Dynamic bandwidth allocation in ATM networks

Includes bibliographical references.This thesis investigates bandwidth allocation methodologies to transport new emerging bursty traffic types in ATM networks. However, existing ATM traffic management solutions are not readily able to handle the inevitable problem of congestion as result of the bursty traffic from the new emerging services. This research basically addresses bandwidth allocation issues for bursty traffic by proposing and exploring the concept of dynamic bandwidth allocation and comparing it to the traditional static bandwidth allocation schemes

Traffic control mechanisms with cell rate simulation for ATM networks.

PhDAbstract not availabl

From burstiness characterisation to traffic control strategy : a unified approach to integrated broadbank networks

The major challenge in the design of an integrated network is the integration and support of a wide variety of applications. To provide the requested performance guarantees, a traffic control strategy has to allocate network resources according to the characteristics of input traffic. Specifically, the definition of traffic characterisation is significant in network conception. In this thesis, a traffic stream is characterised based on a virtual queue principle. This approach provides the necessary link between network resources allocation and traffic control. It is difficult to guarantee performance without prior knowledge of the worst behaviour in statistical multiplexing. Accordingly, we investigate the worst case scenarios in a statistical multiplexer. We evaluate the upper bounds on the probabilities of buffer overflow in a multiplexer, and data loss of an input stream. It is found that in networks without traffic control, simply controlling the utilisation of a multiplexer does not improve the ability to guarantee performance. Instead, the availability of buffer capacity and the degree of correlation among the input traffic dominate the effect on the performance of loss. The leaky bucket mechanism has been proposed to prevent ATM networks from performance degradation due to congestion. We study the leaky bucket mechanism as a regulation element that protects an input stream. We evaluate the optimal parameter settings and analyse the worst case performance. To investigate its effectiveness, we analyse the delay performance of a leaky bucket regulated multiplexer. Numerical results show that the leaky bucket mechanism can provide well-behaved traffic with guaranteed delay bound in the presence of misbehaving traffic. Using the leaky bucket mechanism, a general strategy based on burstiness characterisation, called the LB-Dynamic policy, is developed for packet scheduling. This traffic control strategy is closely related to the allocation of both bandwidth and buffer in each switching node. In addition, the LB-Dynamic policy monitors the allocated network resources and guarantees the network performance of each established connection, irrespective of the traffic intensity and arrival patterns of incoming packets. Simulation studies demonstrate that the LB-Dynamic policy is able to provide the requested service quality for heterogeneous traffic in integrated broadband networks

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

Resource allocation in ATM networks

The areas of resource allocation ancl congestion control in ATM networks have been investigated. ATM networks and the guarantees given to users have been reviewed and a new model of ATM networking has been proposed. To aid the analysis of ATM network issues, performance modelling and simulation methods have been reviewed. Typical sources have been designed : a two-state Markov model for voice ; a multi-state Markov one layer variable bit rate video source model ; an empirical file transfer data source model ; and some basic network elements. The models have been verified and validated on a discrete event simulator. It was shown that there are problems when using ATM over satellite links. A model for the noise analysed from real satellite links was developed. Based on this model a new more efficient protocol for assembling ATM cells was proposed and simulated. Again at the cell level, the traffic that can pass the standardised conformance test and still produce the worst performance in the network was investigated. Counter to the traditional wisdom it was found that the on-off source does not always produce the worst case traffic. Users have been classified with new parameters, and it has been shown that these new classes of users can still be given guarantees without giving traffic descriptors. Adaptive user classes have been modelled mathematically. A new model for efficiency has been developed, which includes both network issues and economic issues. This new model defines congestion and also describes how to allocate resources when congested. It has been shown that this economic model coupled with the adaptive user classes allow for an increase in both network and economic efficiency simultaneously for some sample cases

Dynamic threshold-based algorithms for communication networks

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 68-72.A need to use dynamic thresholds arises in various communication networking scenarios under varying traffic conditions. In this thesis, we propose novel dynamic threshold-based algorithms for two different networking problems, namely the problem of burst assembly in Optical Burst Switching (OBS) networks and of bandwidth reservation in connection-oriented networks. Regarding the first problem, we present dynamic threshold-based burst assembly algorithms that attempt to minimize the average burst assembly delay due to burstification process while taking the burst rate constraints into consideration. Using synthetic and real traffic traces, we show that the proposed algorithms perform significantly better than the conventional timer-based schemes. In the second problem, we propose a model-free adaptive hysteresis algorithm for dynamic bandwidth reservation in a connection-oriented network subject to update frequency constraints. The simulation results in various traffic scenarios show that the proposed technique considerably outperforms the existing schemes without requiring any prior traffic information.Toksöz, Mehmet AltanM.S

Learning algorithms for the control of routing in integrated service communication networks

There is a high degree of uncertainty regarding the nature of traffic on future integrated service networks. This uncertainty motivates the use of adaptive resource allocation policies that can take advantage of the statistical fluctuations in the traffic demands. The adaptive control mechanisms must be 'lightweight', in terms of their overheads, and scale to potentially large networks with many traffic flows. Adaptive routing is one form of adaptive resource allocation, and this thesis considers the application of Stochastic Learning Automata (SLA) for distributed, lightweight adaptive routing in future integrated service communication networks. The thesis begins with a broad critical review of the use of Artificial Intelligence (AI) techniques applied to the control of communication networks. Detailed simulation models of integrated service networks are then constructed, and learning automata based routing is compared with traditional techniques on large scale networks. Learning automata are examined for the 'Quality-of-Service' (QoS) routing problem in realistic network topologies, where flows may be routed in the network subject to multiple QoS metrics, such as bandwidth and delay. It is found that learning automata based routing gives considerable blocking probability improvements over shortest path routing, despite only using local connectivity information and a simple probabilistic updating strategy. Furthermore, automata are considered for routing in more complex environments spanning issues such as multi-rate traffic, trunk reservation, routing over multiple domains, routing in high bandwidth-delay product networks and the use of learning automata as a background learning process. Automata are also examined for routing of both 'real-time' and 'non-real-time' traffics in an integrated traffic environment, where the non-real-time traffic has access to the bandwidth 'left over' by the real-time traffic. It is found that adopting learning automata for the routing of the real-time traffic may improve the performance to both real and non-real-time traffics under certain conditions. In addition, it is found that one set of learning automata may route both traffic types satisfactorily. Automata are considered for the routing of multicast connections in receiver-oriented, dynamic environments, where receivers may join and leave the multicast sessions dynamically. Automata are shown to be able to minimise the average delay or the total cost of the resulting trees using the appropriate feedback from the environment. Automata provide a distributed solution to the dynamic multicast problem, requiring purely local connectivity information and a simple updating strategy. Finally, automata are considered for the routing of multicast connections that require QoS guarantees, again in receiver-oriented dynamic environments. It is found that the distributed application of learning automata leads to considerably lower blocking probabilities than a shortest path tree approach, due to a combination of load balancing and minimum cost behaviour

Análisis y evaluación de los sistemas de protección contra la congestión en la red digital de sevicios integrados de banda ancha

Postprint (published version

Quality-of-service management in IP networks

Quality of Service (QoS) in Internet Protocol (IF) Networks has been the subject of active research over the past two decades. Integrated Services (IntServ) and Differentiated Services (DiffServ) QoS architectures have emerged as proposed standards for resource allocation in IF Networks. These two QoS architectures support the need for multiple traffic queuing systems to allow for resource partitioning for heterogeneous applications making use of the networks. There have been a number of specifications or proposals for the number of traffic queuing classes (Class of Service (CoS)) that will support integrated services in IF Networks, but none has provided verification in the form of analytical or empirical investigation to prove that its specification or proposal will be optimum. Despite the existence of the two standard QoS architectures and the large volume of research work that has been carried out on IF QoS, its deployment still remains elusive in the Internet. This is not unconnected with the complexities associated with some aspects of the standard QoS architectures. [Continues.