Explicit congestion control algorithms for available bit rate services in asynchronous transfer mode networks

Congestion control of available bit rate (ABR) services in asynchronous transfer mode (ATM) networks has been the recent focus of the ATM Forum. The focus of this dissertation is to study the impact of queueing disciplines on ABR service congestion control, and to develop an explicit rate control algorithm. Two queueing disciplines, namely, First-In-First-Out (FIFO) and per-VC (virtual connection) queueing, are examined. Performance in terms of fairness, throughput, cell loss rate, buffer size and network utilization are benchmarked via extensive simulations. Implementation complexity analysis and trade-offs associated with each queueing implementation are addressed. Contrary to the common belief, our investigation demonstrates that per-VC queueing, which is costlier and more complex, does not necessarily provide any significant improvement over simple FIFO queueing. A new ATM switch algorithm is proposed to complement the ABR congestion control standard. The algorithm is designed to work with the rate-based congestion control framework recently recommended by the ATM Forum for ABR services. The algorithm\u27s primary merits are fast convergence, high throughput, high link utilization, and small buffer requirements. Mathematical analysis is done to show that the algorithm converges to the max-min fair allocation rates in finite time, and the convergence time is proportional to the distinct number of fair allocations and the round-trip delays in the network. At the steady state, the algorithm operates without causing any oscillations in rates. The algorithm does not require any parameter tuning, and proves to be very robust in a large ATM network. The impact of ATM switching and ATM layer congestion control on the performance of TCP/IP traffic is studied and the results are presented. The study shows that ATM layer congestion control improves the performance of TCP/IP traffic over ATM, and implementing the proposed switch algorithm drastically reduces the required switch buffer requirements. In order to validate claims, many benchmark ATM networks are simulated, and the performance of the switch is evaluated in terms of fairness, link utilization, response time, and buffer size requirements. In terms of performance and complexity, the algorithm proposed here offers many advantages over other proposed algorithms in the literature

TCP/IP traffic over ATM network with ABR flow and congestion control

Most traffics over the existing ATM network are generated by applications running over TCP/IP protocol stack. In the near future, the success of ATM technology will depend largely on how well it supports the huge legacy of existing TCP/IP applications. In this thesis, we study and compare the performance of TCP/IP traffic running on different rate based ABR flow control algorithms such as EFCI, ERICA and FMMRA by extensive simulations. Infinite source-end traffic behavior is chosen to represent, FTP application running on TCP/IP. Background VBR traffic with different ON-OFF frequency is introduced to produce transient network states as well as congestion. The simulations produce many insights on issues such as: ABR queue length in congested ATM switch, source-end ACR (Allowed Cell Rate), link utilization at congestion point, efficient end to end TCP throughput, the TCP congestion control window size, and the TCP round trip time. Based on the simulation results, zero cell loss switch buffer requirement of the three algorithms are compared, and the fairness of ABR bandwidth allocation among TCP connections are analyzed. The interaction between the TCP layer and the ATM layer flow and congestion control mechanism is analyzed. Our simulation results show that in order to get a good TCP throughput and affordable switch buffer requirement, some kind of switch queue length monitoring and control mechanism is necessary in the ABR. congestion algorithm

A survey of performance enhancement of transmission control protocol (TCP) in wireless ad hoc networks

This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2011 Springer OpenTransmission control protocol (TCP), which provides reliable end-to-end data delivery, performs well in traditional wired network environments, while in wireless ad hoc networks, it does not perform well. Compared to wired networks, wireless ad hoc networks have some specific characteristics such as node mobility and a shared medium. Owing to these specific characteristics of wireless ad hoc networks, TCP faces particular problems with, for example, route failure, channel contention and high bit error rates. These factors are responsible for the performance degradation of TCP in wireless ad hoc networks. The research community has produced a wide range of proposals to improve the performance of TCP in wireless ad hoc networks. This article presents a survey of these proposals (approaches). A classification of TCP improvement proposals for wireless ad hoc networks is presented, which makes it easy to compare the proposals falling under the same category. Tables which summarize the approaches for quick overview are provided. Possible directions for further improvements in this area are suggested in the conclusions. The aim of the article is to enable the reader to quickly acquire an overview of the state of TCP in wireless ad hoc networks.This study is partly funded by Kohat University of Science & Technology (KUST), Pakistan, and the Higher Education Commission, Pakistan

How (Un)Fair are the ABR Binary Schemes, Actually?

It is well known that a simple binary feedback rate--based congestion avoidance scheme cannot ensure a fairness goal of the Available Bit Rate (ABR) service, namely, max--min fairness. In this paper we show how the rates are distributed for the network consisting of the binary switches, and end--systems employing an additive--increase/multiplicative decrease rate control. The modeling assumptions fairly resembles the ABR congestion avoidance, and applies to an arbitrary network topology. The results are obtained on the basis of a stochastic modeling, upon which we obtain certain analytical results, and conduct a numerical simulation. We validate the stochastic modeling through a discrete--event simulation. We believe that modeling presented in this paper enlight the performance issues of the binary ABR schemes. Keywords ABR, ATM, congestion control, binary scheme, EFCI, fairness, max--min, proportional fairness, stochastic approximation, ODE, Lyapunov, Runge-Kutta

Reactive traffic control mechanisms for communication networks with self-similar bandwidth demands

Communication network architectures are in the process of being redesigned so that many different services are integrated within the same network. Due to this integration, traffic management algorithms need to balance the requirements of the traffic which the algorithms are directly controlling with Quality of Service (QoS) requirements of other classes of traffic which will be encountered in the network. Of particular interest is one class of traffic, termed elastic traffic, that responds to dynamic feedback from the network regarding the amount of available resources within the network. Examples of this type of traffic include the Available Bit Rate (ABR) service in Asynchronous Transfer Mode (ATM) networks and connections using Transmission Control Protocol (TCP) in the Internet. Both examples aim to utilise available bandwidth within a network. Reactive traffic management, like that which occurs in the ABR service and TCP, depends explicitly on the dynamic bandwidth requirements of other traffic which is currently using the network. In particular, there is significant evidence that a wide range of network traffic, including Ethernet, World Wide Web, Varible Bit Rate video and signalling traffic, is self-similar. The term self-similar refers to the particular characteristic of network traffic to remain bursty over a wide range of time scales. A closely associated characteristic of self-similar traffic is its long-range dependence (LRD), which refers to the significant correlations that occur with the traffic. By utilising these correlations, greater predictability of network traffic can be achieved, and hence the performance of reactive traffic management algorithms can be enhanced. A predictive rate control algorithm, called PERC (Predictive Explicit Rate Control), is proposed in this thesis which is targeted to the ABR service in ATM networks. By incorporating the LRD stochastic structure of background traffic, measurements of the bandwidth requirements of background traffic, and the delay associated with a particular ABR connection, a predictive algorithm is defined which provides explicit rate information that is conveyed to ABR sources. An enhancement to PERC is also described. This algorithm, called PERC+, uses previous control information to correct prediction errors that occur for connections with larger round-trip delay. These algorithms have been extensively analysed with regards to their network performance, and simulation results show that queue lengths and cell loss rates are significantly reduced when these algorithms are deployed. An adaptive version of PERC has also been developed using real-time parameter estimates of self-similar traffic. This has excellent performance compared with standard ABR rate control algorithms such as ERICA. Since PERC and its enhancement PERC+ have explicitly utilised the index of self-similarity, known as the Hurst parameter, the sensitivity of these algorithms to this parameter can be determined analytically. Research work described in this thesis shows that the algorithms have an asymmetric sensitivity to the Hurst parameter, with significant sensitivity in the region where the parameter is underestimated as being close to 0.5. Simulation results reveal the same bias in the performance of the algorithm with regards to the Hurst parameter. In contrast, PERC is insensitive to estimates of the mean, using the sample mean estimator, and estimates of the traffic variance, which is due to the algorithm primarily utilising the correlation structure of the traffic to predict future bandwidth requirements. Sensitivity analysis falls into the area of investigative research, but it naturally leads to the area of robust control, where algorithms are designed so that uncertainty in traffic parameter estimation or modelling can be accommodated. An alternative robust design approach, to the standard maximum entropy approach, is proposed in this thesis that uses the maximum likelihood function to develop the predictive rate controller. The likelihood function defines the proximity of a specific traffic model to the traffic data, and hence gives a measure of the performance of a chosen model. Maximising the likelihood function leads to optimising robust performance, and it is shown, through simulations, that the system performance is close to the optimal performance as compared with maximising the spectral entropy. There is still debate regarding the influence of LRD on network performance. This thesis also considers the question of the influence of LRD on traffic predictability, and demonstrates that predictive rate control algorithms that only use short-term correlations have close performance to algorithms that utilise long-term correlations. It is noted that predictors based on LRD still out-perform ones which use short-term correlations, but that there is Potential simplification in the design of predictors, since traffic predictability can be achieved using short-term correlations. This thesis forms a substantial contribution to the understanding of control in the case where self-similar processes form part of the overall system. Rather than doggedly pursuing self-similar control, a broader view has been taken where the performance of algorithms have been considered from a number of perspectives. A number of different research avenues lead on from this work, and these are outlined

Some aspects of traffic control and performance evaluation of ATM networks

The emerging high-speed Asynchronous Transfer Mode (ATM) networks are expected to integrate through statistical multiplexing large numbers of traffic sources having a broad range of statistical characteristics and different Quality of Service (QOS) requirements. To achieve high utilisation of network resources while maintaining the QOS, efficient traffic management strategies have to be developed. This thesis considers the problem of traffic control for ATM networks. The thesis studies the application of neural networks to various ATM traffic control issues such as feedback congestion control, traffic characterization, bandwidth estimation, and Call Admission Control (CAC). A novel adaptive congestion control approach based on a neural network that uses reinforcement learning is developed. It is shown that the neural controller is very effective in providing general QOS control. A Finite Impulse Response (FIR) neural network is proposed to adaptively predict the traffic arrival process by learning the relationship between the past and future traffic variations. On the basis of this prediction, a feedback flow control scheme at input access nodes of the network is presented. Simulation results demonstrate significant performance improvement over conventional control mechanisms. In addition, an accurate yet computationally efficient approach to effective bandwidth estimation for multiplexed connections is investigated. In this method, a feed forward neural network is employed to model the nonlinear relationship between the effective bandwidth and the traffic situations and a QOS measure. Applications of this approach to admission control, bandwidth allocation and dynamic routing are also discussed. A detailed investigation has indicated that CAC schemes based on effective bandwidth approximation can be very conservative and prevent optimal use of network resources. A modified effective bandwidth CAC approach is therefore proposed to overcome the drawback of conventional methods. Considering statistical multiplexing between traffic sources, we directly calculate the effective bandwidth of the aggregate traffic which is modelled by a two-state Markov modulated Poisson process via matching four important statistics. We use the theory of large deviations to provide a unified description of effective bandwidths for various traffic sources and the associated ATM multiplexer queueing performance approximations, illustrating their strengths and limitations. In addition, a more accurate estimation method for ATM QOS parameters based on the Bahadur-Rao theorem is proposed, which is a refinement of the original effective bandwidth approximation and can lead to higher link utilisation