On the Use of Queueing Petri Nets for Modeling and Performance Analysis of Distributed Systems

Predictive performance models are used increasingly throughout the phases of the software engineering lifecycle of distributed systems. However, as systems grow in size and complex-ity, building models that accurately capture the different aspects of their behavior becomes a more and more challenging task. The challenge stems from the limited model expressivenes

Sorbanállási rendszerek szimulációja

Sorbanállási rendszerek szimulációja Java alapon az SSJ keretrendszer segítségévelB

Response time distribution in a tandem pair of queues with batch processing

Response time density is obtained in a tandem pair of Markovian queues with both batch arrivals and batch departures. The method uses conditional forward and reversed node sojourn times and derives the Laplace transform of the response time probability density function in the case that batch sizes are finite. The result is derived by a generating function method that takes into account that the path is not overtake-free in the sense that the tagged task being tracked is affected by later arrivals at the second queue. A novel aspect of the method is that a vector of generating functions is solved for, rather than a single scalar-valued function, which requires investigation of the singularities of a certain matrix. A recurrence formula is derived to obtain arbitrary moments of response time by differentiation of the Laplace transform at the origin, and these can be computed rapidly by iteration. Numerical results for the first four moments of response time are displayed for some sample networks that have product-form solutions for their equilibrium queue length probabilities, along with the densities themselves by numerical inversion of the Laplace transform. Corresponding approximations are also obtained for (non-product-form) pairs of “raw” batch-queues – with no special arrivals – and validated against regenerative simulation, which indicates good accuracy. The methods are appropriate for modeling bursty internet and cloud traffic and a possible role in energy-saving is considered

Feature Selection and Classifier Development for Radio Frequency Device Identification

The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection

Performance of sequential batching-based methods of output data analysis in distributed steady-state stochastic simulation

Wir haben die Anpassung von Sequentiellen Analysemethoden von Stochastik Simulationen an einem Szenario von mehreren Unabhängigen Replikationen in Parallel (MRIP) untersucht. Die Hauptidee ist, die statistische Kontrole bzw. die Beschleunigung eines Simulationexperiment zu automatisieren. Die vorgeschlagenen Methoden der Literatur sind auf einzelne Prozessorszenarien orientiert. Wenig ist bekannt hinsichtlich der Anwendungen von Verfahen, die auf Methoden unter MRIP basieren. Auf den ersten Blick sind beide Ziele entgegengesetzt, denn man braucht eine grosse Menge von Beobachtungen, um eine hohe Qualität der Resultate zu erreichen. Dafür benötig man viel Zeit. Man kann jedoch durch einen ausfürlichen Entwurf zusammen mit einem robusten Werkzeug, das auf unabhängige Replikationen basiert ist, ein effizientes Mittel bezüglich Analyse der Resultate produzieren. Diese Recherche wurde mit einer sequentiellen Version des klassischen Verfahren von Nonoverlaping Batch Means (NOBM) angefangen. Obwohl NOBM sehr intuitiv und populär ist, bietet es keine gute Lösung für das Problem starker Autokorrelation zwischen den Beobachtungen an, die normalerweise bei hohen Auslastungen entstehen. Es lohnt sich nicht, grösserer Rechnerleistung zu benutzen, um diese negative Merkmale zu vermindern. Das haben wir mittles einer vollständigen Untersuchung einer Gruppe von Warteschlangsystemen bestätig. Deswegen haben wir den Entwurf von sequentiellen Versionen von ein paar Varianten von Batch Means vorgeschlagen und sie genauso untersucht. Unter den implementierten Verfahren gibt es ein sehr attraktives: Overlapping Batch Means (OBM). OBM ermöglicht eine bessere Nutzung der Daten, da jede Beobachtungen ein neues Batch anfängt, d.h., die Anzahl von Batches ist viel grösser, und das ergibt eine kleinere Varianz. In diesem Fall ist die Anwendung von MRIP empfehlenswert, da diese Kombination weniger Beobachtungen benötigt und somit eine höhere Beschleunigung. Im Laufe der Recherche haben wir eine Klasse von Methoden (Standardized Time Series - STS) untersucht, die teoretisch bessere asymptotische Resultate als NOBM produziert. Die negative Auswirkung von STS ist, dass sie mehr Beobachtungen als die Batch-Means-Verfahren benoetigt. Aber das ist kein Hindernis, wenn wir STS zusammen mit MRIP anwenden. Die experimentelle Untersuchungen bestätigte, dass die Hypothese richtig ist. Die nächste Phase war es, OBM und STS einzustellen, um beide Verfahren unter den grösstmöglichen Anzahl von Prozessoren arbeiten lassen zu können. Fallstudien zeigten uns, dass sich beide sequentiellen Verfahren für die parallele Simulation sowie MRIP einigen.We investigated the feasibility of sequential methods of analysis of stochastic simulation under an environment of Multiple Replications in Parallel (MRIP). The main idea is twofold, the automation of the statistical control and speedup of simulation experiments. The methods of analysis found suggested in the literature were conceived for a single processor environment. Very few is known concerning the application of procedures based in such methods under MRIP. At first glance, sind both goals in opposition, since one needs a large amount of observations in order to achieve good quality of the results, i.e., the simulation takes frequently long time. However, by means of a careful design, together with a robust simulation tool based on independent replications, one can produce an efficient instrument of analysis of the simulation results. This research began with a sequential version of the classical method of Nonoverlapping Batch Means (NOBM). Although intuitiv and popular, under hight traffic intensity NOBM offers no good solution to the problem of strong correlation among the observations. It is not worthwhile to apply more computing power aiming to diminish this negative effect. We have confirmed this claim by means of a detailed and exhaustive analysis of four queuing systems. Therefore, we proposed the design of sequential versions of some Batch Means variants, and we investigated their statistical properties under MRIP. Among the implemented procedures there is one very attractive : Overlapping Batch Means (OBM). OBM makes a better use of collected data, since each observation initiates a new (overlapped) batch, that is, die number of batches is much larger, and this yields smaller variance. In this case, MRIP is highly recommended, since this combination requires less observations and, therefore, speedup. During the research, we investigated also a class of methods based on Standardized Time Series -- STS, that produces theoretically better asymptotical results than NOBM. The undesired negative effect of STS is the large number of observations it requires, when compared to NOBM. But that is no obstacle when we apply STS together with MRIP. The experimental investigation confirmed this hypothesis. The next phase was to tun OBM and STS, in order to put them working with the possible largest number of processors. A case study showed us that both procedures are suitable to the environment of MRIP

On automated sequential steady-state simulation.

The credibility of the final results from stochastic simulation has had limited discussion in the simulation literature so far. However, it is important that the final results from any simulations be credible. To achieve this, validation, which determines whether the conceptual simulation model is an accurate representation of the system under study, has to be done carefully. Additionally, a proper statistical analysis of simulation output data, including a confidence interval or other assessment of statistical errors, has to be conducted before any valid inferences or conclusions about the performance of simulated dynamic systems, such as for example telecommunication networks, are made. There are many other issues, such as choice of a good pseudo-random number generator, elimination of initialisation bias in steady-state simulations, and consideration of auto correlations in collected observations, which have to be appropriately addressed for the final results to be credible. However, many of these issues are not trivial, particularly for simulation users who may not be experts in these areas. As a consequence, a fully-automated simulation package, which can control all important aspects of stochastic simulation, is needed. This dissertation focuses on the following contributions to such a package for steady-state simulation: properties of confidence intervals (CIs) used in coverage analysis, heuristic rules for improving the coverage of the final CIs in practical applications, automated sequential analysis of mean values by the method of regenerative cycles, automatic detection of the initial transient period for steady-state quantile estimation, and sequential steady-state quantile estimation with the automated detection of the length of initial transient period. One difficulty in obtaining precise estimates of a system using stochastic simulation can be the cost of the computing time needed to collect the large amount of output data required. Indeed there are situations, such as estimation of rare events, where, even assuming an appropriate statistical analysis procedure is available, the cost of collecting the number of observations needed by the analysis procedure can be prohibitively large. Fortunately, inexpensive computer network resources enable computationally intensive simulations by allowing us to run parallel and distributed simulations. Therefore, where possible, we extend the contributions to the distributed stochastic simulation scenario known as the Multiple Replications In Parallel (MRIP), in which multiple processors run their own independent replications of the simulated system but cooperate with central analysers that collect data to estimate the final results