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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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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. |
format | Online Article Text |
id | pubmed-3817637 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT luochao extendingstochasticnetworkcalculustolossanalysis AT yuli extendingstochasticnetworkcalculustolossanalysis AT zhengjun extendingstochasticnetworkcalculustolossanalysis |