Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment

In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device net...

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Main Authors: Anh, Tran The, Binh, Huynh Thi Thanh, Son, Do Bao, Nguyen, Binh Minh
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2019
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Online Access:https://hdl.handle.net/10356/87792
http://hdl.handle.net/10220/49304
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-877922020-03-07T11:48:59Z Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment Anh, Tran The Binh, Huynh Thi Thanh Son, Do Bao Nguyen, Binh Minh School of Computer Science and Engineering Engineering::Computer science and engineering Edge Computing Task Scheduling In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device network of IoT. One of the obstacles of Fog Computing is distribution of computing resources to minimize completion time and operating cost. The following study introduces a new approach to optimize task scheduling problem for Bag-of-Tasks applications in Cloud–Fog environment in terms of execution time and operating costs. The proposed algorithm named TCaS was tested on 11 datasets varying in size. The experimental results show an improvement of 15.11% compared to the Bee Life Algorithm (BLA) and 11.04% compared to Modified Particle Swarm Optimization (MPSO), while achieving balance between completing time and operating cost. Published version 2019-07-11T08:56:34Z 2019-12-06T16:49:33Z 2019-07-11T08:56:34Z 2019-12-06T16:49:33Z 2019 Journal Article Nguyen, B. M., Binh, H. T. T., Anh, T. T., & Sun, D. B. (2019). Evolutionary Algorithms to Optimize Task Scheduling Problem for the IoT Based Bag-of-Tasks Application in Cloud–Fog Computing Environment. Applied Sciences, 9(9), 1730-. doi:10.3390/app9091730 2076-3417 https://hdl.handle.net/10356/87792 http://hdl.handle.net/10220/49304 10.3390/app9091730 en Applied Sciences © 2019 by the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 20 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Edge Computing
Task Scheduling
spellingShingle Engineering::Computer science and engineering
Edge Computing
Task Scheduling
Anh, Tran The
Binh, Huynh Thi Thanh
Son, Do Bao
Nguyen, Binh Minh
Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment
description In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device network of IoT. One of the obstacles of Fog Computing is distribution of computing resources to minimize completion time and operating cost. The following study introduces a new approach to optimize task scheduling problem for Bag-of-Tasks applications in Cloud–Fog environment in terms of execution time and operating costs. The proposed algorithm named TCaS was tested on 11 datasets varying in size. The experimental results show an improvement of 15.11% compared to the Bee Life Algorithm (BLA) and 11.04% compared to Modified Particle Swarm Optimization (MPSO), while achieving balance between completing time and operating cost.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Anh, Tran The
Binh, Huynh Thi Thanh
Son, Do Bao
Nguyen, Binh Minh
format Article
author Anh, Tran The
Binh, Huynh Thi Thanh
Son, Do Bao
Nguyen, Binh Minh
author_sort Anh, Tran The
title Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment
title_short Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment
title_full Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment
title_fullStr Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment
title_full_unstemmed Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment
title_sort evolutionary algorithms to optimize task scheduling problem for the iot based bag-of-tasks application in cloud–fog computing environment
publishDate 2019
url https://hdl.handle.net/10356/87792
http://hdl.handle.net/10220/49304
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