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

Study of the Application of Neural Networks in Internet Traffic Engineering

In this study, we showed various approachs implemented in Artificial Neural Networks for network resources management and Internet congestion control. Through a training process, Neural Networks can determine nonlinear relationships in a data set by associating the corresponding outputs to input patterns. Therefore, the application of these networks to Traffic Engineering can help achieve its general objective: “intelligent” agents or systems capable of adapting dataflow according to available resources. In this article, we analyze the opportunity and feasibility to apply Artificial Neural Networks to a number of tasks related to Traffic Engineering. In previous sections, we present the basics of each one of these disciplines, which are associated to Artificial Intelligence and Computer Networks respectively

A simulation model for video traffic performance via ATM over TCP/IP

Although TCP has emerged as the standard in data communication, the introduction of ATM technology has raised numerous problems regarding the effectiveness of using TCP over A TM networks, especially when video traffic performance is considered. This paper presents a simulation model for transmission performance of video traffic via ATM over TCP/IP. The interactivity between TCP/IP and ATM, generation of MPEG traffic and evaluation of traffic performance are implemented in the model. The design and implementation details of the model are carefully described. The experiments conducted using the model and experimental results are briefly introduced, revealing the capability of our model in simulating network events and in evaluating potential solutions to performance issues.<br /

Applications of satellite technology to broadband ISDN networks

Two satellite architectures for delivering broadband integrated services digital network (B-ISDN) service are evaluated. The first is assumed integral to an existing terrestrial network, and provides complementary services such as interconnects to remote nodes as well as high-rate multicast and broadcast service. The interconnects are at a 155 Mbs rate and are shown as being met with a nonregenerative multibeam satellite having 10-1.5 degree spots. The second satellite architecture focuses on providing private B-ISDN networks as well as acting as a gateway to the public network. This is conceived as being provided by a regenerative multibeam satellite with on-board ATM (asynchronous transfer mode) processing payload. With up to 800 Mbs offered, higher satellite EIRP is required. This is accomplished with 12-0.4 degree hopping beams, covering a total of 110 dwell positions. It is estimated the space segment capital cost for architecture one would be about $190M whereas the second architecture would be about$250M. The net user cost is given for a variety of scenarios, but the cost for 155 Mbs services is shown to be about $15-22/minute for 25 percent system utilization

Workload Prediction for Efficient Performance Isolation and System Reliability

In large-scaled and distributed systems, like multi-tier storage systems and cloud data centers, resource sharing among workloads brings multiple benefits while introducing many performance challenges. The key to effective workload multiplexing is accurate workload prediction. This thesis focuses on how to capture the salient characteristics of the real-world workloads to develop workload prediction methods and to drive scheduling and resource allocation policies, in order to achieve efficient and in-time resource isolation among applications. For a multi-tier storage system, high-priority user work is often multiplexed with low-priority background work. This brings the challenge of how to strike a balance between maintaining the user performance and maximizing the amount of finished background work. In this thesis, we propose two resource isolation policies based on different workload prediction methods: one is a Markovian model-based and the other is a neural networks-based. These policies aim at, via workload prediction, discovering the opportune time to schedule background work with minimum impact on user performance. Trace-driven simulations verify the efficiency of the two pro- posed resource isolation policies. The Markovian model-based policy successfully schedules the background work at the appropriate periods with small impact on the user performance. The neural networks-based policy adaptively schedules user and background work, resulting in meeting both performance requirements consistently. This thesis also proposes an accurate while efficient neural networks-based pre- diction method for data center usage series, called PRACTISE. Different from the traditional neural networks for time series prediction, PRACTISE selects the most informative features from the past observations of the time series itself. Testing on a large set of usage series in production data centers illustrates the accuracy (e.g., prediction error) and efficiency (e.g., time cost) of PRACTISE. The superiority of the usage prediction also allows a proactive resource management in the highly virtualized cloud data centers. In this thesis, we analyze on the performance tickets in the cloud data centers, and propose an active sizing algorithm, named ATM, that predicts the usage workloads and re-allocates capacity to work- loads to avoid VM performance tickets. Moreover, driven by cheap prediction of usage tails, we also present TailGuard in this thesis, which dynamically clones VMs among co-located boxes, in order to efficiently reduce the performance violations of physical boxes in cloud data centers

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

Resource dimensioning through buffer sampling

Link dimensioning, i.e., selecting a (minimal) link capacity such that the users’ performance requirements are met, is a crucial component of network design. It requires insight into the interrelationship among the traffic offered (in terms of the mean offered load , but also its fluctuation around the mean, i.e., ‘burstiness’), the envisioned performance level, and the capacity needed. We first derive, for different performance criteria, theoretical dimensioning formulas that estimate the required capacity $c$ as a function of the input traffic and the performance target. For the special case of Gaussian input traffic, these formulas reduce to $c = M + \alpha V$, where directly relates to the performance requirement (as agreed upon in a service level agreement) and $V$ reflects the burstiness (at the timescale of interest). We also observe that Gaussianity applies for virtually all realistic scenarios; notably, already for a relatively low aggregation level, the Gaussianity assumption is justified.\ud As estimating $M$ is relatively straightforward, the remaining open issue concerns the estimation of $V$. We argue that particularly if corresponds to small time-scales, it may be inaccurate to estimate it directly from the traffic traces. Therefore, we propose an indirect method that samples the buffer content, estimates the buffer content distribution, and ‘inverts’ this to the variance. We validate the inversion through extensive numerical experiments (using a sizeable collection of traffic traces from various representative locations); the resulting estimate of $V$ is then inserted in the dimensioning formula. These experiments show that both the inversion and the dimensioning formula are remarkably accurate

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

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Resource dimensioning through buffer sampling

Link dimensioning, i.e., selecting a (minimal) link capacity such that the users’ performance requirements are met, is a crucial component of network design. It requires insight into the interrelationship between the traffic offered (in terms of the mean offered load M, but also its fluctuation around the mean, i.e., ‘burstiness’), the envisioned performance level, and the capacity needed. We first derive, for different performance criteria, theoretical dimensioning formulae that estimate the required capacity C as a function of the input traffic and the performance target. For the special case of Gaussian input traffic these formulae reduce to C = M+V , where directly relates to the performance requirement (as agreed upon in a service level agreement) and V reflects the burstiness (at the timescale of interest). We also observe that Gaussianity applies for virtually all realistic scenarios; notably, already for a relatively low aggregation level the Gaussianity assumption is justified.\ud As estimating M is relatively straightforward, the remaining open issue concerns the estimation of V . We argue that, particularly if V corresponds to small time-scales, it may be inaccurate to estimate it directly from the traffic traces. Therefore, we propose an indirect method that samples the buffer content, estimates the buffer content distribution, and ‘inverts’ this to the variance. We validate the inversion through extensive numerical experiments (using a sizeable collection of traffic traces from various representative locations); the resulting estimate of V is then inserted in the dimensioning formula. These experiments show that both the inversion and the dimensioning formula are remarkably accurate

Adaptive Neural Network Controller for ATM Traffic

Broadband-Integrated Services Digital Networks (B-ISDN), along with Asynchronous Transfer Mode (ATM), were designed to meet the requirements of modern communication networks to handle multiple users and a wide variety of diverse traffic including voice, data and video. ATM responds to requests for admission to the network by analyzing whether or not the grade of service (GOS) requirement, specified in the admission request, can be guaranteed without violating the GOS guaranteed to traffic already accepted into the network. The GOS is typically a parameter such as cell loss rate (CLR), average delay, or some other measurement associated with network performance. In order to develop a tractable mathematical algorithm for controlling admission, an accurate model of the communication network and traffic in question is necessary. The complex and dynamic nature of these communication networks make them very difficult to model. Even when such a model can be developed, often with unrealistic simplifications or unsupportable assumptions, the associated mathematical algorithm is frequently excessively cumbersome and timely processing of an admission request is lost. An alternative to conventional mathematical algorithms for cases like these is the use of neural networks (NN). NNs can learn complicated functions relating the inputs and outputs of a system without prior knowledge about the system itself. For ATM B-ISDN networks, NNs can learn the function relating input traffic parameters and resulting network performance by training on an appropriate set of traffic parameter inputs and resulting GOS outputs. In this work three neural network admission controller schemes are examined. The Bayes error rate, as bounded by the Parzen window technique, is also introduced as a benchmark for measuring the performance of these admission controllers