Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87792
Title: Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment
Authors: Anh, Tran The
Binh, Huynh Thi Thanh
Son, Do Bao
Nguyen, Binh Minh
Keywords: Engineering::Computer science and engineering
Edge Computing
Task Scheduling
Issue Date: 2019
Source: 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
Series/Report no.: Applied Sciences
Abstract: 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.
URI: https://hdl.handle.net/10356/87792
http://hdl.handle.net/10220/49304
ISSN: 2076-3417
DOI: http://dx.doi.org/10.3390/app9091730
Rights: © 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/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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