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Improving application support in 6G networks with CAPOM: Confluence-aided process organization method

Systems requiring terahertz transmission and high sampling capabilities can be supported by sixth-generation (6G) technology with minimal latency and excellent service throughput. Regardless of the distributions of data and services, High-Performance Computing (HPC) enhances speed and provides diver...

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Main Authors: Jamil Alsayaydeh, Jamil Abedalrahim, Al Gburi, Ahmed Jamal Abdullah, Irianto, Herawan, Safarudin Gazali
Format: Article
Language:English
Published: Institute Of Electrical And Electronics Engineers Inc. 2023
Online Access:http://eprints.utem.edu.my/id/eprint/27112/2/0270213092023353.PDF
http://eprints.utem.edu.my/id/eprint/27112/
https://ieeexplore.ieee.org/document/10235956
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spelling my.utem.eprints.271122024-06-19T14:58:01Z http://eprints.utem.edu.my/id/eprint/27112/ Improving application support in 6G networks with CAPOM: Confluence-aided process organization method Jamil Alsayaydeh, Jamil Abedalrahim Al Gburi, Ahmed Jamal Abdullah Irianto Herawan, Safarudin Gazali Systems requiring terahertz transmission and high sampling capabilities can be supported by sixth-generation (6G) technology with minimal latency and excellent service throughput. Regardless of the distributions of data and services, High-Performance Computing (HPC) enhances speed and provides diversified applications and functionality. The Confluence-Aided Process Organization Method (CAPOM) is suggested in this article to take advantage of process allocations while using an HPC paradigm. The process allocations and completions are scheduled based on prior and current system conditions to minimize waiting time based on the 6G qualities. This implies that state requirements for process allocation, distribution, and completion are carried out with the assistance of federated learning. The initial state allocations are based on the user/application request; in other allocations, the application's request for completion time and capacity for processing are considered. Offloading and shared processing are therefore combined to maximize resource deliveries. The federated learning states are checked post-completion times to mitigate the waiting duration of dense service demands. Indicators such as distribution ratios, latency, wait time, and processing rate are considered for the effectiveness of the proofs. The suggested CAPOM achieves an 8.67% higher processing rate, 9.09% reduced latency, 8.76% less wait time, and a 6.73% higher distribution ratio for the various capacities. Institute Of Electrical And Electronics Engineers Inc. 2023-08 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27112/2/0270213092023353.PDF Jamil Alsayaydeh, Jamil Abedalrahim and Al Gburi, Ahmed Jamal Abdullah and Irianto and Herawan, Safarudin Gazali (2023) Improving application support in 6G networks with CAPOM: Confluence-aided process organization method. IEEE Access, 11. pp. 99426-99437. ISSN 2169-3536 https://ieeexplore.ieee.org/document/10235956 10.1109/ACCESS.2023.3310808
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Systems requiring terahertz transmission and high sampling capabilities can be supported by sixth-generation (6G) technology with minimal latency and excellent service throughput. Regardless of the distributions of data and services, High-Performance Computing (HPC) enhances speed and provides diversified applications and functionality. The Confluence-Aided Process Organization Method (CAPOM) is suggested in this article to take advantage of process allocations while using an HPC paradigm. The process allocations and completions are scheduled based on prior and current system conditions to minimize waiting time based on the 6G qualities. This implies that state requirements for process allocation, distribution, and completion are carried out with the assistance of federated learning. The initial state allocations are based on the user/application request; in other allocations, the application's request for completion time and capacity for processing are considered. Offloading and shared processing are therefore combined to maximize resource deliveries. The federated learning states are checked post-completion times to mitigate the waiting duration of dense service demands. Indicators such as distribution ratios, latency, wait time, and processing rate are considered for the effectiveness of the proofs. The suggested CAPOM achieves an 8.67% higher processing rate, 9.09% reduced latency, 8.76% less wait time, and a 6.73% higher distribution ratio for the various capacities.
format Article
author Jamil Alsayaydeh, Jamil Abedalrahim
Al Gburi, Ahmed Jamal Abdullah
Irianto
Herawan, Safarudin Gazali
spellingShingle Jamil Alsayaydeh, Jamil Abedalrahim
Al Gburi, Ahmed Jamal Abdullah
Irianto
Herawan, Safarudin Gazali
Improving application support in 6G networks with CAPOM: Confluence-aided process organization method
author_facet Jamil Alsayaydeh, Jamil Abedalrahim
Al Gburi, Ahmed Jamal Abdullah
Irianto
Herawan, Safarudin Gazali
author_sort Jamil Alsayaydeh, Jamil Abedalrahim
title Improving application support in 6G networks with CAPOM: Confluence-aided process organization method
title_short Improving application support in 6G networks with CAPOM: Confluence-aided process organization method
title_full Improving application support in 6G networks with CAPOM: Confluence-aided process organization method
title_fullStr Improving application support in 6G networks with CAPOM: Confluence-aided process organization method
title_full_unstemmed Improving application support in 6G networks with CAPOM: Confluence-aided process organization method
title_sort improving application support in 6g networks with capom: confluence-aided process organization method
publisher Institute Of Electrical And Electronics Engineers Inc.
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/27112/2/0270213092023353.PDF
http://eprints.utem.edu.my/id/eprint/27112/
https://ieeexplore.ieee.org/document/10235956
_version_ 1802981603872342016
score 13.211869

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