Evolutionary Algorithms to Optimize Task Scheduling Problem for the IoT Based Bag-of-Tasks Application in Cloud–Fog Computing Environment
Source abstract
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.
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Research context
OpenAlex topics: IoT and Edge/Fog Computing · IoT Networks and Protocols · Smart Parking Systems Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet Street, Hai Ba Trung District, Hanoi 100000, Vietnam
School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang avenue, Singapore 639798, Singapore
Faculty of Information Technology, University of Transport Technology, 54 Trieu Khuc Street, Thanh Xuan District, Hanoi 100000, Vietnam
References (26)
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