Traffic engineering in data center networks : prediction and scheduling via randomized rounding for elephant flows
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFPE |
dARK ID: | ark:/64986/0013000015w0d |
Texto Completo: | https://repositorio.ufpe.br/handle/123456789/40068 |
Resumo: | Applications and services hosted in large Data Centers account for most of the increase in Internet traffic. Data Center Networks (DCNs) are often designed with a fat-tree topology, allowing multiple paths between any two servers. The most widely adopted solution for flow routing in DCNs is equal-cost multipath (ECMP), which can cause link performance degra dation due to the possible occurrence of hash collisions in the presence of flows with many gigabytes of data, called elephants. Such collisions can result in packet discard, which generates packet retransmission, causes additional latency, and further degrades link performance. This thesis proposes a hybrid prediction model by combining aspects of the FARIMA and the Recur rent Neural Network (FARIMA-RNN) models to predict elephant flows on a short-term basis. Besides, we implement an SDN solution based on a randomized rounding heuristic, named RDRH, to schedule elephant flows in DCNs. We employ a linear programming formulation that provides in polynomial time lower bounds for balancing elephant flows. A methodology based on the rank of the prediction accuracy metrics is applied to compare the hybrid model’s performance with the ARIMA, GARCH, RBF, MLP, and LSTM models. Results show that the FARIMA-RNN model presents lower error rates than the other predictors. Furthermore, we evaluate our proposed heuristic performance on an emulated network with Mininet. The ex periments show that the RDRH solution presents a performance gain compared to the ECMP and Hedera solutions in the round-trip delay and loss metrics in two of the four evaluated scenarios. |
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BEZERRA, Jeandro de Mesquitahttp://lattes.cnpq.br/8443091736542183http://lattes.cnpq.br/9838400375894439CAMPELO, Divanilson Rodrigo de Sousa2021-05-13T17:06:47Z2021-05-13T17:06:47Z2020-11-25BEZERRA, Jeandro de Mesquita. Traffic engineering in data center networks: prediction and scheduling via randomized rounding for elephant flows. 2020. Tese (Doutorado em Ciências da Computação) - Universidade Federal de Pernambuco, Recife, 2020.https://repositorio.ufpe.br/handle/123456789/40068ark:/64986/0013000015w0dApplications and services hosted in large Data Centers account for most of the increase in Internet traffic. Data Center Networks (DCNs) are often designed with a fat-tree topology, allowing multiple paths between any two servers. The most widely adopted solution for flow routing in DCNs is equal-cost multipath (ECMP), which can cause link performance degra dation due to the possible occurrence of hash collisions in the presence of flows with many gigabytes of data, called elephants. Such collisions can result in packet discard, which generates packet retransmission, causes additional latency, and further degrades link performance. This thesis proposes a hybrid prediction model by combining aspects of the FARIMA and the Recur rent Neural Network (FARIMA-RNN) models to predict elephant flows on a short-term basis. Besides, we implement an SDN solution based on a randomized rounding heuristic, named RDRH, to schedule elephant flows in DCNs. We employ a linear programming formulation that provides in polynomial time lower bounds for balancing elephant flows. A methodology based on the rank of the prediction accuracy metrics is applied to compare the hybrid model’s performance with the ARIMA, GARCH, RBF, MLP, and LSTM models. Results show that the FARIMA-RNN model presents lower error rates than the other predictors. Furthermore, we evaluate our proposed heuristic performance on an emulated network with Mininet. The ex periments show that the RDRH solution presents a performance gain compared to the ECMP and Hedera solutions in the round-trip delay and loss metrics in two of the four evaluated scenarios.FUNCAPAplicações e serviços hospedados em grandes data centers ocasionam um aumento no volume de tráfego da Internet. Redes de data center (DCNs) são frequentemente projetadas com a topologia fat-tree que permitem múltiplos caminhos entre quaisquer dois servidores. A solução mais adotada para roteamento de fluxos em DCNs é o ECMP (equal-cost multipath), que pode causar degradação do desempenho no enlace devido à possibilidade de ocorrência de colisões de hash na presença de fluxos com muitos gigabytes, chamados de elefante. Tais colisões podem ocasionar descarte de pacotes que geram retransmissões causando atrasos adicionais e degradam o desempenho do enlace. Esta tese propõe um modelo híbrido de predição combinando aspectos dos modelos FARIMA e de Redes Neurais Recorrentes, chamado de (FARIMA-RNN), para prever fluxos elefante em curto período. Além disso, implementamos uma solução SDN baseada em uma heurística de arredondamento probabilístico, denominada de RDRH, para escalonar fluxos elefante em DCNs. Uma metodologia baseada no rank da métrica de acurácia de previsão é aplicada para comparar o desempenho do modelo híbrido com os modelos ARIMA, GARCH, RBF, MLP e LSTM. Os resultados mostram que o modelo FARIMA-RNN apresenta taxas de erro menores que os demais modelos. A heurística proposta foi avaliada em uma DCN emulada com o Mininet. Os experimentos mostram que a solução RDRH apresenta ganho de desempenho comparado com as soluções ECMP e Hedera nas métricas atraso de ida e volta e perda em dois dos quatro cenários avaliadosengUniversidade Federal de PernambucoPrograma de Pos Graduacao em Ciencia da ComputacaoUFPEBrasilAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessRedes de computadoresAvaliação de desempenhoTraffic engineering in data center networks : prediction and scheduling via randomized rounding for elephant flowsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisdoutoradoreponame:Repositório Institucional da UFPEinstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPETEXTTESE Jeandro de Mesquita Bezerra.pdf.txtTESE Jeandro de Mesquita Bezerra.pdf.txtExtracted texttext/plain194787https://repositorio.ufpe.br/bitstream/123456789/40068/4/TESE%20Jeandro%20de%20Mesquita%20Bezerra.pdf.txt6f3a69ede3adebc1da5f74ff87473eb1MD54THUMBNAILTESE Jeandro de Mesquita Bezerra.pdf.jpgTESE Jeandro de Mesquita Bezerra.pdf.jpgGenerated Thumbnailimage/jpeg1214https://repositorio.ufpe.br/bitstream/123456789/40068/5/TESE%20Jeandro%20de%20Mesquita%20Bezerra.pdf.jpgd75dc33e8396cd82be6adeabdbaa6eb8MD55CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.pt_BR.fl_str_mv |
Traffic engineering in data center networks : prediction and scheduling via randomized rounding for elephant flows |
title |
Traffic engineering in data center networks : prediction and scheduling via randomized rounding for elephant flows |
spellingShingle |
Traffic engineering in data center networks : prediction and scheduling via randomized rounding for elephant flows BEZERRA, Jeandro de Mesquita Redes de computadores Avaliação de desempenho |
title_short |
Traffic engineering in data center networks : prediction and scheduling via randomized rounding for elephant flows |
title_full |
Traffic engineering in data center networks : prediction and scheduling via randomized rounding for elephant flows |
title_fullStr |
Traffic engineering in data center networks : prediction and scheduling via randomized rounding for elephant flows |
title_full_unstemmed |
Traffic engineering in data center networks : prediction and scheduling via randomized rounding for elephant flows |
title_sort |
Traffic engineering in data center networks : prediction and scheduling via randomized rounding for elephant flows |
author |
BEZERRA, Jeandro de Mesquita |
author_facet |
BEZERRA, Jeandro de Mesquita |
author_role |
author |
dc.contributor.authorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/8443091736542183 |
dc.contributor.advisorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/9838400375894439 |
dc.contributor.author.fl_str_mv |
BEZERRA, Jeandro de Mesquita |
dc.contributor.advisor1.fl_str_mv |
CAMPELO, Divanilson Rodrigo de Sousa |
contributor_str_mv |
CAMPELO, Divanilson Rodrigo de Sousa |
dc.subject.por.fl_str_mv |
Redes de computadores Avaliação de desempenho |
topic |
Redes de computadores Avaliação de desempenho |
description |
Applications and services hosted in large Data Centers account for most of the increase in Internet traffic. Data Center Networks (DCNs) are often designed with a fat-tree topology, allowing multiple paths between any two servers. The most widely adopted solution for flow routing in DCNs is equal-cost multipath (ECMP), which can cause link performance degra dation due to the possible occurrence of hash collisions in the presence of flows with many gigabytes of data, called elephants. Such collisions can result in packet discard, which generates packet retransmission, causes additional latency, and further degrades link performance. This thesis proposes a hybrid prediction model by combining aspects of the FARIMA and the Recur rent Neural Network (FARIMA-RNN) models to predict elephant flows on a short-term basis. Besides, we implement an SDN solution based on a randomized rounding heuristic, named RDRH, to schedule elephant flows in DCNs. We employ a linear programming formulation that provides in polynomial time lower bounds for balancing elephant flows. A methodology based on the rank of the prediction accuracy metrics is applied to compare the hybrid model’s performance with the ARIMA, GARCH, RBF, MLP, and LSTM models. Results show that the FARIMA-RNN model presents lower error rates than the other predictors. Furthermore, we evaluate our proposed heuristic performance on an emulated network with Mininet. The ex periments show that the RDRH solution presents a performance gain compared to the ECMP and Hedera solutions in the round-trip delay and loss metrics in two of the four evaluated scenarios. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020-11-25 |
dc.date.accessioned.fl_str_mv |
2021-05-13T17:06:47Z |
dc.date.available.fl_str_mv |
2021-05-13T17:06:47Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
BEZERRA, Jeandro de Mesquita. Traffic engineering in data center networks: prediction and scheduling via randomized rounding for elephant flows. 2020. Tese (Doutorado em Ciências da Computação) - Universidade Federal de Pernambuco, Recife, 2020. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufpe.br/handle/123456789/40068 |
dc.identifier.dark.fl_str_mv |
ark:/64986/0013000015w0d |
identifier_str_mv |
BEZERRA, Jeandro de Mesquita. Traffic engineering in data center networks: prediction and scheduling via randomized rounding for elephant flows. 2020. Tese (Doutorado em Ciências da Computação) - Universidade Federal de Pernambuco, Recife, 2020. ark:/64986/0013000015w0d |
url |
https://repositorio.ufpe.br/handle/123456789/40068 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
dc.publisher.program.fl_str_mv |
Programa de Pos Graduacao em Ciencia da Computacao |
dc.publisher.initials.fl_str_mv |
UFPE |
dc.publisher.country.fl_str_mv |
Brasil |
publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
dc.source.none.fl_str_mv |
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Repositório Institucional da UFPE |
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