Multi-Criteria Dynamic Service Migration for Ultra-Large-Scale Edge Computing Networks

Detalhes bibliográficos
Autor(a) principal: Chi, Hao Ran
Data de Publicação: 2023
Outros Autores: Silva, Rui, Santos, David, Quevedo, José, Corujo, Daniel, Abboud, Osama, Radwan, Ayman, Hecker, Artur, Aguiar, Rui L.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/39947
Resumo: Multiaccess edge computing (MEC) service migration is a technology whose key objective is to support ultralow-latency access to services. However, the complex ultralarge-scale edge service migration problem requires extensive research efforts, regarding the foreseen ultradensified edge nodes in 5G and beyond. In this article, we propose a novel dynamic service migration optimization architecture for ultralarge-scale MEC networks. We develop a new multicriteria decision-making algorithm: Technique for order of preference by similarity to ideal solution with attribute-based Niche count, named TOPANSIS, which showcases its strength to provide an optimal solution for service migration in large-scale deployments towards optimal data rate, latency, and load balancing. We further decentralize the operation of TOPANSIS to release the traffic burden from central datacenters by leveraging local decision making by edge nodes, while relying on central cloud coordination to account for the overall network information. Simulation results showcase that the proposed architecture outperforms the selected benchmarks with an average improvement of 39.41% for latency, 2.92% for data rate, as well as 10.53% and 6.26% for RAM and CPU load balancing, respectively. Moreover, the feasibility of the proposed solution is validated by means of a proof-of-concept implementation and experimental assessments.
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spelling Multi-Criteria Dynamic Service Migration for Ultra-Large-Scale Edge Computing NetworksEdge computingMulticriteria decision making (MCDM)Resource allocationService migrationMultiaccess edge computing (MEC) service migration is a technology whose key objective is to support ultralow-latency access to services. However, the complex ultralarge-scale edge service migration problem requires extensive research efforts, regarding the foreseen ultradensified edge nodes in 5G and beyond. In this article, we propose a novel dynamic service migration optimization architecture for ultralarge-scale MEC networks. We develop a new multicriteria decision-making algorithm: Technique for order of preference by similarity to ideal solution with attribute-based Niche count, named TOPANSIS, which showcases its strength to provide an optimal solution for service migration in large-scale deployments towards optimal data rate, latency, and load balancing. We further decentralize the operation of TOPANSIS to release the traffic burden from central datacenters by leveraging local decision making by edge nodes, while relying on central cloud coordination to account for the overall network information. Simulation results showcase that the proposed architecture outperforms the selected benchmarks with an average improvement of 39.41% for latency, 2.92% for data rate, as well as 10.53% and 6.26% for RAM and CPU load balancing, respectively. Moreover, the feasibility of the proposed solution is validated by means of a proof-of-concept implementation and experimental assessments.IEEE2026-01-01T00:00:00Z2023-11-01T00:00:00Z2023-11-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/39947eng1551-320310.1109/TII.2023.3244321Chi, Hao RanSilva, RuiSantos, DavidQuevedo, JoséCorujo, DanielAbboud, OsamaRadwan, AymanHecker, ArturAguiar, Rui L.info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T12:17:55Zoai:ria.ua.pt:10773/39947Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:09:57.215956Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Multi-Criteria Dynamic Service Migration for Ultra-Large-Scale Edge Computing Networks
title Multi-Criteria Dynamic Service Migration for Ultra-Large-Scale Edge Computing Networks
spellingShingle Multi-Criteria Dynamic Service Migration for Ultra-Large-Scale Edge Computing Networks
Chi, Hao Ran
Edge computing
Multicriteria decision making (MCDM)
Resource allocation
Service migration
title_short Multi-Criteria Dynamic Service Migration for Ultra-Large-Scale Edge Computing Networks
title_full Multi-Criteria Dynamic Service Migration for Ultra-Large-Scale Edge Computing Networks
title_fullStr Multi-Criteria Dynamic Service Migration for Ultra-Large-Scale Edge Computing Networks
title_full_unstemmed Multi-Criteria Dynamic Service Migration for Ultra-Large-Scale Edge Computing Networks
title_sort Multi-Criteria Dynamic Service Migration for Ultra-Large-Scale Edge Computing Networks
author Chi, Hao Ran
author_facet Chi, Hao Ran
Silva, Rui
Santos, David
Quevedo, José
Corujo, Daniel
Abboud, Osama
Radwan, Ayman
Hecker, Artur
Aguiar, Rui L.
author_role author
author2 Silva, Rui
Santos, David
Quevedo, José
Corujo, Daniel
Abboud, Osama
Radwan, Ayman
Hecker, Artur
Aguiar, Rui L.
author2_role author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Chi, Hao Ran
Silva, Rui
Santos, David
Quevedo, José
Corujo, Daniel
Abboud, Osama
Radwan, Ayman
Hecker, Artur
Aguiar, Rui L.
dc.subject.por.fl_str_mv Edge computing
Multicriteria decision making (MCDM)
Resource allocation
Service migration
topic Edge computing
Multicriteria decision making (MCDM)
Resource allocation
Service migration
description Multiaccess edge computing (MEC) service migration is a technology whose key objective is to support ultralow-latency access to services. However, the complex ultralarge-scale edge service migration problem requires extensive research efforts, regarding the foreseen ultradensified edge nodes in 5G and beyond. In this article, we propose a novel dynamic service migration optimization architecture for ultralarge-scale MEC networks. We develop a new multicriteria decision-making algorithm: Technique for order of preference by similarity to ideal solution with attribute-based Niche count, named TOPANSIS, which showcases its strength to provide an optimal solution for service migration in large-scale deployments towards optimal data rate, latency, and load balancing. We further decentralize the operation of TOPANSIS to release the traffic burden from central datacenters by leveraging local decision making by edge nodes, while relying on central cloud coordination to account for the overall network information. Simulation results showcase that the proposed architecture outperforms the selected benchmarks with an average improvement of 39.41% for latency, 2.92% for data rate, as well as 10.53% and 6.26% for RAM and CPU load balancing, respectively. Moreover, the feasibility of the proposed solution is validated by means of a proof-of-concept implementation and experimental assessments.
publishDate 2023
dc.date.none.fl_str_mv 2023-11-01T00:00:00Z
2023-11-01
2026-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/39947
url http://hdl.handle.net/10773/39947
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1551-3203
10.1109/TII.2023.3244321
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dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
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