An Improved Link Model for Window Flow Control and Its Application to FAST TCP

This paper presents a link model which captures the queue dynamics in response to a change in a transmission control protocol (TCP) source's congestion window. By considering both self-clocking and the link integrator effect, the model generalizes existing models and is shown to be more accurate by both open loop and closed loop packet level simulations. It reduces to the known static link model when flows' round trip delays are identical, and approximates the standard integrator link model when there is significant cross traffic. We apply this model to the stability analysis of fast active queue management scalable TCP (FAST TCP) including its filter dynamics. Under this model, the FAST control law is linearly stable for a single bottleneck link with an arbitrary distribution of round trip delays. This result resolves the notable discrepancy between empirical observations and previous theoretical predictions. The analysis highlights the critical role of self-clocking in TCP stability, and the proof technique is new and less conservative than existing ones

Queue Dynamics With Window Flow Control

This paper develops a new model that describes the queueing process of a communication network when data sources use window flow control. The model takes into account the burstiness in sub-round-trip time (RTT) timescales and the instantaneous rate differences of a flow at different links. It is generic and independent of actual source flow control algorithms. Basic properties of the model and its relation to existing work are discussed. In particular, for a general network with multiple links, it is demonstrated that spatial interaction of oscillations allows queue instability to occur even when all flows have the same RTTs and maintain constant windows. The model is used to study the dynamics of delay-based congestion control algorithms. It is found that the ratios of RTTs are critical to the stability of such systems, and previously unknown modes of instability are identified. Packet-level simulations and testbed measurements are provided to verify the model and its predictions

Sparse estimation of polynomial dynamical models

In many practical situations, it is highly desirable to estimate an accurate mathematical model of a real system using as few parameters as possible. This can be motivated either from appealing to a parsimony principle (Occam's razor) or from the view point of the utilization complexity in terms of control synthesis, prediction, etc. At the same time, the need for an accurate description of the system behavior without knowing its complete dynamical structure often leads to model parameterizations describing a rich set of possible hypotheses; an unavoidable choice, which suggests sparsity of the desired parameter estimate. An elegant way to impose this expectation of sparsity is to estimate the parameters by penalizing the criterion with the l0 norm of the parameters, which is often implemented as solving an optimization program based on a convex relaxation (e.g. l1/LASSO, nuclear norm, ...). However, in order to apply these methods, the (unpenalized) cost function must be convex. This imposes a severe constraint on the types of model structures or estimation methods on which these relaxations can be applied. In this paper, we extend the use of convex relaxation techniques for sparsity to general rational plant model structures estimated by using prediction error minimization. This is done by combining the LASSO and the Steiglitz-McBride approaches. To demonstrate the advantages of the proposed solution an extensive simulation study is provided

A new kernel-based approach to system identification with quantized output data

In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. This serves as a starting point to cast our system identification problem into a Bayesian framework. We employ Markov Chain Monte Carlo methods to provide an estimate of the system. In particular, we design two methods based on the so-called Gibbs sampler that allow also to estimate the kernel hyperparameters by marginal likelihood maximization via the expectation–maximization method. Numerical simulations show the effectiveness of the proposed scheme, as compared to the state-of-the-art kernel-based methods when these are employed in system identification with quantized data.</p

Association between exposure to crystalline silica and risk of sarcoidosis.

To access publisher full text version of this article. Please click on the hyperlink in Additional Links fieldOBJECTIVES: The possibility of an association between exposure to silica and autoimmune diseases has recently come under discussion. In the following case-referent study, a cohort exposed to diatomaceous earth and cristobalite provided an opportunity to evaluate such an exposure with reference to sarcoidosis. METHODS: The inhabitants of a district served by a single healthcare centre and a hospital formed the study base. A diatomaceous earth plant is located in this community and the medical institutions are responsible for primary and secondary health care of the population. Cases of sarcoidosis were identified from the hospital records according to certain clinical, radiological, and histological criteria. Referents were selected randomly from the population of the district. Information on exposure to crystalline silica, cristobalite, was obtained by record linkage of the cases and referents with a file which included all present and past workers at the diatomaceous earth plant and those who had worked at loading vessels with the product from the plant. RESULTS: Eight cases of sarcoidosis were found, six of which were in the exposed group. Of the 70 referents, 13 were exposed. The odds ratio (95% confidence interval) was 13.2 (2.0 to 140.9). CONCLUSION: The odds ratios were high and there were some indications of a dose-response relation which will hopefully encourage further studies. To our knowledge this is the first study to indicate a relation between sarcoidosis and exposure to the crystalline silica, cristobalite

Robust optimal identification experiment design for multisine excitation

In least costly experiment design, the optimal spectrum of an identification experiment is determined in such a way that the cost of the experiment is minimized under some accuracy constraint on the identified parameter vector. Like all optimal experiment design problems, this optimization problem depends on the unknown true system, which is generally replaced by an initial estimate. One important consequence of this is that we can underestimate the actual cost of the experiment and that the accuracy of the identified model can be lower than desired. Here, based on an a-priori uncertainty set for the true system, we propose a convex optimization approach that allows to prevent these issues from happening. We do this when the to-be-determined spectrum is the one of a multisine signal. 1 Introduction We consider in this paper the problem of optimally designing the spectrum Φ u of the excitation signal u of an open-loop identification experiment. By optimal spectrum , we here mean the spectrum yielding the smallest experiment cost while guaranteeing that the accuracy of the identified parameter vector of the plant transfer function is larger than a given threshold. We thus consider the least costly experiment design framework [5], but the approach can easily be adapted to other (dual) frameworks [10,17,13]. The experiment cost J can be defined as a linear combination of the power of the exci-tation signal u and of the power of the part of the output signal induced by u. The experiment cost will therefore be a function of the spectrum Φ u , but also of the unknown true parameter vector θ 0 (we therefore denote the cost as J (θ 0 , Φ u)). Likewise, the accuracy constraint will also depend on θ 0 and on Φ u since the classical accuracy constraints are of the type P −1 (θ 0 , Φ u) ≥ R adm where P (θ 0 , Φ u) is the covariance matrix of the to-be-identified parameter vector (which depends on θ 0 and Φ u) and R adm a matrix reflecting the desired accuracy. The dependency of the optimal spectrum Φ u,opt on the unknown true parameter vector θ 0 is the so-called chicken-and-egg issue encountered in optimal experiment design. This issue is generally circumvented by replacing θ 0 b

Least costly identification experiment for the identification of one module in a dynamic network

In this paper we consider the design of the least costly experiment for the identification of one module in a given network of locally controlled systems. The identification experiment will be designed in such a way that we obtain a sufficiently accurate model of the to-be-identified module with the smallest identification cost i.e. with the least perturbation of the network