Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Optimized task group aggregation-based overflow handling on fog computing environment using neural computing Subject The topic of the resource ABC; Fog computing; Neural computing and ANN; Overflow handling; Resource scheduling; TGA; Virtual machines Description An account of the resource 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 models QoS characteristics to detect an overloaded server and then move the models 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 works 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. 2021 by the authors. Licensee MDPI, Basel, Switzerland. Creator An entity primarily responsible for making the resource Arri H.S.; Khosa R.S.; Jha S.; Prashar D.; Joshi G.P.; Doo I.C. Source A related resource from which the described resource is derived Mathematics, Vol-9, No. 19 Publisher An entity responsible for making the resource available MDPI Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3390/math9192522" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/math9192522</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116607183&amp;doi=10.3390%2Fmath9192522&amp;partnerID=40&amp;md5=d392508c32f799bad2ed9bb8ef9f6a09" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116607183&amp;doi=10.3390%2fmath9192522&amp;partnerID=40&amp;md5=d392508c32f799bad2ed9bb8ef9f6a09</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 22277390 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Arri H.S., School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India; Khosa R.S., School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India; Jha S., School of Computational Sciences, CHRIST (Deemed to be University), Mariam Nagar, Meerut Road, Delhi NCR, Ghaziabad, 201003, India; Prashar D., School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India; Joshi G.P., Department of Computer Science and Engineering, Sejong University, Gwangjin-gu, Seoul, 05006, South Korea; Doo I.C., Artificial Intelligence Education, Hankuk University of Foreign Studies, Dongdaemun-gu, Seoul, 02450, South Korea