**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.

Publication# On the Long-Run Behavior of Equation-Based Rate Control

Abstract

AbstractWe consider unicast equation-based rate con-trol, where, at some points in time, a sender adjusts its rate to f(p,r), where p is an on-line estimate of the loss-event rate observed by this source, r of the average round-trip time, and f is a TCP throughput formula. Conventional wisdom holds that such a source would be TCP-friendly, that is, it would not attain a larger long-run average send rate than a TCP source under the same operating conditions. Our goal is to identify the key factors that determine whether, and how far, this is true. We point out that it is important to breakdown the TCP-friendliness condition into sub-conditions and study them separately. One sub-condition is conservativeness (throughput not larger than f(p,r)). The conservativeness is primarily influenced by some convexity properties of the function f, and a covariance property of the loss process. In many cases, these conditions result in conservativeness, in some cases, excessive conservativeness. Another sub-condition is that the source experiences a loss-event rate that is not smaller than that of TCP. We show two limit cases for which the last sub-condition, respectively, does and does not hold. We show that in the latter situation, the outcome can be a significant non-TCP-friendliness. The claims suggested by our analysis are verified by numerical examples, simulations, Internet and lab experiments. Our findings should help us better understand when to expect the source to be TCP-friendly, or in contrast, non-TCP-friendly. On the basis of our analysis and empirical evaluations we observe that TCP-friendliness is difficult to verify, whereas conservativeness is easier.

Official source

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related concepts (37)

Related publications (39)

Related MOOCs (9)

Covariance matrix

In probability theory and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square matrix giving the covariance between each pair of elements of a given random vector. Any covariance matrix is symmetric and positive semi-definite and its main diagonal contains variances (i.e., the covariance of each element with itself). Intuitively, the covariance matrix generalizes the notion of variance to multiple dimensions.

Covariance

In probability theory and statistics, covariance is a measure of the joint variability of two random variables. If the greater values of one variable mainly correspond with the greater values of the other variable, and the same holds for the lesser values (that is, the variables tend to show similar behavior), the covariance is positive. In the opposite case, when the greater values of one variable mainly correspond to the lesser values of the other, (that is, the variables tend to show opposite behavior), the covariance is negative.

OSI model

The Open Systems Interconnection model (OSI model) is a conceptual model from the International Organization for Standardization (ISO) that "provides a common basis for the coordination of standards development for the purpose of systems interconnection." In the OSI reference model, the communications between a computing system are split into seven different abstraction layers: Physical, Data Link, Network, Transport, Session, Presentation, and Application.

Analyse I

Le contenu de ce cours correspond à celui du cours d'Analyse I, comme il est enseigné pour les étudiantes et les étudiants de l'EPFL pendant leur premier semestre. Chaque chapitre du cours correspond

Analyse I (partie 1) : Prélude, notions de base, les nombres réels

Concepts de base de l'analyse réelle et introduction aux nombres réels.

Analyse I (partie 2) : Introduction aux nombres complexes

Introduction aux nombres complexes

Daniel Kuhn, Yves Rychener, Viet Anh Nguyen

The state-of-the-art methods for estimating high-dimensional covariance matrices all shrink the eigenvalues of the sample covariance matrix towards a data-insensitive shrinkage target. The underlying shrinkage transformation is either chosen heuristically ...

2024The objective of this paper is to investigate a new numerical method for the approximation of the self-diffusion matrix of a tagged particle process defined on a grid. While standard numerical methods make use of long-time averages of empirical means of de ...

Mathieu Salzmann, Yinlin Hu, Fulin Liu

Most modern image-based 6D object pose estimation methods learn to predict 2D-3D correspondences, from which the pose can be obtained using a PnP solver. Because of the non-differentiable nature of common PnP solvers, these methods are supervised via the i ...