1,720,962 research outputs found
An Importance Sampling Algorithm for the Simulation of a GPS scheduler
No full textA key requirement of modern telecommunications networks is the capability of providing a variety of Quality of Service (QoS) guarantees to different classes of users. In this scenario scheduling systems play a major role, thanks to their ability of offering different levels of service, while preventing some "greedy users" from degrading the QoS of other classes. A primary QoS parameter is the Cell Loss Probability (CLP), whose typical values are very small and therefore difficult to estimate through standard Monte Carlo (MC) simulation, since long run times are required to achieve accurate results. Under proper conditions. Importance Sampling (IS) techniques can speed up simulations involving rare events. In this paper we propose an application of IS to the simulation of a Generalized Processor Sharing (GPS) scheduler, fed by Markov Arrival Processes (MAP). The algorithms we propose are based on large deviations principles, which provide asymptotic results on the decay rate of per-session queue length tail distributions in an idealized GPS discipline. In particular, we first present a scheme for the simulation of a two-buffer system, then we extend the technique to the general case of a multi-buffer scheduler. We subsequently employ our algorithms to simulate a packetized realistic system, namely the Packet-by-packet Generalized Processor Sharing
Efficiency of Importance Sampling Technique for Queueing Performance Evaluation with LRD Traffic
No full textFuture B-ISDN networks, based on asynchronous transfer mode (ATM), will be able to offer services with statistically guaranteed quality of service (QoS). A typical target is a very low cell loss probability, which raises the problem of rare events probability estimation. Moreover, experimental measurements highlighted the bursty nature of real data paths, which cannot be captured by traditional Markovian models, while this behaviour is web described by self-similar processes. To cope with these two issues, in this work we applied importance sampling (IS) techniques to the simulation of realistic broadband traffic, In particular we pointed out the effectiveness of a heuristic IS approach in evaluating the performances of a single server queueing system, loaded by realistic traffic traces, that is taking into account the bursty behaviour of real data paths
Efficient Estimation of Buffer Occupancy in ATM Systems Loaded by Self-Similar Traffic
No full textB-ISDN networks based on Asynchronous Transfer Mode (ATM) are characterized by very low cell loss probability. This results in an enormous computational burden when determining a system performance via Monte Carlo simulation. To improve computational efficiency different methods (for example Restart and GEVT) have been proposed in literature to evaluate the occurrence of rare events. In this work we show how Importance Sampling (IS) allows to obtain useful results in this field. While IS techniques have been largely exploited in the evaluation of bit error rate in digital communication systems and of false alarm rate in radar devices very little has been done to develop efficient IS-based schemes to simulate a queueing system fed with self-similar traffic. With this work we mean to show how IS may be applied to simulate a queue fed both with Fractional Gaussian Noise (FGN) and with synthetic traces with characteristics close to the ones of real traffic, resulting from videoconferencing sessions
A Wavelet-based approach to the estimation of the Hurst Parameter for self-similar data
No full textIn this paper we analyze a wavelet based method for the estimation of the Hurst parameter of synthetically-generated self-similar traces, widely used in a great variety of applications, ranging from computer graphics to parsimonious traffic modelling in broadband networks. The aim of this work is to point out the efficiency of multiresolution schemes in the analysis of fractal processes, characterized by similar statistical features over different time scales. To this end we generated a huge amount of data using tile Random Midpoint Displacement (RMD) algorithm, a well-known fast technique for the generation of the fractional Gaussian noise (fGn) traces. We then evaluated the Hurst parameter of such sequences in the wavelet domain and compared the results with those obtained with more traditional methods, based on the estimation of the fractal dimension (Higuchi method) and the moments of the aggregated series
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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