On the Queue Length Distribution in BMAP Systems

Batch Markovian Arrival Process – BMAP – is a teletraffic model which combines high ability to imitate complex statistical behaviour of network traces with relative simplicity in analysis and simulation. It is also a generalization of a wide class of Markovian processes, a class which in particular include the Poisson process, the compound Poisson process, the Markovmodulated Poisson process, the phase-type renewal process and others. In this paper we study the main queueing performance characteristic of a finite-buffer queue fed by the BMAP, namely the queue length distribution. In particular, we show a formula for the Laplace transform of the queue length distribution. The main benefit of this formula is that it may be used to obtain both transient and stationary characteristics. To demonstrate this, several numerical results are presented

A Unified Method of Analysis for Queues with Markovian Arrivals

We deal with finite-buffer queueing systems fed by a Markovian point process. This class includes the queues of type M/G/1/N, /G/1/N, PH/G/1/N, MMPP/G/1/N, MAP/G/1/N, and BMAP/G/1/N and is commonly used in the performance evaluation of network traffic buffering processes. Typically, such queueing systems are studied in the stationary regime using matrix-analytic methods connected with M/G/1-type Markov processes. Herein, another method for finding transient and stationary characteristics of these queues is presented. The approach is based on finding a closed-form formula for the Laplace transform of the time-dependent performance measure of interest. The method can be used for finding all basic characteristics like queue size distribution, workload distribution, loss ratio, time to buffer overflow, and so forth. To demonstrate this, several examples for different combinations of arrival processes and characteristics are presented. In addition, the most complex results are illustrated via numerical calculations based on an IP traffic parameterization

Packet dropping characteristics in a queue with autocorrelated arrivals

This paper provides a detailed description of the packet dropping process connected with the buffer overflows in a network node. Namely, we show the formulas for the most important loss characteristics, both in the transient and the stationary regime and then illustrate them via numericalexamples. In order to make it possible to obtain the droppingcharacteristics for strongly autocorrelated arrivals, the Markovmodulated Poisson process is used as a traffic model

Buffer overflow period in a batch-arrival queue with autocorrelated arrivals

In this paper a study on the buffer overflow period in a finite-buffer queue is presented. A special attention is paid to the autocorrelation and batch arrivals, which are supposed to mimic the properties of real arrival processes. Using the batch Markovian arrival process, formulas for the duration of the first, subsequent and stationary overflow periods, as well as formulas for the distribution of the number of consecutive losses during the overflow period are shown. Moreover, analytical results are illustrated via numerical examples. In particular, the influence of the autocorrelation and batch arrivals on the duration of the overflow period is demonstrated

Buffer Overflow Period in a MAP Queue

The buffer overflow period in a queue with Markovian arrival process (MAP) and general service time distribution is investigated. The results include distribution of the overflow period in transient and stationary regimes and the distribution of the number of cells lost during the overflow interval. All theorems are illustrated via numerical calculations

Impact of the Dropping Function on Clustering of Packet Losses

The dropping function mechanism is known to improve the performance of TCP/IP networks by reducing queueing delays and desynchronizing flows. In this paper, we study yet another positive effect caused by this mechanism, i.e., the reduction in the clustering of packet losses, measured by the burst ratio. The main contribution consists of two new formulas for the burst ratio in systems with and without the dropping function, respectively. These formulas enable the easy calculation of the burst ratio for a general, non-Poisson traffic, and for an arbitrary form of the dropping function. Having the formulas, we provide several numerical examples that demonstrate their usability. In particular, we test the effect of the dropping function’s shape on the burst ratio. Several shapes of the dropping function proposed in the literature are compared in this context. We also demonstrate, how the optimal shape can be found in a parameter-depended class of functions. Finally, we investigate the impact of different system parameters on the burst ratio, including the load of the system and the variance of the service time. The most important conclusion drawn from these examples is that it is not only the dropping function that reduces the burst ratio by far; simultaneously, the more variable the traffic, the more beneficial the application of the dropping function

Output Stream from the AQM Queue with BMAP Arrivals

We analyse the output stream from a packet buffer governed by the policy that incoming packets are dropped with a probability related to the buffer occupancy. The results include formulas for the number of packets departing the buffer in a specific time, for the time-dependent output rate and for the steady-state output rate. The latter is the key performance measure of the buffering mechanism, as it reflects its ability to process a specific number of packets in a time unit. To ensure broad applicability of the results in various networks and traffic types, a powerful and versatile model of the input stream is used, i.e., a BMAP. Numeric examples are provided, with several parameterisations of the BMAP, dropping probabilities and loads of the system

Queues with Dropping Functions and General Arrival Processes.

In a queueing system with the dropping function the arriving customer can be denied service (dropped) with the probability that is a function of the queue length at the time of arrival of this customer. The potential applicability of such mechanism is very wide due to the fact that by choosing the shape of this function one can easily manipulate several performance characteristics of the queueing system. In this paper we carry out analysis of the queueing system with the dropping function and a very general model of arrival process--the model which includes batch arrivals and the interarrival time autocorrelation, and allows for fitting the actual shape of the interarrival time distribution and its moments. For such a system we obtain formulas for the distribution of the queue length and the overall customer loss ratio. The analytical results are accompanied with numerical examples computed for several dropping functions