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Edge Computing in Centralized Data Server Deployment for Network Qos and Latency Improvement for Virtualization Environment

Author(s)
Ashok Kumar Yadav School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India
Bhanu Sharma Department of Computer Science Engineering, Chandigarh Engineering College, Jhanjeri, India
Akash Kumar Bhagat Department of Computer Science, ARKA JAIN University, Jamshedpur, Jharkhand, India
Harshal Shah Department of Computer Science and Engineering, Parul Institute of Technology, Parul University, Vadodara, Gujarat, India
Manjunath C R Department of Computer Science and Engineering, Jain (Deemed-to-be University), Bangalore, India
Aishwary Awasthi Department of Mechanical, Sanskriti University, Mathura, Uttar Pradesh, India
Abstract
With the advancement of Internet of Things (IoT), the network devices seem to be raising, and the cloud data centre load also raises; certain delay-sensitive services are not responded to promptly which leads to a reduced quality of service (QoS). The technique of resource estimation could offer the appropriate source for users through analyses of load of resource itself. Thus, the prediction of resource QoS was important to user fulfillment and task allotment in edge computing. This study develops a new manta ray foraging optimization with backpropagation neural network (MRFO-BPNN) model for resource estimation using quality of service (QoS) in the edge computing platform. Primarily, the MRFO-BPNN model makes use of BPNN algorithm for the estimation of resources in edge computing. Besides, the parameters relevant to the BPNN model are adjusted effectually by the use of MRFO algorithm. Moreover, an objective function is derived for the MRFO algorithm for the investigation of load state changes and choosing proper ones. To facilitate the enhanced performance of the MRFO-BPNN model, a widespread experimental analysis is made. The comprehensive comparison study highlighted the excellency of the MRFO-BPNN model.
Publication Details
Page(s) 214-225
DOI DOI not available
Published Journal: International Journal of Communication Networks and Information Security, Volume: 14, Issue: 3, Year: 2022

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Keywords
Edge Computing, machine learning, Quality of Service, Virtualization, Resource estimation
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