research-article

A Task Scheduling Algorithm Based on Classification Mining in Fog Computing Environment

Authors:

Yilin WuAuthors Info & Claims

Wireless Communications and Mobile Computing, Volume 2018

https://doi.org/10.1155/2018/2102348

Published: 01 January 2018 Publication History

Abstract

Fog computing (FC) is an emerging paradigm that extends computation, communication, and storage facilities towards the edge of a network. In this heterogeneous and distributed environment, resource allocation is very important. Hence, scheduling will be a challenge to increase productivity and allocate resources appropriately to the tasks. We schedule tasks in fog computing devices based on classification data mining technique. A key contribution is that a novel classification mining algorithm I-Apriori is proposed based on the Apriori algorithm. Another contribution is that we propose a novel task scheduling model and a TSFC (Task Scheduling in Fog Computing) algorithm based on the I-Apriori algorithm. Association rules generated by the I-Apriori algorithm are combined with the minimum completion time of every task in the task set. Furthermore, the task with the minimum completion time is selected to be executed at the fog node with the minimum completion time. We finally evaluate the performance of I-Apriori and TSFC algorithm through experimental simulations. The experimental results show that TSFC algorithm has better performance on reducing the total execution time of tasks and average waiting time.

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Cited By

Sabireen HVenkataraman N(2023)A Hybrid and Light Weight Metaheuristic Approach with Clustering for Multi-Objective Resource Scheduling and Application Placement in Fog EnvironmentExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119895223:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.119895
Robles-Enciso ASkarmeta A(2023)A multi-layer guided reinforcement learning-based tasks offloading in edge computingComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2022.109476220:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.comnet.2022.109476
Khan AAbbas AKhattak HRehman FDin IAli S(2022)Effective Task Scheduling in Critical Fog ApplicationsScientific Programming10.1155/2022/92080662022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9208066
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A Task Scheduling Algorithm Based on Classification Mining in Fog Computing Environment

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Published In

cover image Wireless Communications & Mobile Computing

Wireless Communications & Mobile Computing Volume 2018, Issue

2018

6447 pages

ISSN:1530-8669

Issue’s Table of Contents

Copyright © 2018 Lindong Liu et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 01 January 2018

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Cited By

Sabireen HVenkataraman N(2023)A Hybrid and Light Weight Metaheuristic Approach with Clustering for Multi-Objective Resource Scheduling and Application Placement in Fog EnvironmentExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119895223:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.119895
Robles-Enciso ASkarmeta A(2023)A multi-layer guided reinforcement learning-based tasks offloading in edge computingComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2022.109476220:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.comnet.2022.109476
Khan AAbbas AKhattak HRehman FDin IAli S(2022)Effective Task Scheduling in Critical Fog ApplicationsScientific Programming10.1155/2022/92080662022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9208066
Arshed JAhmed MMuhammad TAfzal MArif MBazezew B(2022)GA-IRACEWireless Communications & Mobile Computing10.1155/2022/63551922022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6355192
Jamil BIjaz HShojafar MMunir KBuyya R(2022)Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/351300254:11s(1-38)Online publication date: 9-Sep-2022
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Gupta SAgarwal G(2022)Hybrid fuzzy-based Deep Remora Reinforcement Learning Based Task Scheduling in Heterogeneous Multicore-processorMicroprocessors & Microsystems10.1016/j.micpro.2022.10454492:COnline publication date: 1-Jul-2022
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Jain VKumar B(2022)QoS-Aware Task Offloading in Fog Environment Using Multi-agent Deep Reinforcement LearningJournal of Network and Systems Management10.1007/s10922-022-09696-y31:1Online publication date: 11-Oct-2022
https://dl.acm.org/doi/10.1007/s10922-022-09696-y
Abohamama AEl-Ghamry AHamouda E(2022)Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog EnvironmentJournal of Network and Systems Management10.1007/s10922-022-09664-630:4Online publication date: 2-Jul-2022
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Rahimikhanghah ATajkey MRezazadeh BRahmani A(2022)Resource scheduling methods in cloud and fog computing environments: a systematic literature reviewCluster Computing10.1007/s10586-021-03467-125:2(911-945)Online publication date: 1-Apr-2022
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