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Extending Stochastic Network Calculus to Loss Analysis

Loss is an important parameter of Quality of Service (QoS). Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss...

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Detalles Bibliográficos
Autores principales: Luo, Chao, Yu, Li, Zheng, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3817637/
https://www.ncbi.nlm.nih.gov/pubmed/24228019
http://dx.doi.org/10.1155/2013/918565
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author Luo, Chao
Yu, Li
Zheng, Jun
author_facet Luo, Chao
Yu, Li
Zheng, Jun
author_sort Luo, Chao
collection PubMed
description Loss is an important parameter of Quality of Service (QoS). Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss by deterministic network calculus, but there are few results to extend stochastic network calculus for loss analysis. In this paper, we introduce a new parameter named loss factor into stochastic network calculus and then derive the loss bound through the existing arrival curve and service curve via this parameter. We then prove that our result is suitable for the networks with multiple input flows. Simulations show the impact of buffer size, arrival traffic, and service on the loss factor.
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spelling pubmed-38176372013-11-13 Extending Stochastic Network Calculus to Loss Analysis Luo, Chao Yu, Li Zheng, Jun ScientificWorldJournal Research Article Loss is an important parameter of Quality of Service (QoS). Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss by deterministic network calculus, but there are few results to extend stochastic network calculus for loss analysis. In this paper, we introduce a new parameter named loss factor into stochastic network calculus and then derive the loss bound through the existing arrival curve and service curve via this parameter. We then prove that our result is suitable for the networks with multiple input flows. Simulations show the impact of buffer size, arrival traffic, and service on the loss factor. Hindawi Publishing Corporation 2013-10-20 /pmc/articles/PMC3817637/ /pubmed/24228019 http://dx.doi.org/10.1155/2013/918565 Text en Copyright © 2013 Chao Luo et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Luo, Chao
Yu, Li
Zheng, Jun
Extending Stochastic Network Calculus to Loss Analysis
title Extending Stochastic Network Calculus to Loss Analysis
title_full Extending Stochastic Network Calculus to Loss Analysis
title_fullStr Extending Stochastic Network Calculus to Loss Analysis
title_full_unstemmed Extending Stochastic Network Calculus to Loss Analysis
title_short Extending Stochastic Network Calculus to Loss Analysis
title_sort extending stochastic network calculus to loss analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3817637/
https://www.ncbi.nlm.nih.gov/pubmed/24228019
http://dx.doi.org/10.1155/2013/918565
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