Statistical multiplexing and connection admission control in ATM networks

Asynchronous Transfer Mode (ATM) technology is widely employed for the transport of network traffic, and has the potential to be the base technology for the next generation of global communications. Connection Admission Control (CAC) is the effective traffic control mechanism which is necessary in ATM networks in order to avoid possible congestion at each network node and to achieve the Quality-of-Service (QoS) requested by each connection. CAC determines whether or not the network should accept a new connection. A new connection will only be accepted if the network has sufficient resources to meet its QoS requirements without affecting the QoS commitments already made by the network for existing connections. The design of a high-performance CAC is based on an in-depth understanding of the statistical characteristics of the traffic sources

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

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

Application of learning algorithms to traffic management in integrated services networks.

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN027131 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

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.

Resource dimensioning in a mixed traffic environment

An important goal of modern data networks is to support multiple applications over a single network infrastructure. The combination of data, voice, video and conference traffic, each requiring a unique Quality of Service (QoS), makes resource dimensioning a very challenging task. To guarantee QoS by mere over-provisioning of bandwidth is not viable in the long run, as network resources are expensive. The aim of proper resource dimensioning is to provide the required QoS while making optimal use of the allocated bandwidth. Dimensioning parameters used by service providers today are based on best practice recommendations, and are not necessarily optimal. This dissertation focuses on resource dimensioning for the DiffServ network architecture. Four predefined traffic classes, i.e. Real Time (RT), Interactive Business (IB), Bulk Business (BB) and General Data (GD), needed to be dimensioned in terms of bandwidth allocation and traffic regulation. To perform this task, a study was made of the DiffServ mechanism and the QoS requirements of each class. Traffic generators were required for each class to perform simulations. Our investigations show that the dominating Transport Layer protocol for the RT class is UDP, while TCP is mostly used by the other classes. This led to a separate analysis and requirement for traffic models for UDP and TCP traffic. Analysis of real-world data shows that modern network traffic is characterized by long-range dependency, self-similarity and a very bursty nature. Our evaluation of various traffic models indicates that the Multi-fractal Wavelet Model (MWM) is best for TCP due to its ability to capture long-range dependency and self-similarity. The Markov Modulated Poisson Process (MMPP) is able to model occasional long OFF-periods and burstiness present in UDP traffic. Hence, these two models were used in simulations. A test bed was implemented to evaluate performance of the four traffic classes defined in DiffServ. Traffic was sent through the test bed, while delay and loss was measured. For single class simulations, dimensioning values were obtained while conforming to the QoS specifications. Multi-class simulations investigated the effects of statistical multiplexing on the obtained values. Simulation results for various numerical provisioning factors (PF) were obtained. These factors are used to determine the link data rate as a function of the required average bandwidth and QoS. The use of class-based differentiation for QoS showed that strict delay and loss bounds can be guaranteed, even in the presence of very high (up to 90%) bandwidth utilization. Simulation results showed small deviations from best practice recommendation PF values: A value of 4 is currently used for both RT and IB classes, while 2 is used for the BB class. This dissertation indicates that 3.89 for RT, 3.81 for IB and 2.48 for BB achieve the prescribed QoS more accurately. It was concluded that either the bandwidth distribution among classes, or quality guarantees for the BB class should be adjusted since the RT and IB classes over-performed while BB under-performed. The results contribute to the process of resource dimensioning by adding value to dimensioning parameters through simulation rather than mere intuition or educated guessing.Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2007.Electrical, Electronic and Computer Engineeringunrestricte

A new numerical performance analysis method of leaky bucket policing algorithm over heavy-tailed on/off internet traffic

Master'sMASTER OF ENGINEERIN

Network delay control through adaptive queue management

Timeliness in delivering packets for delay-sensitive applications is an important QoS (Quality of Service) measure in many systems, notably those that need to provide real-time performance. In such systems, if delay-sensitive traffic is delivered to the destination beyond the deadline, then the packets will be rendered useless and dropped after received at the destination. Bandwidth that is already scarce and shared between network nodes is wasted in relaying these expired packets. This thesis proposes that a deterministic per-hop delay can be achieved by using a dynamic queue threshold concept to bound delay of each node. A deterministic per-hop delay is a key component in guaranteeing a deterministic end-to-end delay. The research aims to develop a generic approach that can constrain network delay of delay-sensitive traffic in a dynamic network. Two adaptive queue management schemes, namely, DTH (Dynamic THreshold) and ADTH (Adaptive DTH) are proposed to realize the claim. Both DTH and ADTH use the dynamic threshold concept to constrain queuing delay so that bounded average queuing delay can be achieved for the former and bounded maximum nodal delay can be achieved for the latter. DTH is an analytical approach, which uses queuing theory with superposition of N MMBP-2 (Markov Modulated Bernoulli Process) arrival processes to obtain a mapping relationship between average queuing delay and an appropriate queuing threshold, for queue management. While ADTH is an measurement-based algorithmic approach that can respond to the time-varying link quality and network dynamics in wireless ad hoc networks to constrain network delay. It manages a queue based on system performance measurements and feedback of error measured against a target delay requirement. Numerical analysis and Matlab simulation have been carried out for DTH for the purposes of validation and performance analysis. While ADTH has been evaluated in NS-2 simulation and implemented in a multi-hop wireless ad hoc network testbed for performance analysis. Results show that DTH and ADTH can constrain network delay based on the specified delay requirements, with higher packet loss as a trade-off

Quality of Service Controlled Multimedia Transport Protocol

PhDThis research looks at the design of an open transport protocol that supports a range of services including multimedia over low data-rate networks. Low data-rate multimedia applications require a system that provides quality of service (QoS) assurance and flexibility. One promising field is the area of content-based coding. Content-based systems use an array of protocols to select the optimum set of coding algorithms. A content-based transport protocol integrates a content-based application to a transmission network. General transport protocols form a bottleneck in low data-rate multimedia communicationbsy limiting throughpuot r by not maintainingt iming requirementsT. his work presents an original model of a transport protocol that eliminates the bottleneck by introducing a flexible yet efficient algorithm that uses an open approach to flexibility and holistic architectureto promoteQ oS.T he flexibility andt ransparenccyo mesi n the form of a fixed syntaxt hat providesa seto f transportp rotocols emanticsT. he mediaQ oSi s maintained by defining a generic descriptor. Overall, the structure of the protocol is based on a single adaptablea lgorithm that supportsa pplication independencen, etwork independencea nd quality of service. The transportp rotocol was evaluatedth rougha set of assessmentos:f f-line; off-line for a specific application; and on-line for a specific application. Application contexts used MPEG-4 test material where the on-line assessmenuts eda modified MPEG-4 pl; yer. The performanceo f the QoSc ontrolledt ransportp rotocoli s often bettert hano thers chemews hen appropriateQ oS controlledm anagemenatl gorithmsa re selectedT. his is shownf irst for an off-line assessmenwt here the performancei s compared between the QoS controlled multiplexer,a n emulatedM PEG-4F lexMux multiplexers chemea, ndt he targetr equirements. The performanceis also shownt o be better in a real environmentw hen the QoS controlled multiplexeri s comparedw ith the real MPEG-4F lexMux scheme

Modeling network traffic on a global network-centric system with artificial neural networks

This dissertation proposes a new methodology for modeling and predicting network traffic. It features an adaptive architecture based on artificial neural networks and is especially suited for large-scale, global, network-centric systems. Accurate characterization and prediction of network traffic is essential for network resource sizing and real-time network traffic management. As networks continue to increase in size and complexity, the task has become increasingly difficult and current methodology is not sufficiently adaptable or scaleable. Current methods model network traffic with express mathematical equations which are not easily maintained or adjusted. The accuracy of these models is based on detailed characterization of the traffic stream which is measured at points along the network where the data is often subject to constant variation and rapid evolution. The main contribution of this dissertation is development of a methodology that allows utilization of artificial neural networks with increased capability for adaptation and scalability. Application on an operating global, broadband network, the Connexion by Boeingʼ network, was evaluated to establish feasibility. A simulation model was constructed and testing was conducted with operational scenarios to demonstrate applicability on the case study network and to evaluate improvements in accuracy over existing methods --Abstract, page iii