Cross Traffic Estimation By Loss Process Analysis (original) (raw)

An analytical model for loss estimation in network traffic analysis systems

Journal of Computer and System Sciences, 2006

Simulation models have been developed in order to foresee characteristics of networks, systems or protocols when carrying out tests in laboratories is very expensive or even impossible. This paper presents a simulation model of a multiprocessor network traffic analysis system. The model, which is based on closed networks of queues, evaluates the efficiency of the system depending on the hardware/software platform features. Therefore, this model is able to estimate performance early in the design and development stages simulating a multiprocessor architecture in charge of analysing network traffic. The goodness of the model will be checked by comparing analytical results with practical ones obtained in laboratory using a traffic analysis system that runs on a multiprocessor platform.

Rigorous Statistical Analysis of Internet Loss Measurements

IEEE/ACM Transactions on Networking, 2000

Loss measurements are widely used in today's networks. There are existing standards and commercial products to perform these measurements. The missing element is a rigorous statistical methodology for their analysis. Indeed, most existing tools ignore the correlation between packet losses and severely underestimate the errors in the measured loss ratios.

Measurement Based Modeling of Quality of Service in the Internet: A Methodological Approach

2001

This paper introduces a newmetho dology for analyzing and interpreting QoS values collected by active measurement and associated with an a priori constructive model. The originality of this solution is that we start with observed performance (or QoS) measures and derive inputs that have lead to these observations. This process is illustrated in the context of the modeling of the loss observed in an Internet path. It provides a powerful solution to address the complex problem of QoS estimation and network modeling.

Performance Evaluation and Traffic Modeling

2009

IntroductionMobile and wireless communication systems are becoming more and more complex, making understanding the interaction of different technologies on different layers a very difficult task. The introduction of sophisticated techniques on the physical layer that react to changes of the wireless channel on small timescales requires new paradigms for modeling, simulating, and analyzing current and future wireless networks. Investigating the relationship of new physical layer techniques, application-specific requirements and performance measures will become a major research topic for future wireless networks. A continuous change in the methodology for evaluating the network performance takes place in the Internet. In the past, network performance was mainly evaluated using concretely measurable values like packet loss rate, delay, or jitter. The current trend in the Internet goes toward application-specific quality measures that judge more the subjective experience of the end user th ...

Modelling Internet End-to-End Loss Behaviors: A Congestion Control Perspective

Journal of Computers, 2007

This paper proposes a new approach to modelling and controlling Internet end-to-end loss behaviours. Rather than select the model structure from the loss observations as being done previously, we construct a new loss model based on the TCP congestion control mechanisms. Thus, the model can explicitly reflect the correlation between end-to-end loss observations and network flow level activities. Besides simulation, the model has been tested in both wired and wireless Internet environments. The result shows that, unless the losses due to the transmission errors are excessive e.g. in some lossy wireless channels, the model can correctly capture end-to-end loss behaviours not only in terms of average rates but also in terms of loss patterns i.e. loss and good runlengths. This implies a good connection between the model structure and network flow level activities, which makes the model attractive for assisting network traffic control.

Traffic Analyses and Measurements

Resource Management of Mobile Cloud Computing Networks and Environments

Resource management schemes in current data centers, including cloud environments, are not well equipped to handle the dynamic variation in traffic caused by the large diversity of traffic sources, source mobility patterns, and underlying network characteristics. Part of the problem is lacking knowledge on the traffic source behaviour and its proper representation for development and operation. Inaccurate, static traffic models lead to incorrect estimation of traffic characteristics, making resource allocation, migration, and release schemes inefficient, and limit scalability. The end result is unsatisfied customers (due to service degradation) and operators (due to costly inefficient infrastructure use). The authors argue that developing appropriate methods and tools for traffic predictability requires carefully conducted and analysed traffic experiments. This chapter presents their measurements and statistical analyses on various traffic sources for two network settings, namely lo...

Estimating packet loss rate in the access through application-level measurements

2012

Abstract End user monitoring of quality of experience is one of the necessary steps to achieve an effective and winning control over network neutrality. The involvement of the end user, however, requires the development of light and user-friendly tools that can be easily run at the application level with limited effort and network resources usage. In this paper, we propose a simple model to estimate packet loss rate perceived by a connection, by round trip time and TCP goodput samples collected at the application level.

Statistical modeling and performance analysis of multi-scale traffic

2003

In this paper we propose a new statistical model for multi-scale traffic, and present an exact queueing analysis for the model. The model is based on the central moments and the marginal distributions of the cumulative traffic loads in different time scales. Only the first two moments are needed to characterize the traffic process, which greatly simplifies the representation and estimation. The queueing analysis uses a very general approach and can evaluate not only the steady state performance but also the transient queueing behavior. The analysis reveals that there exist two classes of packet losses, the absolute loss and the opportunistic loss, both of which can be examined exactly with the method. Based on the statistical model and the queueing analysis method, a compound model is constructed for the practical multiscale traffic, and its performance is evaluated from various aspects. This work provides a good basis for practical application of the mutli-scale traffic characterizations in network dimensioning and resource management.