Publicação
Evaluating the impact of traffic sampling in network analysis
| Resumo: | The sampling of network traffic is a very effective method in order to comprehend the behaviour and flow of a network, essential to build network management tools to control Service Level Agreements (SLAs), Quality of Service (QoS), traffic engineering, and the planning of both the capacity and the safety of the network. With the exponential rise of the amount traffic caused by the number of devices connected to the Internet growing, it gets increasingly harder and more expensive to understand the behaviour of a network through the analysis of the total volume of traffic. The use of sampling techniques, or selective analysis, which consists in the election of small number of packets in order to estimate the expected behaviour of a network, then becomes essential. Even though these techniques drastically reduce the amount of data to be analyzed, the fact that the sampling analysis tasks have to be performed in the network equipment can cause a significant impact in the performance of these equipment devices, and a reduction in the accuracy of the estimation of network state. In this dissertation project, an evaluation of the impact of selective analysis of network traffic will be explored, at a level of performance in estimating network state, and statistical properties such as self-similarity and Long-Range Dependence (LRD) that exist in original network traffic, allowing a better understanding of the behaviour of sampled network traffic. |
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| Autores principais: | Mendes, João Emanuel da Silva |
| Assunto: | Sampling Quality of service Long-range dependence Análise seletiva Qualidade de serviço Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | The sampling of network traffic is a very effective method in order to comprehend the behaviour and flow of a network, essential to build network management tools to control Service Level Agreements (SLAs), Quality of Service (QoS), traffic engineering, and the planning of both the capacity and the safety of the network. With the exponential rise of the amount traffic caused by the number of devices connected to the Internet growing, it gets increasingly harder and more expensive to understand the behaviour of a network through the analysis of the total volume of traffic. The use of sampling techniques, or selective analysis, which consists in the election of small number of packets in order to estimate the expected behaviour of a network, then becomes essential. Even though these techniques drastically reduce the amount of data to be analyzed, the fact that the sampling analysis tasks have to be performed in the network equipment can cause a significant impact in the performance of these equipment devices, and a reduction in the accuracy of the estimation of network state. In this dissertation project, an evaluation of the impact of selective analysis of network traffic will be explored, at a level of performance in estimating network state, and statistical properties such as self-similarity and Long-Range Dependence (LRD) that exist in original network traffic, allowing a better understanding of the behaviour of sampled network traffic. |
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