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

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Publicado en:Wireless Communications & Mobile Computing (Online) vol. 2018 (2018)
Autor principal: Liu, Lindong
Otros Autores: Qi, Deyu, Zhou, Naqin, Wu, Yilin
Publicado:
John Wiley & Sons, Inc.
Materias:
Research
Standard deviation
Text categorization
Task scheduling
Datasets
Performance evaluation
Classification
Electronic devices
Resource allocation
Internet of Things
Researchers
Traffic congestion
Computer simulation
Distributed processing
Scheduling
Big Data
Data mining
Cloud computing
Algorithms
Storage facilities
Completion time
Schedules
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1530-8677
1530-8669
DOI:10.1155/2018/2102348
Fuente:Advanced Technologies & Aerospace Database