750 research outputs found
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
A zero burst loss architecture for star OBS networks
Performance studies point to the fact that in an OBS network, the link utilization has to be kept very low in order for the burst loss probability to be within an acceptable level. Various congestion control schemes have been proposed, such as the use of converters, fiber delay lines, and deflection routing. However, these schemes do not alleviate this problem. It is our position that in order for OBS to become commercially viable, new schemes have to be devised that will either guarantee zero burst loss, or very low burst loss at high utilization. In a previous paper, we described effective zero burst loss schemes for OBS rings. In this paper, we present a zero burst loss scheme for star OBS topologies. Further research into the topic is required.5th IFIP International Conference on Network Control & Engineering for QoS, Security and MobilityRed de Universidades con Carreras en Informática (RedUNCI
Investigation of the tolerance of wavelength-routed optical networks to traffic load variations.
This thesis focuses on the performance of circuit-switched wavelength-routed optical network with unpredictable traffic pattern variations. This characteristic of optical networks is termed traffic forecast tolerance. First, the increasing volume and heterogeneous nature of data and voice traffic is discussed. The challenges in designing robust optical networks to handle unpredictable traffic statistics are described. Other work relating to the same research issues are discussed. A general methodology to quantify the traffic forecast tolerance of optical networks is presented. A traffic model is proposed to simulate dynamic, non-uniform loads, and used to test wavelength-routed optical networks considering numerous network topologies. The number of wavelengths required and the effect of the routing and wavelength allocation algorithm are investigated. A new method of quantifying the network tolerance is proposed, based on the calculation of the increase in the standard deviation of the blocking probabilities with increasing traffic load non-uniformity. The performance of different networks are calculated and compared. The relationship between physical features of the network topology and traffic forecast tolerance is investigated. A large number of randomly connected networks with different sizes were assessed. It is shown that the average lightpath length and the number of wavelengths required for full interconnection of the nodes in static operation both exhibit a strong correlation with the network tolerance, regardless of the degree of load non-uniformity. Finally, the impact of wavelength conversion on network tolerance is investigated. Wavelength conversion significantly increases the robustness of optical networks to unpredictable traffic variations. In particular, two sparse wavelength conversion schemes are compared and discussed: distributed wavelength conversion and localized wavelength conversion. It is found that the distributed wavelength conversion scheme outperforms localized wavelength conversion scheme, both with uniform loading and in terms of the network tolerance. The results described in this thesis can be used for the analysis and design of reliable WDM optical networks that are robust to future traffic demand variations
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Isochronets: a High-Speed Network Switching Architecture
Traditional switching techniques need hundred- or thousand-MIPS processing power within switches to support Gbit/s transmission rates available today. These techniques anchor their decision-making on control information within transmitted frames and thus must resolve routes at the speed in which frames are being pumped into switches. Isochronets can potentially switch at any transmission rate by making switching decisions independent of frame contents. Isochronets divide network bandwidth among routing trees, a technique called Route Division Multiple Access (RDMA). Frames access network resources through the appropriate routing tree to the destination. Frame structures are irrelevant for switching decisions. Consequently, Isochronets can support multiple framing protocols without adaptation layers and are strong candidates for all-optical implementations. All network-layer functions are reduced to an admission control mechanism designed to provide quality of service (QOS) guarantees for multiple classes of traffic. The main results of this work are: (1) A new network architecture suitable for high-speed transmissions; (2) An implementation of Isochronets using cheap off-theshelf components; (3) A comparison of RDMA with more traditional switching techniques, such as Packet Switching and Circuit Switching; (4) New protocols necessary for Isochronet operations; and (5) Use of Isochronet techniques at higher layers of the protocol stack (in particular, we show how Isochronet techniques may solve routing problems in ATM 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
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