1,353 research outputs found
Network monitoring and performance assessment: from statistical models to neural networks
MĂĄster en InvestigaciĂłn e InnovaciĂłn en TecnologĂas de la InformaciĂłn y las
ComunicacionesIn the last few years, computer networks have been playing a key role in many
different fields. Companies have also evolved around the internet, getting advantage of
the huge capacity of diffusion. Nevertheless, this also means that computer networks
and IT systems have become a critical element for the business. In case of interruption or
malfunction of the systems, this could result in devastating economic impact.
In this light, it is necessary to provide models to properly evaluate and characterize
the computer networks. Focusing on modeling, one has many different alternatives: from
classical options based on statistic to recent alternatives based on machine learning and
deep learning. In this work, we want to study the different models available for each
context, paying attention to the advantage and disadvantages to provide the best solution
for each case.
To cover the majority of the spectrum, three cases have been studied: time-unaware
phenomena, where we look at the bias-variance trade-off, time-dependent phenomena,
where we pay attention the trends of the time series, and text processing to process
attributes obtained by DPI. For each case, several alternatives have been studied and
solutions have been tested both with synthetic data and real-world data, showing the
successfulness of the proposa
TCP throughput guarantee in the DiffServ Assured Forwarding service: what about the results?
Since the proposition of Quality of Service architectures by the IETF, the
interaction between TCP and the QoS services has been intensively studied. This
paper proposes to look forward to the results obtained in terms of TCP
throughput guarantee in the DiffServ Assured Forwarding (DiffServ/AF) service
and to present an overview of the different proposals to solve the problem. It
has been demonstrated that the standardized IETF DiffServ conditioners such as
the token bucket color marker and the time sliding window color maker were not
good TCP traffic descriptors. Starting with this point, several propositions
have been made and most of them presents new marking schemes in order to
replace or improve the traditional token bucket color marker. The main problem
is that TCP congestion control is not designed to work with the AF service.
Indeed, both mechanisms are antagonists. TCP has the property to share in a
fair manner the bottleneck bandwidth between flows while DiffServ network
provides a level of service controllable and predictable. In this paper, we
build a classification of all the propositions made during these last years and
compare them. As a result, we will see that these conditioning schemes can be
separated in three sets of action level and that the conditioning at the
network edge level is the most accepted one. We conclude that the problem is
still unsolved and that TCP, conditioned or not conditioned, remains
inappropriate to the DiffServ/AF service
A Quality of Service Monitoring System for Service Level Agreement Verification
Service-level-agreement (SLA) monitoring measures network Quality-of-Service (QoS) parameters to evaluate whether the service performance complies with the SLAs. It is becoming increasingly important for both Internet service providers (ISPs) and their customers. However, the rapid expansion of the Internet makes SLA monitoring a challenging task. As an efficient method to reduce both complexity and overheads for QoS measurements, sampling techniques have been used in SLA monitoring systems. In this thesis, I conduct a comprehensive study of sampling methods for network QoS measurements. I develop an efficient sampling strategy, which makes the measurements less intrusive and more efficient, and I design a network performance monitoring software, which monitors such QoS parameters as packet delay, packet loss and jitter for SLA monitoring and verification. The thesis starts with a discussion on the characteristics of QoS metrics related to the design of the monitoring system and the challenges in monitoring these metrics. Major measurement methodologies for monitoring these metrics are introduced. Existing monitoring systems can be broadly classified into two categories: active and passive measurements. The advantages and disadvantages of both methodologies are discussed and an active measurement methodology is chosen to realise the monitoring system. Secondly, the thesis describes the most common sampling techniques, such as systematic sampling, Poisson sampling and stratified random sampling. Theoretical analysis is performed on the fundamental limits of sampling accuracy. Theoretical analysis is also conducted on the performance of the sampling techniques, which is validated using simulation with real traffic. Both theoretical analysis and simulation results show that the stratified random sampling with optimum allocation achieves the best performance, compared with the other sampling methods. However, stratified sampling with optimum allocation requires extra statistics from the parent traffic traces, which cannot be obtained in real applications. In order to overcome this shortcoming, a novel adaptive stratified sampling strategy is proposed, based on stratified sampling with optimum allocation. A least-mean-square (LMS) linear prediction algorithm is employed to predict the required statistics from the past observations. Simulation results show that the proposed adaptive stratified sampling method closely approaches the performance of the stratified sampling with optimum allocation. Finally, a detailed introduction to the SLA monitoring software design is presented. Measurement results are displayed which calibrate systematic error in the measurements. Measurements between various remote sites have demonstrated impressively good QoS provided by Australian ISPs for premium services
A Quality of Service Monitoring System for Service Level Agreement Verification
Service-level-agreement (SLA) monitoring measures network Quality-of-Service (QoS) parameters to evaluate whether the service performance complies with the SLAs. It is becoming increasingly important for both Internet service providers (ISPs) and their customers. However, the rapid expansion of the Internet makes SLA monitoring a challenging task. As an efficient method to reduce both complexity and overheads for QoS measurements, sampling techniques have been used in SLA monitoring systems. In this thesis, I conduct a comprehensive study of sampling methods for network QoS measurements. I develop an efficient sampling strategy, which makes the measurements less intrusive and more efficient, and I design a network performance monitoring software, which monitors such QoS parameters as packet delay, packet loss and jitter for SLA monitoring and verification. The thesis starts with a discussion on the characteristics of QoS metrics related to the design of the monitoring system and the challenges in monitoring these metrics. Major measurement methodologies for monitoring these metrics are introduced. Existing monitoring systems can be broadly classified into two categories: active and passive measurements. The advantages and disadvantages of both methodologies are discussed and an active measurement methodology is chosen to realise the monitoring system. Secondly, the thesis describes the most common sampling techniques, such as systematic sampling, Poisson sampling and stratified random sampling. Theoretical analysis is performed on the fundamental limits of sampling accuracy. Theoretical analysis is also conducted on the performance of the sampling techniques, which is validated using simulation with real traffic. Both theoretical analysis and simulation results show that the stratified random sampling with optimum allocation achieves the best performance, compared with the other sampling methods. However, stratified sampling with optimum allocation requires extra statistics from the parent traffic traces, which cannot be obtained in real applications. In order to overcome this shortcoming, a novel adaptive stratified sampling strategy is proposed, based on stratified sampling with optimum allocation. A least-mean-square (LMS) linear prediction algorithm is employed to predict the required statistics from the past observations. Simulation results show that the proposed adaptive stratified sampling method closely approaches the performance of the stratified sampling with optimum allocation. Finally, a detailed introduction to the SLA monitoring software design is presented. Measurement results are displayed which calibrate systematic error in the measurements. Measurements between various remote sites have demonstrated impressively good QoS provided by Australian ISPs for premium services
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