694 research outputs found

    Aggregate matrix-analytic techniques and their applications

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    The complexity of computer systems affects the complexity of modeling techniques that can be used for their performance analysis. In this dissertation, we develop a set of techniques that are based on tractable analytic models and enable efficient performance analysis of computer systems. Our approach is three pronged: first, we propose new techniques to parameterize measurement data with Markovian-based stochastic processes that can be further used as input into queueing systems; second, we propose new methods to efficiently solve complex queueing models; and third, we use the proposed methods to evaluate the performance of clustered Web servers and propose new load balancing policies based on this analysis.;We devise two new techniques for fitting measurement data that exhibit high variability into Phase-type (PH) distributions. These techniques apply known fitting algorithms in a divide-and-conquer fashion. We evaluate the accuracy of our methods from both the statistics and the queueing systems perspective. In addition, we propose a new methodology for fitting measurement data that exhibit long-range dependence into Markovian Arrival Processes (MAPs).;We propose a new methodology, ETAQA, for the exact solution of M/G/1-type processes, (GI/M/1-type processes, and their intersection, i.e., quasi birth-death (QBD) processes. ETAQA computes an aggregate steady state probability distribution and a set of measures of interest. E TAQA is numerically stable and computationally superior to alternative solution methods. Apart from ETAQA, we propose a new methodology for the exact solution of a class of GI/G/1-type processes based on aggregation/decomposition.;Finally, we demonstrate the applicability of the proposed techniques by evaluating load balancing policies in clustered Web servers. We address the high variability in the service process of Web servers by dedicating the servers of a cluster to requests of similar sizes and propose new, content-aware load balancing policies. Detailed analysis shows that the proposed policies achieve high user-perceived performance and, by continuously adapting their scheduling parameters to the current workload characteristics, provide good performance under conditions of transient overload

    Glosarium Matematika

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    273 p.; 24 cm

    Glosarium Matematika

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    Non-expected utility vs. expected utility theory in consumption/savings decisions over the life cycle

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    Diese Dissertation beschäftigt sich mit der Frage, ob die Rank-Dependent Utility Theorie oder die Cumulative Prospect Theorie, welche zu den sogenannten Nicht-Erwartungsnutzen Theorien gehören, reale Daten über Konsum- und Sparentscheidungen besser abbilden können als die Erwartungsnutzentheorie. Wir verwenden ein Konsum/Spar Modell and die Methode der Simulierten Momente um zwei Parameter zu schätzen, welche in unserem Aufbau die Rank-Dependent Utility Theory, die Cumulative Prospect Theorie und die Erwartungsnutzentheorie unterscheiden. Wir schlagen für das Auflösen des Modells eine Methode vor, für welche die Kritik hinsichtlich dem Gebrauch von Nicht-Erwartungsnutzen Theorien in dynamischen Modellen nicht zutreffend ist. Das Endergebnis ist, dass die Rank-Dependent Utility Theorie die Daten am besten abbilden kann.This thesis deals with the question if Rank-Dependent Utility or Cumulative Prospect Theory, belonging to the so called Non-Expected Utility models, are better in explaining real life data on consumption and savings decisions than Expected Utility Theory. We use a consumption/savings model and the Method of Simulated Moments to estimate two parameters, which distinguish Expected Utility, Rank-Dependent Utility and Cumulative Prospect Theory in our setting. For the solution of the model we propose a method to which the conventional critique on Non-Expected Utility in dynamic settings does not apply. Our main finding is that Rank-Dependent Utility Theory is the theory which fits the data best

    Stochastic analysis of nonlinear dynamics and feedback control for gene regulatory networks with applications to synthetic biology

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    The focus of the thesis is the investigation of the generalized repressilator model (repressing genes ordered in a ring structure). Using nonlinear bifurcation analysis stable and quasi-stable periodic orbits in this genetic network are characterized and a design for a switchable and controllable genetic oscillator is proposed. The oscillator operates around a quasi-stable periodic orbit using the classical engineering idea of read-out based control. Previous genetic oscillators have been designed around stable periodic orbits, however we explore the possibility of quasi-stable periodic orbit expecting better controllability. The ring topology of the generalized repressilator model has spatio-temporal symmetries that can be understood as propagating perturbations in discrete lattices. Network topology is a universal cross-discipline transferable concept and based on it analytical conditions for the emergence of stable and quasi-stable periodic orbits are derived. Also the length and distribution of quasi-stable oscillations are obtained. The findings suggest that long-lived transient dynamics due to feedback loops can dominate gene network dynamics. Taking the stochastic nature of gene expression into account a master equation for the generalized repressilator is derived. The stochasticity is shown to influence the onset of bifurcations and quality of oscillations. Internal noise is shown to have an overall stabilizing effect on the oscillating transients emerging from the quasi-stable periodic orbits. The insights from the read-out based control scheme for the genetic oscillator lead us to the idea to implement an algorithmic controller, which would direct any genetic circuit to a desired state. The algorithm operates model-free, i.e. in principle it is applicable to any genetic network and the input information is a data matrix of measured time series from the network dynamics. The application areas for readout-based control in genetic networks range from classical tissue engineering to stem cells specification, whenever a quantitatively and temporarily targeted intervention is required

    A Queueing Model to Study Ambulance Offload Delays

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    The ambulance offload delay problem is a well-known result of overcrowding and congestion in emergency departments. Offload delay refers to the situation where area hospitals are unable to accept patients from regional ambulances in a timely manner due to lack of staff and bed capacity. The problem of offload delays is not a simple issue to resolve and has caused severe problems to the emergency medical services (EMS) providers, emergency department (ED) staff, and most importantly patients that are transferred to hospitals by ambulance. Except for several reports on the problem, not much research has been done on the subject. Almost all research to date has focused on either EMS or ED planning and operation and as far as we are aware there are no models which have considered the coordination of these units. We propose an analytical model which will allow us to analyze and explore the ambulance offload delay problem. We use queuing theory to construct a system representing the interaction of EMS and ED, and model the behavior of the system as a continuous time Markov chain. The matrix geometric method will be used to numerically compute various system performance measures under different conditions. We analyze the effect of adding more emergency beds in the ED, adding more ambulances, and reducing the ED patient length of stay, on various system performance measures such as the average number of ambulances in offload delay, average time in offload delay, and ambulance and bed utilization. We will show that adding more beds to the ED or reducing ED patient length of stay will have a positive impact on system performance and in particular will decrease the average number of ambulances experiencing offload delay and the average time in offload delay. Also, it will be shown that increasing the number of ambulances will have a negative impact on offload delays and increases the average number of ambulances in offload delay. However, other system performance measures are improved by adding more ambulances to the system. Finally, we will show the tradeoffs between adding more emergency beds, adding more ambulances, and reducing ED patient length of stay. We conclude that the hospital is the bottleneck in the system and in order to reduce ambulance offload delays, either hospital capacity has to be increased or ED patient length of stay is to be reduced

    Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

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    An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

    Proceedings, MSVSCC 2013

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    Proceedings of the 7th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 11, 2013 at VMASC in Suffolk, Virginia
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