TCP-Aware Backpressure Routing and Scheduling

In this work, we explore the performance of backpressure routing and scheduling for TCP flows over wireless networks. TCP and backpressure are not compatible due to a mismatch between the congestion control mechanism of TCP and the queue size based routing and scheduling of the backpressure framework. We propose a TCP-aware backpressure routing and scheduling that takes into account the behavior of TCP flows. TCP-aware backpressure (i) provides throughput optimality guarantees in the Lyapunov optimization framework, (ii) gracefully combines TCP and backpressure without making any changes to the TCP protocol, (iii) improves the throughput of TCP flows significantly, and (iv) provides fairness across competing TCP flows

Analysis of Multiple Flows using Different High Speed TCP protocols on a General Network

We develop analytical tools for performance analysis of multiple TCP flows (which could be using TCP CUBIC, TCP Compound, TCP New Reno) passing through a multi-hop network. We first compute average window size for a single TCP connection (using CUBIC or Compound TCP) under random losses. We then consider two techniques to compute steady state throughput for different TCP flows in a multi-hop network. In the first technique, we approximate the queues as M/G/1 queues. In the second technique, we use an optimization program whose solution approximates the steady state throughput of the different flows. Our results match well with ns2 simulations.Comment: Submitted to Performance Evaluatio

DTMsim - DTM channel simulation in ns

Dynamic Transfer Mode (DTM) is a ring based MAN technology that provides a channel abstraction with a dynamically adjustable capacity. TCP is a reliable end to end transport protocol capable of adjusting its rate. The primary goal of this work is investigate the coupling of dynamically allocating bandwidth to TCP flows with the affect this has on the congestion control mechanism of TCP. In particular we wanted to find scenerios where this scheme does not work, where either all the link capacity is allocated to TCP or congestion collapse occurs and no capacity is allocated to TCP. We have created a simulation environment using ns-2 to investigate TCP over networks which have a variable capacity link. We begin with a single TCP Tahoe flow over a fixed bandwidth link and progressively add more complexity to understand the behaviour of dynamically adjusting link capacity to TCP and vice versa

TCP Congestion Control Identification

Transmission Control Protocol (TCP) carries most of the traffic on the Internet these days. There are several implementations of TCP, and the most important difference among them is their mechanism for controlling congestion. One of the methods for determining type of a TCP is active probing. Active probing considers a TCP implementation as a black box, sends different streams of data to the appropriate host. According to the response received from the host, it figures out the type of TCP version implemented. TCP Behavior Inference Tool (TBIT) is an implemented tool that uses active probing to check the running TCP on web servers. It can check several aspects of the running TCP including initial value of congestion window, congestion control algorithm, conformant congestion control, response to selective acknowledgment, response to Explicit Congestion Notification (ECN) and time wait duration. In this paper we focus on congestion control algorithm aspect of it, explain the mechanism used by TBIT and present the results

FAST TCP: Motivation, Architecture, Algorithms, Performance

We describe FAST TCP, a new TCP congestion control algorithm for high-speed long-latency networks, from design to implementation. We highlight the approach taken by FAST TCP to address the four difficulties which the current TCP implementation has at large windows. We describe the architecture and summarize some of the algorithms implemented in our prototype. We characterize its equilibrium and stability properties. We evaluate it experimentally in terms of throughput, fairness, stability, and responsiveness

Validation of simulated real world TCP stacks

The TCP models in ns-2 have been validated and are widely used in network research. They are however not aimed at producing results consistent with a TCP implementation, they are rather designed to be a general model for TCP congestion control. The Network Simulation Cradle makes real world TCP implementations available to ns-2: Linux, FreeBSD and OpenBSD can all be simulated as easily as using the original simplified models. These simulated TCP implementations can be validated by directly comparing packet traces from simulations to traces measured from a real network. We describe the Network Simulation Cradle, present packet trace comparison results showing the high degree of accuracy possible when simulating with real TCP implementations and briefly show how this is reflected in a simulation study of TCP throughput

New Method of Measuring TCP Performance of IP Network using Bio-computing

The measurement of performance of Internet Protocol IP network can be done by Transmission Control Protocol TCP because it guarantees send data from one end of the connection actually gets to the other end and in the same order it was send, otherwise an error is reported. There are several methods to measure the performance of TCP among these methods genetic algorithms, neural network, data mining etc, all these methods have weakness and can't reach to correct measure of TCP performance. This paper proposed a new method of measuring TCP performance for real time IP network using Biocomputing, especially molecular calculation because it provides wisdom results and it can exploit all facilities of phylogentic analysis. Applying the new method at real time on Biological Kurdish Messenger BIOKM model designed to measure the TCP performance in two types of protocols File Transfer Protocol FTP and Internet Relay Chat Daemon IRCD. This application gives very close result of TCP performance comparing with TCP performance which obtains from Little's law using same model (BIOKM), i.e. the different percentage of utilization (Busy or traffic industry) and the idle time which are obtained from a new method base on Bio-computing comparing with Little's law was (nearly) 0.13%. KEYWORDS Bio-computing, TCP performance, Phylogenetic tree, Hybridized Model (Normalized), FTP, IRCDComment: 17 Pages,10 Figures,5 Table

Transport congestion events detection (TCED): towards decorrelating congestion detection from TCP

TCP (Transmission Control Protocol) uses a loss-based algorithm to estimate whether the network is congested or not. The main difficulty for this algorithm is to distinguish spurious from real network congestion events. Other research studies have proposed to enhance the reliability of this congestion estimation by modifying the internal TCP algorithm. In this paper, we propose an original congestion event algorithm implemented independently of the TCP source code. Basically, we propose a modular architecture to implement a congestion event detection algorithm to cope with the increasing complexity of the TCP code and we use it to understand why some spurious congestion events might not be detected in some complex cases. We show that our proposal is able to increase the reliability of TCP NewReno congestion detection algorithm that might help to the design of detection criterion independent of the TCP code. We find out that solutions based only on RTT (Round-Trip Time) estimation are not accurate enough to cover all existing cases. Furthermore, we evaluate our algorithm with and without network reordering where other inaccuracies, not previously identified, occur

Performance, Validation and Testing with the Network Simulation Cradle

Much current simulation of TCP makes use of simplified models of TCP, which is a large and complex protocol with many variations possible between implementations. We use direct execution of real world network stacks in the network simulator ns-2 to compare TCP performance between implementations and reproduce existing work. A project called The Network Simulation Cradle provides the real world network stacks and we show how it can be used for performance evaluation and validation. There are large differences in performance between simplified TCP models and TCP implementations in some situations. Such differences are apparent in some reproduced research, with results using the Network Simulation Cradle very different from the results produced with the ns-2 TCP models. In other cases, using the real implementations gives very similar results, validating the original research