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

B&W Call Admission Control for Multimedia Communication Networks

In the multimedia communication networks providing quality of service (QoS), an interface between the signal processing systems and the communication systems is the call admission control (CAC) mechanism. Owing to the heterogeneous traffic produced by diverse signal processing systems in a multimedia communication network, the traditional CAC mechanism that used only one CAC algorithm can no longer satisfy the aim of QoS CAC: Utilize the network resource to the most best and still satisfy the QoS requirements of all connections. For satisfying the aim of QoS CAC in the multimedia communication networks, this study proposed an innovative CAC mechanism called black and white CAC (B&W CAC), which uses two CAC algorithms. One of them is called black CAC controller and is used for the traffic with specifications more uncertain, which is called black traffic here. The other is call white CAC controller and is for the traffic with clearer specifications, which is call white traffic. Because white traffic is simple, an equivalent bandwidth CAC is taken as the white CAC. On the other hand, a neural network CAC (NNCAC) is employed to be the black CAC to overcome the uncertainty of black traffic. Furthermore, owing to more parameters needed in a QoS CAC mechanism, a hierarchical NNCAC is proposed instead of the common used NNCAC. Besides to accommodate more parameters, a hierarchical NNCAC can keep the complexity low. The simulation results show the B&W CAC can obtain higher utilization and still meet the QoS requirements of traffic sources

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

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Fuzzy-Logic Based Call Admission Control in 5G Cloud Radio Access Networks with Pre-emption

YesFifth generation (5G) cellular networks will be comprised of millions of connected devices like wearable devices, Androids, iPhones, tablets and the Internet of Things (IoT) with a plethora of applications generating requests to the network. The 5G cellular networks need to cope with such sky-rocketing tra c requests from these devices to avoid network congestion. As such, cloud radio access networks (C-RAN) has been considered as a paradigm shift for 5G in which requests from mobile devices are processed in the cloud with shared baseband processing. Despite call admission control (CAC) being one of radio resource management techniques to avoid the network congestion, it has recently been overlooked by the community. The CAC technique in 5G C-RAN has a direct impact on the quality of service (QoS) for individual connections and overall system e ciency. In this paper, a novel Fuzzy-Logic based CAC scheme with pre-emption in C-RAN is proposed. In this scheme, cloud bursting technique is proposed to be used during congestion, where some delay tolerant low-priority connections are pre-empted and outsourced to a public cloud with a penalty charge. Simulation results show that the proposed scheme has low blocking probability below 5%, high throughput, low energy consumption and up to 95% of return on revenue

A review of connection admission control algorithms for ATM networks

The emergence of high-speed networks such as those with ATM integrates large numbers of services with a wide range of characteristics. Admission control is a prime instrument for controlling congestion in the network. As part of connection services to an ATM system, the Connection Admission Control (CAC) algorithm decides if another call or connection can be admitted to the Broadband Network. The main task of the CAC is to ensure that the broadband resources will not saturate or overflow within a very small probability. It limits the connections and guarantees Quality of Service for the new connection. The algorithm for connection admission is crucial in determining bandwidth utilisation efficiency. With statistical multiplexing more calls can be allocated on a network link, while still maintaining the Quality of Service specified by the connection with traffic parameters and type of service. A number of algorithms for admission control for Broadband Services with ATM Networks are described and compared for performance under different traffic loads. There is a general description of the ATM Network as an introduction. Issues to do with source distributions and traffic models are explored in Chapter 2. Chapter 3 provides an extensive presentation of the CAC algorithms for ATM Broadband Networks. The ideas about the Effective Bandwidth are reviewed in Chapter 4, and a different approach to admission control using online measurement is presented in Chapter 5. Chapter 6 has the numerical evaluation of four of the key algorithms, with simulations. Finally Chapter 7 has conclusions of the findings and explores some possibilities for further work

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

Application of an automatically designed fuzzy logic decision support system to connection admission control in ATM networks

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