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

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Study of the CAC mechanisms for telecommunications systems with adaptive links according to propagation conditions

This paper presents the framework and the activities of a PhD research work in progress supported by Alcatel Alenia Space in collaboration with TeSA and SUPAERO. It deals with Connection Admission Control (CAC) for Telecommunications Systems with adaptive links according to propagation conditions. Indeed, in high frequency bands communications, deep fadings may occur because of atmospheric propagation losses. The mitigation techniques used to counteract fades impacts the system capacity, therefore the CAC mechanism. The CAC which only uses current capacity information may lead to intolerable dropping of admitted connection, and thus breaches the QoS guarantees made upon connection acceptance. New CAC mechanisms shall be studied to take into account the capacity variation and the mitigation techniques (IFMT) developed to compensate the attenuation in Ka and above frequency range

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

An Adaptive Scheme for Admission Control in ATM Networks

This paper presents a real time front-end admission control scheme for ATM networks. A call management scheme which uses the burstiness associated with traffic sources in a heterogeneous ATM environment to effect dynamic assignment of bandwidth is presented. In the proposed scheme, call acceptance is based on an on-line evaluation of the upper bound on cell loss probability which is derived from the estimated distribution of the number of calls arriving. Using this scheme, the negotiated quality of service will be assured when there is no estimation error. The control mechanism is effective when the number of calls is large, and tolerates loose bandwidth enforcement and loose policing control. The proposed approach is very effective in the connection oriented transport of ATM networks where the decision to admit new traffic is based on thea priori knowledge of the state of the route taken by the traffic

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

A new approach to service provisioning in ATM networks

The authors formulate and solve a problem of allocating resources among competing services differentiated by user traffic characteristics and maximum end-to-end delay. The solution leads to an alternative approach to service provisioning in an ATM network, in which the network offers directly for rent its bandwidth and buffers and users purchase freely resources to meet their desired quality. Users make their decisions based on their own traffic parameters and delay requirements and the network sets prices for those resources. The procedure is iterative in that the network periodically adjusts prices based on monitored user demand, and is decentralized in that only local information is needed for individual users to determine resource requests. The authors derive the network's adjustment scheme and the users' decision rule and establish their optimality. Since the approach does not require the network to know user traffic and delay parameters, it does not require traffic policing on the part of the network

Renegotiation based dynamic bandwidth allocation for selfsimilar VBR traffic

The provision of QoS to applications traffic depends heavily on how different traffic types are categorized and classified, and how the prioritization of these applications are managed. Bandwidth is the most scarce network resource. Therefore, there is a need for a method or system that distributes an available bandwidth in a network among different applications in such a way that each class or type of traffic receives their constraint QoS requirements. In this dissertation, a new renegotiation based dynamic resource allocation method for variable bit rate (VBR) traffic is presented. First, pros and cons of available off-line methods that are used to estimate selfsimilarity level (represented by Hurst parameter) of a VBR traffic trace are empirically investigated, and criteria to select measurement parameters for online resource management are developed. It is shown that wavelet analysis based methods are the strongest tools in estimation of Hurst parameter with their low computational complexities, compared to the variance-time method and R/S pox plot. Therefore, a temporal energy distribution of a traffic data arrival counting process among different frequency sub-bands is considered as a traffic descriptor, and then a robust traffic rate predictor is developed by using the Haar wavelet analysis. The empirical results show that the new on-line dynamic bandwidth allocation scheme for VBR traffic is superior to traditional dynamic bandwidth allocation methods that are based on adaptive algorithms such as Least Mean Square, Recursive Least Square, and Mean Square Error etc. in terms of high utilization and low queuing delay. Also a method is developed to minimize the number of bandwidth renegotiations to decrease signaling costs on traffic schedulers (e.g. WFQ) and networks (e.g. ATM). It is also quantified that the introduced renegotiation based bandwidth management scheme decreases heavytailedness of queue size distributions, which is an inherent impact of traffic self similarity. The new design increases the achieved utilization levels in the literature, provisions given queue size constraints and minimizes the number of renegotiations simultaneously. This renegotiation -based design is online and practically embeddable into QoS management blocks, edge routers and Digital Subscriber Lines Access Multiplexers (DSLAM) and rate adaptive DSL modems

Improved learning automata applied to routing in multi-service networks

Multi-service communications networks are generally designed, provisioned and configured, based on source-destination user demands expected to occur over a recurring time period. However due to network users' actions being non-deterministic, actual user demands will vary from those expected, potentially causing some network resources to be under- provisioned, with others possibly over-provisioned. As actual user demands vary over the recurring time period from those expected, so the status of the various shared network resources may also vary. This high degree of uncertainty necessitates using adaptive resource allocation mechanisms to share the finite network resources more efficiently so that more of actual user demands may be accommodated onto the network. The overhead for these adaptive resource allocation mechanisms must be low in order to scale for use in large networks carrying many source-destination user demands. This thesis examines the use of stochastic learning automata for the adaptive routing problem (these being adaptive, distributed and simple in implementation and operation) and seeks to improve their weakness of slow convergence whilst maintaining their strength of subsequent near optimal performance. Firstly, current reinforcement algorithms (the part causing the automaton to learn) are examined for applicability, and contrary to the literature the discretised schemes are found in general to be unsuitable. Two algorithms are chosen (one with fast convergence, the other with good subsequent performance) and are improved through automatically adapting the learning rates and automatically switching between the two algorithms. Both novel methods use local entropy of action probabilities for determining convergence state. However when the convergence speed and blocking probability is compared to a bandwidth-based dynamic link-state shortest-path algorithm, the latter is found to be superior. A novel re-application of learning automata to the routing problem is therefore proposed: using link utilisation levels instead of call acceptance or packet delay. Learning automata now return a lower blocking probability than the dynamic shortest-path based scheme under realistic loading levels, but still suffer from a significant number of convergence iterations. Therefore the final improvement is to combine both learning automata and shortest-path concepts to form a hybrid algorithm. The resulting blocking probability of this novel routing algorithm is superior to either algorithm, even when using trend user demands

A Survey of Network Optimization Techniques for Traffic Engineering

TCP/IP represents the reference standard for the implementation of interoperable communication networks. Nevertheless, the layering principle at the basis of interoperability severely limits the performance of data communication networks, thus requiring proper configuration and management in order to provide effective management of traffic flows. This paper presents a brief survey related to network optimization using Traffic Engineering algorithms, aiming at providing additional insight to the different alternatives available in the scientific literature

Quality of service optimization of multimedia traffic in mobile networks

Mobile communication systems have continued to evolve beyond the currently deployed Third Generation (3G) systems with the main goal of providing higher capacity. Systems beyond 3G are expected to cater for a wide variety of services such as speech, data, image transmission, video, as well as multimedia services consisting of a combination of these. With the air interface being the bottleneck in mobile networks, recent enhancing technologies such as the High Speed Downlink Packet Access (HSDPA), incorporate major changes to the radio access segment of 3G Universal Mobile Telecommunications System (UMTS). HSDPA introduces new features such as fast link adaptation mechanisms, fast packet scheduling, and physical layer retransmissions in the base stations, necessitating buffering of data at the air interface which presents a bottleneck to end-to-end communication. Hence, in order to provide end-to-end Quality of Service (QoS) guarantees to multimedia services in wireless networks such as HSDPA, efficient buffer management schemes are required at the air interface. The main objective of this thesis is to propose and evaluate solutions that will address the QoS optimization of multimedia traffic at the radio link interface of HSDPA systems. In the thesis, a novel queuing system known as the Time-Space Priority (TSP) scheme is proposed for multimedia traffic QoS control. TSP provides customized preferential treatment to the constituent flows in the multimedia traffic to suit their diverse QoS requirements. With TSP queuing, the real-time component of the multimedia traffic, being delay sensitive and loss tolerant, is given transmission priority; while the non-real-time component, being loss sensitive and delay tolerant, enjoys space priority. Hence, based on the TSP queuing paradigm, new buffer managementalgorithms are designed for joint QoS control of the diverse components in a multimedia session of the same HSDPA user. In the thesis, a TSP based buffer management algorithm known as the Enhanced Time Space Priority (E-TSP) is proposed for HSDPA. E-TSP incorporates flow control mechanisms to mitigate congestion in the air interface buffer of a user with multimedia session comprising real-time and non-real-time flows. Thus, E-TSP is designed to provide efficient network and radio resource utilization to improve end-to-end multimedia traffic performance. In order to allow real-time optimization of the QoS control between the real-time and non-real-time flows of the HSDPA multimedia session, another TSP based buffer management algorithm known as the Dynamic Time Space Priority (D-TSP) is proposed. D-TSP incorporates dynamic priority switching between the real-time and non-real-time flows. D-TSP is designed to allow optimum QoS trade-off between the flows whilst still guaranteeing the stringent real-time component’s QoS requirements. The thesis presents results of extensive performance studies undertaken via analytical modelling and dynamic network-level HSDPA simulations demonstrating the effectiveness of the proposed TSP queuing system and the TSP based buffer management schemes