (IoT)
cloud computing
fog computing
task scheduling
makespan
artificial ecosystem-based optimization
salp
swarm
algorithm
интернет вещей
облачные вычисления
оптимизация
экосистемы
алгоритмы
Online-Zugang:https://doi.org/10.1016/j.future.2021.05.026
Format: Elektronisch Buchkapitel
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=665196

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