The fluid flow approximation of the TCP vegas and reno congestion control mechanism

TCP congestion control algorithms have been design to improve Internet transmission performance and stability. In recent years the classic Tahoe/Reno/NewReno TCP congestion control, based on losses as congestion indicators, has been improved and many congestion control algorithms have been proposed. In this paper the performance of standard TCP NewReno algorithm is compared to the performance of TCP Vegas, which tries to avoid congestion by reducing the congestion window (CWND) size before packets are lost. The article uses fluid flow approximation to investigate the influence of the two above-mentioned TCP congestion control mechanisms on CWND evolution, packet loss probability, queue length and its variability. Obtained results show that TCP Vegas is a fair algorithm, however it has problems with the use of available bandwidth

Modeling and estimation techniques for understanding heterogeneous traffic behavior

The majority of current internet traffic is based on TCP. With the emergence of new applications, especially new multimedia applications, however, UDP-based traffic is expected to increase. Furthermore, multimedia applications have sparkled the development of protocols responding to congestion while behaving differently from TCP. As a result, network traffc is expected to become more and more diverse. The increasing link capacity further stimulates new applications utilizing higher bandwidths of future. Besides the traffic diversity, the network is also evolving around new technologies. These trends in the Internet motivate our research work. In this dissertation, modeling and estimation techniques of heterogeneous traffic at a router are presented. The idea of the presented techniques is that if the observed queue length and packet drop probability do not match the predictions from a model of responsive (TCP) traffic, then the error must come from non-responsive traffic; it can then be used for estimating the proportion of non-responsive traffic. The proposed scheme is based on the queue length history, packet drop history, expected TCP and queue dynamics. The effectiveness of the proposed techniques over a wide range of traffic scenarios is corroborated using NS-2 based simulations. Possible applications based on the estimation technique are discussed. The implementation of the estimation technique in the Linux kernel is presented in order to validate our estimation technique in a realistic network environment

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

Improving Large-Scale Network Traffic Simulation with Multi-Resolution Models

Simulating a large-scale network like the Internet is a challenging undertaking because of the sheer volume of its traffic. Packet-oriented representation provides high-fidelity details but is computationally expensive; fluid-oriented representation offers high simulation efficiency at the price of losing packet-level details. Multi-resolution modeling techniques exploit the advantages of both representations by integrating them in the same simulation framework. This dissertation presents solutions to the problems regarding the efficiency, accuracy, and scalability of the traffic simulation models in this framework. The ``ripple effect\u27\u27 is a well-known problem inherent in event-driven fluid-oriented traffic simulation, causing explosion of fluid rate changes. Integrating multi-resolution traffic representations requires estimating arrival rates of packet-oriented traffic, calculating the queueing delay upon a packet arrival, and computing packet loss rate under buffer overflow. Real time simulation of a large or ultra-large network demands efficient background traffic simulation. The dissertation includes a rate smoothing technique that provably mitigates the ``ripple effect\u27\u27, an accurate and efficient approach that integrates traffic models at multiple abstraction levels, a sequential algorithm that achieves real time simulation of the coarse-grained traffic in a network with 3 tier-1 ISP (Internet Service Provider) backbones using an ordinary PC, and a highly scalable parallel algorithm that simulates network traffic at coarse time scales

Fixed-point analysis of a network of routers with persistent TCP/UDP flows and class-based weighted fair queuing

Fixed-point models have already been successfully used to analytically study networks consisting of persistent TCP flows only, or mixed TCP/UDP flows with a single queue per link and differentiated buffer management for these two types of flows. In the current study, we propose a nested fixed-point analytical method to obtain the throughput of persistent TCP and UDP flows in a network of routers supporting class-based weighted fair queuing allowing the use of separate queues for each class. In particular, we study the case of two classes where one of the classes uses drop-tail queue management and is intended for only UDP traffic. The other class targeting TCP, but also allowing UDP traffic for the purpose of generality, is assumed to employ active queue management. The effectiveness of the proposed analytical method is validated in terms of accuracy using ns-3 simulations and the required computational effort. © 2016, Springer Science+Business Media New York

A study on fairness and latency issues over high speed networks and data center networks

Newly emerging computer networks, such as high speed networks and data center networks, have characteristics of high bandwidth and high burstiness which make it difficult to address issues such as fairness, queuing latency and link utilization. In this study, we first conduct extensive experimental evaluation of the performance of 10Gbps high speed networks. We found inter-protocol unfairness and larger queuing latency are two outstanding issues in high speed networks and data center networks. There have been several proposals to address fairness and latency issues at switch level via queuing schemes. These queuing schemes have been fairly successful in addressing either fairness issue or large latency but not both at the same time. We propose a new queuing scheme called Approximated-Fair and Controlled-Delay (AFCD) queuing scheme that meets following goals for high speed networks: approximated fairness, controlled low queuing delay, high link utilization and simple implementation. The design of AFCD utilizes a novel synergistic approach by forming an alliance between approximated fair queuing and controlled delay queuing. AFCD maintains very small amount of state information in sending rate estimation of flows and makes drop decision based on a target delay of individual flow. We then present FaLL, a Fair and Low Latency queuing scheme that meets stringent performance requirements of data center networks: fair share of bandwidth, low queuing latency, high throughput, and ease of deployment. FaLL uses an efficiency module, a fairness module and a target delay based dropping scheme to meet these goals. Through rigorous experiments on real testbed, we show that FaLL outperforms various peer solutions in variety of network conditions over data center networks