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

Performance analysis of an asynchronous transfer mode multiplexer with Markov modulated inputs

Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 1993.Thesis (Ph.D.) -- Bilkent Iniversity, 1993.Includes bibliographical references leaves 108-113.Asynchronous Transfer Mode (ATM) networks have inputs which consist of superpositions of correlated cell streams. Markov modulated processes are commonly used to characterize this correlation. The first step through gaining an analytical insight in the performance issues of an ATM network is the analysis of a single channel. One objective of this study is the performance analysis of an ATM multiplexer whose input is a Markov modulated periodic arrival process. Based on the transient behavior of the nD/D/1 queue, we present an approximate method to compute the queue length distribution accurately. The method reduces to the solution of a linear differential equation with variable coefficients. Another general traffic model is the Markov Modulated Poisson Process (MMPP). We employ Pade approximations in transform domain for the deterministic service time distribution in an M MPP/D/1 queue so as to compute the distribution of the buffer occupancy. For both models, we also provide algorithms for analysis in the case of finite queue capacities and for computation of effective bandwidth.Akar, NailPh.D

Stochastic Dynamic Programming and Stochastic Fluid-Flow Models in the Design and Analysis of Web-Server Farms

A Web-server farm is a specialized facility designed specifically for housing Web servers catering to one or more Internet facing Web sites. In this dissertation, stochastic dynamic programming technique is used to obtain the optimal admission control policy with different classes of customers, and stochastic uid- ow models are used to compute the performance measures in the network. The two types of network traffic considered in this research are streaming (guaranteed bandwidth per connection) and elastic (shares available bandwidth equally among connections). We first obtain the optimal admission control policy using stochastic dynamic programming, in which, based on the number of requests of each type being served, a decision is made whether to allow or deny service to an incoming request. In this subproblem, we consider a xed bandwidth capacity server, which allocates the requested bandwidth to the streaming requests and divides all of the remaining bandwidth equally among all of the elastic requests. The performance metric of interest in this case will be the blocking probability of streaming traffic, which will be computed in order to be able to provide Quality of Service (QoS) guarantees. Next, we obtain bounds on the expected waiting time in the system for elastic requests that enter the system. This will be done at the server level in such a way that the total available bandwidth for the requests is constant. Trace data will be converted to an ON-OFF source and fluid- flow models will be used for this analysis. The results are compared with both the mean waiting time obtained by simulating real data, and the expected waiting time obtained using traditional queueing models. Finally, we consider the network of servers and routers within the Web farm where data from servers flows and merges before getting transmitted to the requesting users via the Internet. We compute the waiting time of the elastic requests at intermediate and edge nodes by obtaining the distribution of the out ow of the upstream node. This out ow distribution is obtained by using a methodology based on minimizing the deviations from the constituent in flows. This analysis also helps us to compute waiting times at different bandwidth capacities, and hence obtain a suitable bandwidth to promise or satisfy the QoS guarantees. This research helps in obtaining performance measures for different traffic classes at a Web-server farm so as to be able to promise or provide QoS guarantees; while at the same time helping in utilizing the resources of the server farms efficiently, thereby reducing the operational costs and increasing energy savings

Buffer management and cell switching management in wireless packet communications

The buffer management and the cell switching (e.g., packet handoff) management using buffer management scheme are studied in Wireless Packet Communications. First, a throughput improvement method for multi-class services is proposed in Wireless Packet System. Efficient traffic management schemes should be developed to provide seamless access to the wireless network. Specially, it is proposed to regulate the buffer by the Selective- Delay Push-In (SDPI) scheme, which is applicable to scheduling delay-tolerant non-real time traffic and delay-sensitive real time traffic. Simulation results show that the performance observed by real time traffics are improved as compared to existing buffer priority scheme in term of packet loss probability. Second, the performance of the proposed SDPI scheme is analyzed in a single CBR server. The arrival process is derived from the superposition of two types of traffics, each in turn results from the superposition of homogeneous ON-OFF sources that can be approximated by means of a two-state Markov Modulated Poisson Process (MMPP). The buffer mechanism enables the ATM layer to adapt the quality of the cell transfer to the QoS requirements and to improve the utilization of network resources. This is achieved by selective-delaying and pushing-in cells according to the class they belong to. Analytical expressions for various performance parameters and numerical results are obtained. Simulation results in term of cell loss probability conform with our numerical analysis. Finally, a novel cell-switching scheme based on TDMA protocol is proposed to support QoS guarantee for the downlink. The new packets and handoff packets for each type of traffic are defined and a new cutoff prioritization scheme is devised at the buffer of the base station. A procedure to find the optimal thresholds satisfying the QoS requirements is presented. Using the ON-OFF approximation for aggregate traffic, the packet loss probability and the average packet delay are computed. The performance of the proposed scheme is evaluated by simulation and numerical analysis in terms of packet loss probability and average packet delay

Performance Analysis of an ATM MUX with a New Space Priority Mechanism under ON-OFF Arrival Processes

Abstract: We propose a new space priority mechanism, and analyze its performance in a single Constant Bit Rate (CBR) server. The arrival process is derived from the superposition of two types of traffics, each in turn results from the superposition of homogeneous ON-OFF sources that can be approximated by means of a two-state Markov Modulated Poisson Process (MMPP). The buffer mechanism enables the Asynchronous Transfer Mode (ATM) layer to adapt the quality of the cell transfer to the Quality of Service (QoS) requirements and to improve the utilization of network resources. This is achieved by "Selective-Delaying and Pushing-In" (SDPI) cells according to the class they belong to. The scheme is applicable to schedule delay-tolerant non-real time traffic and delaysensitive real time traffic. Analytical expressions for various performance parameters and numerical results are obtained. Simulation results in term of cell loss probability conform with our numerical analysis

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

Center for Aeronautics and Space Information Sciences

This report summarizes the research done during 1991/92 under the Center for Aeronautics and Space Information Science (CASIS) program. The topics covered are computer architecture, networking, and neural nets