Optimized Task Group Aggregation-Based Overflow Handling on Fog Computing Environment Using Neural Computing

Harwant Singh Arri, Ramandeep Singh, Sudan Jha, Deepak Prashar, Gyanendra Prasad Joshi and Ill Chul Doo
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Harwant Singh Arri: School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab 144411, India
Ramandeep Singh: School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab 144411, India
Sudan Jha: School of Computational Sciences, CHRIST (Deemed to be University), Mariam Nagar, Meerut Road, Delhi NCR, Ghaziabad 201003, India
Deepak Prashar: School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab 144411, India
Gyanendra Prasad Joshi: Department of Computer Science and Engineering, Sejong University, Gwangjin-gu, Seoul 05006, Korea
Ill Chul Doo: Artificial Intelligence Education, Hankuk University of Foreign Studies, Dongdaemun-gu, Seoul 02450, Korea

Mathematics, 2021, vol. 9, issue 19, 1-15

Abstract: It is a non-deterministic challenge on a fog computing network to schedule resources or jobs in a manner that increases device efficacy and throughput, diminishes reply period, and maintains the system well-adjusted. Using Machine Learning as a component of neural computing, we developed an improved Task Group Aggregation (TGA) overflow handling system for fog computing environments. As a result of TGA usage in conjunction with an Artificial Neural Network (ANN), we may assess the model’s QoS characteristics to detect an overloaded server and then move the model’s data to virtual machines (VMs). Overloaded and underloaded virtual machines will be balanced according to parameters, such as CPU, memory, and bandwidth to control fog computing overflow concerns with the help of ANN and the machine learning concept. Additionally, the Artificial Bee Colony (ABC) algorithm, which is a neural computing system, is employed as an optimization technique to separate the services and users depending on their individual qualities. The response time and success rate were both enhanced using the newly proposed optimized ANN-based TGA algorithm. Compared to the present work’s minimal reaction time, the total improvement in average success rate is about 3.6189 percent, and Resource Scheduling Efficiency has improved by 3.9832 percent. In terms of virtual machine efficiency for resource scheduling, average success rate, average task completion success rate, and virtual machine response time are improved. The proposed TGA-based overflow handling on a fog computing domain enhances response time compared to the current approaches. Fog computing, for example, demonstrates how artificial intelligence-based systems can be made more efficient.

Keywords: fog computing; resource scheduling; overflow handling; virtual machines; TGA; ABC; neural computing and ANN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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