TCP Symbiosis

2012 ◽  
pp. 104-131
Author(s):  
Go Hasegawa ◽  
Masayuki Murata
Keyword(s):  
Window Size ◽  
Convergence Time ◽  
Logistic Growth ◽  
Fast Tcp ◽  
Network Bandwidth ◽  
Scalable Tcp ◽  
Logistic Growth Model ◽  
Congestion Control Mechanism ◽  
Tcp Reno ◽  
Time Fairness

In this chapter, we introduce a robust, self-adaptive and scalable congestion control mechanism for TCP. We change the window size of a TCP connection according to the information of the physical and available bandwidths of the end-to-end network path. The bandwidth information is obtained by an inline network measurement technique. We also borrowed algorithms from biophysics to update the window size: the logistic growth model and the Lotka-Volterra competition model. The greatest advantage of using these models is that we can refer previous discussions and results for various characteristics of the mathematical models, including scalability, convergence, fairness and stability in these models. Through mathematical analysis and simulation experiments, we compare the proposed mechanism with traditional TCP Reno, HighSpeed TCP, Scalable TCP and FAST TCP, and exhibit its effectiveness in terms of scalability to the network bandwidth and delay, convergence time, fairness among competing connections, and stability.

TCP for High-Speed Networks

2008 ◽  
pp. 626-632
Author(s):  
Nelson Luís Saldanha da Fonseca ◽  
Neila Fernanda Michel
Keyword(s):  
Congestion Control ◽  
High Speed ◽  
Transmission Control Protocol ◽  
Window Size ◽  
Transport Layer ◽  
The Internet ◽  
Congestion Window ◽  
High Speed Networks ◽  
Congestion Control Mechanism ◽  
Tcp Reno

In response to a series of collapses due to congestion on the Internet in the mid-’80s, congestion control was added to the transmission control protocol (TCP) (Jacobson, 1988), thus allowing individual connections to control the amount of traffic they inject into the network. This control involves regulating the size of the congestion window (cwnd) to impose a limit on the size of the transmission window. In the most deployed TCP variant on the Internet, TCP Reno (Allman, Floyd, & Partridge, 2002), changes in congestion window size are driven by the loss of segments. Congestion window size is increased by 1/cwnd for each acknowledgement (ack) received, and reduced to half for the loss of a segment in a pattern known as additive increase multiplicative decrease (AIMD). Although this congestion control mechanism was derived at a time when the line speed was of the order of 56 kbs, it has performed remarkably well given that the speed, size, load, and connectivity of the Internet have increased by approximately six orders of magnitude in the past 15 years. However, the AIMD pattern of window growth seriously limits efficienct operation of TCP-Reno over high-capacity links, so that the transport layer is the network bottleneck. This text explains the major challenges involved in using TCP for high-speed networks and briefly describes some of the variations of TCP designed to overcome these challenges.

scholarly journals Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase

2019 ◽  
Vol 9 (21) ◽  
pp. 4698
Author(s):  
Sarfraz Ahmad ◽  
Muhammad Junaid Arshad
Keyword(s):  
Congestion Control ◽  
High Speed ◽  
Recovery Phase ◽  
Transport Layer ◽  
Convergence Time ◽  
Transmission Protocol ◽  
Fast Recovery ◽  
Congestion Window ◽  
Fast Tcp ◽  
Network Bandwidth

The purpose of this study is to enhance the performance of Multistream Fast Transmission Control Protocol (TCP) keeping in view the recent web-based applications that are being deployed on long-range, high-speed, and high-bandwidth networks. To achieve the objective of the research study, a congestion control after fast-recovery module for congestion control scheme of Multistream Fast TCP is proposed. The module optimized the performance of the protocol by reducing the time that is required to consume the available bandwidth after a fast-recovery phase. The module is designed after studying additive-increase, multiplicative-decrease and rate-based congestion window management schemes of related transport protocols. The module adjusts the congestion window on receipt of each individual acknowledgment instead of each round trip time after the fast-recovery phase until it consumes vacant bandwidth of the network link. The module is implemented by using Network Simulator 2. Convergence time, throughput, fairness index, and goodput are the parameters used to assess the performance of proposed module. The results indicate that Enhanced Multistream Fast TCP with congestion control after fast recovery recovers its congestion window in a shorter time period as compared to multistream Fast TCP, Fast TCP, TCP New Reno, and Stream Control Transmission Protocol. Consequently, Enhanced Multistream Fast TCP consumes the available network bandwidth in lesser time and increases the throughput and goodput. The proposed module enhanced the performance of the transport layer protocol. Our findings demonstrate the performance impact in the form of a decrease in the convergence time to consume the available network bandwidth and the increase in the throughput and the goodput.

scholarly journals Extended logistic growth model for heterogeneous populations

2017 ◽  
Author(s):  
Wang Jin ◽  
Scott W McCue ◽  
Matthew J Simpson
Keyword(s):  
Cell Proliferation ◽  
Growth Model ◽  
Numerical Solutions ◽  
Cellular Level ◽  
Logistic Growth ◽  
Logistic Growth Model ◽  
Living Tissues ◽  
Discrete Mathematical Model ◽  
The Continuum

AbstractCell proliferation is the most important cellular-level mechanism responsible for regulating cell population dynamics in living tissues. Modern experimental procedures show that the proliferation rates of individual cells can vary significantly within the same cell line. However, in the mathematical biology literature, cell proliferation is typically modelled using a classical logistic equation which neglects variations in the proliferation rate. In this work, we consider a discrete mathematical model of cell migration and cell proliferation, modulated by volume exclusion (crowding) effects, with variable rates of proliferation across the total population. We refer to this variability as heterogeneity. Constructing the continuum limit of the discrete model leads to a generalisation of the classical logistic growth model. Comparing numerical solutions of the model to averaged data from discrete simulations shows that the new model captures the key features of the discrete process. Applying the extended logistic model to simulate a proliferation assay using rates from recent experimental literature shows that neglecting the role of heterogeneity can, at times, lead to misleading results.

scholarly journals Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

2021 ◽  
Author(s):  
Mohamed LOUNIS ◽  
Babu Malavika
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Polynomial Regression ◽  
Logistic Growth ◽  
The Novel ◽  
Logistic Growth Model ◽  
Polynomial Regression Models ◽  
Using Data ◽  
Novel Coronavirus ◽  
Polynomial Regression Model

Abstract The novel Coronavirus respiratory disease 2019 (COVID-19) is still expanding through the world since it started in Wuhan (China) on December 2019 reporting a number of more than 84.4 millions cases and 1.8 millions deaths on January 3rd 2021.In this work and to forecast the COVID-19 cases in Algeria, we used two models: the logistic growth model and the polynomial regression model using data of COVID-19 cases reported by the Algerian ministry of health from February 25th to December 2nd, 2020. Results showed that the polynomial regression model fitted better the data of COVID-19 in Algeria the Logistic model. The first model estimated the number of cases on January, 19th 2021 at 387673 cases. This model could help the Algerian authorities in the fighting against this disease.

scholarly journals The shape of density dependence and the relationship between population growth, intraspecific competition and equilibrium population density

2018 ◽  
Author(s):  
Emanuel A. Fronhofer ◽  
Lynn Govaert ◽  
Mary I. O’Connor ◽  
Sebastian J. Schreiber ◽  
Florian Altermatt
Keyword(s):  
Population Growth ◽  
Growth Models ◽  
Population Level ◽  
Logistic Growth ◽  
Population Densities ◽  
Density Regulation ◽  
Logistic Growth Model ◽  
Regulation Functions ◽  
Consumer Resource ◽  
Population Growth Models

AbstractThe logistic growth model is one of the most frequently used formalizations of density dependence affecting population growth, persistence and evolution. Ecological and evolutionary theory and applications to understand population change over time often include this model. However, the assumptions and limitations of this popular model are often not well appreciated.Here, we briefly review past use of the logistic growth model and highlight limitations by deriving population growth models from underlying consumer-resource dynamics. We show that the logistic equation likely is not applicable to many biological systems. Rather, density-regulation functions are usually non-linear and may exhibit convex or both concave and convex curvatures depending on the biology of resources and consumers. In simple cases, the dynamics can be fully described by the continuous-time Beverton-Holt model. More complex consumer dynamics show similarities to a Maynard Smith-Slatkin model.Importantly, we show how population-level parameters, such as intrinsic rates of increase and equilibrium population densities are not independent, as often assumed. Rather, they are functions of the same underlying parameters. The commonly assumed positive relationship between equilibrium population density and competitive ability is typically invalid. As a solution, we propose simple and general relationships between intrinsic rates of increase and equilibrium population densities that capture the essence of different consumer-resource systems.Relating population level models to underlying mechanisms allows us to discuss applications to evolutionary outcomes and how these models depend on environmental conditions, like temperature via metabolic scaling. Finally, we use time-series from microbial food chains to fit population growth models and validate theoretical predictions.Our results show that density-regulation functions need to be chosen carefully as their shapes will depend on the study system’s biology. Importantly, we provide a mechanistic understanding of relationships between model parameters, which has implications for theory and for formulating biologically sound and empirically testable predictions.

A Neoclassical Growth Model for Population Dynamics in a Homogeneous Habitat

2001 ◽  
Author(s):  
Peter Vadasz ◽  
Alisa S. Vadasz
Keyword(s):  
Experimental Data ◽  
Growth Model ◽  
Logistic Growth ◽  
Lag Phase ◽  
Growth Stages ◽  
Initial Growth ◽  
Neoclassical Model ◽  
Wide Range ◽  
Logistic Growth Model ◽  
Special Case

Abstract A neoclassical model is proposed for the growth of cell and other populations in a homogeneous habitat. The model extends on the Logistic Growth Model (LGM) in a non-trivial way in order to address the cases where the Logistic Growth Model (LGM) fails short in recovering qualitative as well as quantitative features that appear in experimental data. These features include in some cases overshooting and oscillations, in others the existence of a “Lag Phase” at the initial growth stages, as well as an inflection point in the “In curve” of the population size. The proposed neoclassical model recovers also the Logistic Growth Curve as a special case. Comparisons of the solutions obtained from the proposed neoclassical model with experimental data confirm its quantitative validity, as well as its ability to recover a wide range of qualitative features captured in experiments.

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