Traffic measurement and analysis

Measurement and analysis of real traffic is important to gain knowledge about the characteristics of the traffic. Without measurement, it is impossible to build realistic traffic models. It is recent that data traffic was found to have self-similar properties. In this thesis work traffic captured on the network at SICS and on the Supernet, is shown to have this fractal-like behaviour. The traffic is also examined with respect to which protocols and packet sizes are present and in what proportions. In the SICS trace most packets are small, TCP is shown to be the predominant transport protocol and NNTP the most common application. In contrast to this, large UDP packets sent between not well-known ports dominates the Supernet traffic. Finally, characteristics of the client side of the WWW traffic are examined more closely. In order to extract useful information from the packet trace, web browsers use of TCP and HTTP is investigated including new features in HTTP/1.1 such as persistent connections and pipelining. Empirical probability distributions are derived describing session lengths, time between user clicks and the amount of data transferred due to a single user click. These probability distributions make up a simple model of WWW-sessions

Providing proportional TCP performance by fixed-point approximations over bandwidth on demand satellite networks

In this paper we focus on the provision of propor- tional class-based service differentiation to transmission control protocol (TCP) flows in the context of bandwidth on demand(BoD) split-TCP geostationary (GEO) satellite networks. Our approach involves the joint configuration of TCP-Performance Enhancing Proxy (TCP-PEP) agents at the transport layer and the scheduling algorithm controlling the resource allocation at the Medium Access Control (MAC) layer. We show that the two differentiation mechanisms exhibit complementary behavior in achieving the desired differentiation throughout the traffic load space: the TCP-PEPs control differentiation at low and medium system utilization, whereas the MAC scheduler becomes the dominant differentiation factor under high traffic load. The main challenge for the satellite operator is to appropriately configure those two mechanisms to achieve a specific differentiation target for the different classes of TCP flows. To this end, we propose a fixed-point framework to analytically approximate the achieved differentiated TCP performance. We validate the predictive capacity of our analytical method via simulations and show that our approximations closely match the performance of different classes of TCP flows under various scenarios for the network traffic load and configuration of the MAC scheduler and TCP-PEP agent. Satellite network operators could use our approximations as an analytical tool to tune their network

A hybrid queueing model for fast broadband networking simulation

PhDThis research focuses on the investigation of a fast simulation method for broadband telecommunication networks, such as ATM networks and IP networks. As a result of this research, a hybrid simulation model is proposed, which combines the analytical modelling and event-driven simulation modelling to speeding up the overall simulation. The division between foreground and background traffic and the way of dealing with these different types of traffic to achieve improvement in simulation time is the major contribution reported in this thesis. Background traffic is present to ensure that proper buffering behaviour is included during the course of the simulation experiments, but only the foreground traffic of interest is simulated, unlike traditional simulation techniques. Foreground and background traffic are dealt with in a different way. To avoid the need for extra events on the event list, and the processing overhead, associated with the background traffic, the novel technique investigated in this research is to remove the background traffic completely, adjusting the service time of the queues for the background traffic to compensate (in most cases, the service time for the foreground traffic will increase). By removing the background traffic from the event-driven simulator the number of cell processing events dealt with is reduced drastically. Validation of this approach shows that, overall, the method works well, but the simulation using this method does have some differences compared with experimental results on a testbed. The reason for this is mainly because of the assumptions behind the analytical model that make the modelling tractable. Hence, the analytical model needs to be adjusted. This is done by having a neural network trained to learn the relationship between the input traffic parameters and the output difference between the proposed model and the testbed. Following this training, simulations can be run using the output of the neural network to adjust the analytical model for those particular traffic conditions. The approach is applied to cell scale and burst scale queueing to simulate an ATM switch, and it is also used to simulate an IP router. In all the applications, the method ensures a fast simulation as well as an accurate result

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

Delay-oriented active queue management in TCP/IP networks

PhDInternet-based applications and services are pervading everyday life. Moreover, the growing popularity of real-time, time-critical and mission-critical applications set new challenges to the Internet community. The requirement for reducing response time, and therefore latency control is increasingly emphasized. This thesis seeks to reduce queueing delay through active queue management. While mathematical studies and research simulations reveal that complex trade-off relationships exist among performance indices such as throughput, packet loss ratio and delay, etc., this thesis intends to find an improved active queue management algorithm which emphasizes delay control without trading much on other performance indices such as throughput and packet loss ratio. The thesis observes that in TCP/IP network, packet loss ratio is a major reflection of congestion severity or load. With a properly functioning active queue management algorithm, traffic load will in general push the feedback system to an equilibrium point in terms of packet loss ratio and throughput. On the other hand, queue length is a determinant factor on system delay performance while has only a slight influence on the equilibrium. This observation suggests the possibility of reducing delay while maintaining throughput and packet loss ratio relatively unchanged. The thesis also observes that queue length fluctuation is a reflection of both load changes and natural fluctuation in arriving bit rate. Monitoring queue length fluctuation alone cannot distinguish the difference and identify congestion status; and yet identifying this difference is crucial in finding out situations where average queue size and hence queueing delay can be properly controlled and reasonably reduced. However, many existing active queue management algorithms only monitor queue length, and their control policies are solely based on this measurement. In our studies, our novel finding is that the arriving bit rate distribution of all sources contains information which can be a better indication of congestion status and has a correlation with traffic burstiness. And this thesis develops a simple and scalable way to measure its two most important characteristics, namely the mean ii and the variance of the arriving rate distribution. The measuring mechanism is based on a Zombie List mechanism originally proposed and deployed in Stabilized RED to estimate the number of flows and identify misbehaving flows. This thesis modifies the original zombie list measuring mechanism, makes it capable of measuring additional variables. Based on these additional measurements, this thesis proposes a novel modification to the RED algorithm. It utilizes a robust adaptive mechanism to ensure that the system reaches proper equilibrium operating points in terms of packet loss ratio and queueing delay under various loads. Furthermore, it identifies different congestion status where traffic is less bursty and adapts RED parameters in order to reduce average queue size and hence queueing delay accordingly. Using ns-2 simulation platform, this thesis runs simulations of a single bottleneck link scenario which represents an important and popular application scenario such as home access network or SoHo. Simulation results indicate that there are complex trade-off relationships among throughput, packet loss ratio and delay; and in these relationships delay can be substantially reduced whereas trade-offs on throughput and packet loss ratio are negligible. Simulation results show that our proposed active queue management algorithm can identify circumstances where traffic is less bursty and actively reduce queueing delay with hardly noticeable sacrifice on throughput and packet loss ratio performances. In conclusion, our novel approach enables the application of adaptive techniques to more RED parameters including those affecting queue occupancy and hence queueing delay. The new modification to RED algorithm is a scalable approach and does not introduce additional protocol overhead. In general it brings the benefit of substantially reduced delay at the cost of limited processing overhead and negligible degradation in throughput and packet loss ratio. However, our new algorithm is only tested on responsive flows and a single bottleneck scenario. Its effectiveness on a combination of responsive and non-responsive flows as well as in more complicated network topology scenarios is left for future work

Performance modelling with adaptive hidden Markov models and discriminatory processor sharing queues

In modern computer systems, workload varies at different times and locations. It is important to model the performance of such systems via workload models that are both representative and efficient. For example, model-generated workloads represent realistic system behaviour, especially during peak times, when it is crucial to predict and address performance bottlenecks. In this thesis, we model performance, namely throughput and delay, using adaptive models and discrete queues. Hidden Markov models (HMMs) parsimoniously capture the correlation and burstiness of workloads with spatiotemporal characteristics. By adapting the batch training of standard HMMs to incremental learning, online HMMs act as benchmarks on workloads obtained from live systems (i.e. storage systems and financial markets) and reduce time complexity of the Baum-Welch algorithm. Similarly, by extending HMM capabilities to train on multiple traces simultaneously it follows that workloads of different types are modelled in parallel by a multi-input HMM. Typically, the HMM-generated traces verify the throughput and burstiness of the real data. Applications of adaptive HMMs include predicting user behaviour in social networks and performance-energy measurements in smartphone applications. Equally important is measuring system delay through response times. For example, workloads such as Internet traffic arriving at routers are affected by queueing delays. To meet quality of service needs, queueing delays must be minimised and, hence, it is important to model and predict such queueing delays in an efficient and cost-effective manner. Therefore, we propose a class of discrete, processor-sharing queues for approximating queueing delay as response time distributions, which represent service level agreements at specific spatiotemporal levels. We adapt discrete queues to model job arrivals with distributions given by a Markov-modulated Poisson process (MMPP) and served under discriminatory processor-sharing scheduling. Further, we propose a dynamic strategy of service allocation to minimise delays in UDP traffic flows whilst maximising a utility function.Open Acces

A Survey of Performance Evaluation and Control for Self-Similar Network Traffic

This paper surveys techniques for the recognition and treatment of self-similar network or internetwork traffic. Various researchers have reported traffic measurements that demonstrate considerable burstiness on a range of time scales with properties of self-similarity. Rapid technological development has widened the scope of network and Internet applications and, in turn, increased traffic volume. The exponential growth of the number of servers, as well as the number of users, causes Internet performance to be problematic as a result of the significant impact that long-range dependent traffic has on buffer requirements. Consequently, accurate and reliable measurement, analysis and control of Internet traffic are vital. The most significant techniques for performance evaluation include theoretical analysis, simulation, and empirical study based on measurement. In this research, we discuss existing and recent developments in performance evaluation and control tools used in network traffic engineering

Discrete-time queueing model for responsive network traffic and bottleneck queues

The Internet has been more and more intensively used in recent years. Although network infrastructure has been regularly upgraded, and the ability to manage heavy traffic greatly increased, especially on the core networks, congestion never ceases to appear, as the amount of traffic that flow on the Internet seems to be increasing at an even faster rate. Thus, congestion control mechanisms play a vital role in the functioning of the Internet. Active Queue Management (AQM) is a popular type of congestion control mechanism that is implemented on gateways (most notably routers), which can predict and avoid the congestion before it happens. When properly configured, AQMs can effectively reduce the congestion, and alleviate some of the problems such as global synchronisation and unfairness to bursty traffic. However, there are still many problems regarding AQMs. Most of the AQM schemes are quite sensitive to their parameters setting, and these parameters may be heavily dependent on the network traffic profile, which the administrator may not have intensive knowledge of, and is likely to change over time. When poorly configured, many AQMs perform no better than the basic drop-tail queue. There is currently no effective method to compare the performance of these AQM algorithms, caused by the parameter configuration problem. In this research, the aim is to propose a new analytical model, which mainly uses discrete-time queueing theory. A novel transient modification to the conventional equilibrium-based method is proposed, and it is utilised to further develop a dynamic interactive model of responsive traffic and bottleneck queues. Using step-by-step analysis, it represents the bursty traffic and oscillating queue length behaviour in practical network more accurately. It also provides an effective way of predicting the behaviour of a TCP-AQM system, allowing easier parameter optimisation for AQM schemes. Numerical solution using MATLAB and software simulation using NS-2 are used to extensively validate the proposed models, theories and conclusions