A Performance Model for the Link-Transport Layer Serving XTP in a High Speed Network

This paper presents a traffic model for an ATM multiplexer providing video, voice, image, and data services. The traffic model classifies the input traffic into two types: real-time and non-real-time. The input process for real-time traffic is a Markov modulated Bernoulli process (MMBP), while that for non-real-time traffic is a Bernoulli process with batches. The z-transform of the cell interarrival time A/sub MMBP/(z) is obtained. The average traffic load /spl rho//sub MMBP/ and the squared coefficient of variation of the time between success cell C/sup 2//sub MMBP/ are then obtained from the respective derivatives of A/sub MMBP/(z). The autocorrelation coefficient of the cell interarrival time with lag 1 /spl psi/, can be also obtained. This paper presents an iterative algorithm that can be used to calculate the MMBP probability distribution of the cell interarrival time. So far few results about discrete-time finite buffer queues with correlated inputs are known. This paper analyzes the performance of an ATM multiplexer considering the correlation and burst in cell arrivals. For the MMBP-stream, the waiting time distribution and the cell loss probability for MMBP+M/sup [X]//D/1/K finite buffer system are all exactly calculated with the use of a recursive algorithm. Calculations show the influence of burstiness and correlation on both the loss probability and waiting time. The accuracy of the analytical results has been largely validated by means of a simulation approach.

The layer3 switch enables us to fast transmit IP datagrams using the cut-through technique. The current layer3 router would become a bottleneck in terms of delay performance, as the amount of traffic injected into high speed networks gets relatively large. Thus, the layer3 switch should be an important element in constructing the next generation Internet backbone. We analyze the cut-through rate, the datagram waiting time and the mis-ordered rate of a layer3 switch in case of flow-driven connection setup. In the analysis, by using a 3-state Markov modulated Bernoulli process (MMBP), we model the arrival process of IP flow and IP datagram from each source. Furthermore, we investigate impacts of the arrival rate and the average datagram length on performance.

This paper presents analyses to study the persession performance of an ATM multiplexer which finds application in call admission control (CAC). We consider a scenario where a switching node is non-blocking and output buffered, links are of different speeds and the traffic is bursty with correlated arrivals. We model the session of interest as a Markov modulated Bernoulli process (MMBP) and the interfering traffic as an MMBP with batch arrivals. We propose an efficient method to obtain the exact steady-state performance results of a session. An approximation technique is also presented. This method could also be used to analyze any Markov renewal traffic model such as discrete-batch Markovian arrival process (D-BMAP), and study the transient behavior of a session.

In ATM networks, the time axis is divided into equal intervals called time slots, so most references assume that the Markov chain makes a transition from state i to state j in the boundary of time slots in view of Markov-modulated Bernoulli process (MMBP). This paper considers a more general situation, i.e., the Markov chain makes a transition from state i to state j in the boundary of frames whose length are an integral multiple of length of one time slot. Under this MMBP model, the paper develops a four-dimensional discrete-time Markov chain, and obtains results of the performance analysis of leaky bucket algorithms.

In the ATM network, the leaky bucket scheme is a promising method that regulates input traffics for preventive congestion control. In order to satisfy the loss probability constraint of more bursty input traffic, it is known that the leaky bucket scheme with static token generation interval requires larger data buffer and token pool size. This causes an increase in the mean waiting time for input traffic to pass the leaky bucket scheme, which would be inappropriate for real time traffics such as voice and video. The authors present the leaky bucket scheme with threshold-based token generation intervals in which the token generation interval changes according to buffer occupancy. The proposed leaky bucket scheme is analysed in the discrete-time case by assuming the arrival process to be a Markov-modulated Bernoulli process (MMBP). The authors show that the quality of service (QoS) of input traffics is satisfied with smaller data buffer and token pool size in the proposed leaky bucket scheme.

In this paper, we examine the queuing performance of terminals in a multichannel centralized S-ALOHA system with a finite terminal population and finite queue size in each terminal, employing a uniform backoff (UB) algorithm with retry limit for collision resolution. The performance evaluations focus on uplink traffic from web browsing, which is modeled as a Markov-modulated Bernoulli process with correlated arrivals. We analyze the system performance in terms of system throughput, mean queue length, mean delay, the probability that a packet is dropped by retry limit, the probability that a packet is blocked by a full queue, and the mean and variance of packet retransmission time, in relation to the source correlation, number of channels, window size, and retry limit. For comparison, we also consider bufferless terminals with correlated arrivals. In addition, we evaluate by simulations the performance of terminals with finite queue size when they have perfect knowledge of the backlog size. Results from our study allow the parameters of the UB algorithm to properly be chosen to meet the access-level quality-of-service requirements.

Burstiness in broadband integrated networks

We consider simple models of congestion control in high-speed networks, and develop diffusion approximations which could be useful for resource allocation. We first show that, if the sources are ON-OFF type with exponential ON and OFF times, then, under a certain scaling, the steady-state distribution of the number of active sources can be described by a combination of two appropriately truncated and renormalized normal distributions. For the case where the source arrival process is Poisson and the service times are exponential, the steady-state distribution consists of appropriately normalized and truncated Gaussian and exponential distributions. We then consider the case where the arrival process is a general renewal process with finite coefficient of variation and service-time distributions that are phase type, and show the impact of these distributions on the steady-state distribution of the number of sources in the system. We also establish an insensitivity to service-time distribution when the arrival process is Poisson. We use these results to relate the capacity of a bottleneck node to performance measures of interest for best effort traffic.

As the packet/cell loss ratio (PLR/CLR) is counted as one of the major quality of service (QoS) requirements; in this paper we proposed a linear upper bound expression for the loss probability approximation in high-speed communication network technologies. These technologies can support multiple classes of traffic with different QoS requirements and diverse traffic characteristics, by the use of traffic multiplexing. The proposed expression can be calculated in real-time, so it can be properly used in the network multiplexers. Based on the fact that in many cases, the real traffic in high-speed networks shows a self-similar behavior, we have used self-similar traffic with Pareto distribution as the input traffic model in our PLR approximation simulations. We have studied application of this expression in non-homogeneous as well as homogeneous cases.

Instantaneous traffic changes in high-speed networks will interfere with abnormal traffic characteristics, making it difficult to accurately identify hidden targets of security threats. This paper designs a high-speed network security threat hidden target recognition method based on attack graph theory. Using the high-speed network traffic reduction method, under the condition that the network topology remains unchanged, the instantaneous input traffic is reduced according to a certain proportion, and after compressing the flow data scale, the abnormal traffic of the high-speed network is identified through the convolutional recurrent neural network, and the information entropy is used to describe the high-speed network. The abnormal traffic characteristics of the network are used as constraints to design an attack graph of hidden targets of high-speed network security threats, and an attack path discovery method based on multi-heuristic information fusion is designed to extract attack paths of high-speed networks, locate attacking hosts, and identify hidden threat targets. In the experiment, the method can accurately identify the hidden targets of high-speed network security threats, and has better identification ability.

In this article we propose methods to estimate the parameters of arrival processes to G/ D/1 queueing systems only based on observed departures from the system. The derived estimates can be used for performance evaluation and capacity planning in cases where the arrival process is not observable directly. Instead, only the departure process modified at the (last) server needs to be observed. Concerning the arrival process we begin by focusing on the normally distributed number of arrivals per interval and on autoregressive processes. Both classes can be used to model traffic in communication networks on links with a high degree of aggregation. In the case of autoregressive processes we apply the Tobit regression model in order to derive accurate estimates of all parameters of the arrival process. We then continue by generalizing the estimation procedures to correlated arrival processes by using the Buckley–James Estimator. The results are presented for the single G/ D/1 queue but are then generalized to the more realistic scenario of sequences of queues with possibly varying bottleneck capacity. The latter especially permits the modeling of effects due to interfering cross traffic. We show how the derived estimates can be used for performance evaluation or capacity planning based on effective bandwidth theory. Finally, we demonstrate that the estimation procedures can be utilized within wireless networks by means of a detailed simulation model.

Suggestions of efficient self-similar generators

Performance analysis of statistical multiplexing of heterogeneous discrete-time Markovian arrival processes in an ATM network

Impact of high speed network on performance of real-time concurrency control protocol

Hop-by-hop bandwidth allocation and deployment for SFC with end-to-end delay QoS guarantees