Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment

IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure on the resources of traditional cloud data centers. This prevents cloud data centers from fulfilling the requirements of IoT applications, particul...

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Vydáno v:Journal of network and systems management Ročník 30; číslo 4
Hlavní autoři: Abohamama, A. S., El-Ghamry, Amir, Hamouda, Eslam
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.10.2022
Springer Nature B.V
Témata:
Algorithms
Application
Cloud computing
Communications Engineering
Computer centers
Computer Communication Networks
Computer Science
Computer Systems Organization and Communication Networks
Data centers
Data quality
Delay time
Electronic devices
Failure rates
Genetic algorithms
Genetics
Information Systems and Communication Service
Internet of Things
Networks
Operations Research/Decision Theory
Optimization
Paradigms
Permutations
Quality of life
Real time
Resources
Run time (computers)
Scheduling
Task scheduling
Time
Virtual environments
Fog computing
Cloud–fog system
Bag-of-tasks applications
IoT applications
Scheduling optimization
ISSN:1064-7570, 1573-7705
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Shrnutí:IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure on the resources of traditional cloud data centers. This prevents cloud data centers from fulfilling the requirements of IoT applications, particularly delay-sensitive applications. Fog computing is a relatively recent computing paradigm that extends cloud resources to the edge of the network. However, task scheduling in this computing paradigm is still a challenge. In this study, a semidynamic real-time task scheduling algorithm is proposed for bag-of-tasks applications in the cloud–fog environment. The proposed scheduling algorithm formulates task scheduling as a permutation-based optimization problem. A modified version of the genetic algorithm is used to provide different permutations for arrived tasks at each scheduling round. Then, the tasks are assigned, in the order defined by the best permutation, to a virtual machine, which has sufficient resources and achieves the minimum expected execution time. A conducted optimality study reveals that the proposed algorithm has a comparative performance with respect to the optimal solution. Additionally, the proposed algorithm is compared with first fit, best fit, the genetic algorithm, and the bees life algorithm in terms of makespan, total execution time, failure rate, average delay time, and elapsed run time. The experimental results show the superiority of the proposed algorithm over the other algorithms. Moreover, the proposed algorithm achieves a good balance between the makespan and the total execution cost and minimizes the task failure rate compared to the other algorithms. Graphical Abstract
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ISSN:1064-7570
1573-7705
DOI:10.1007/s10922-022-09664-6