Link Buffer Sizing: a New Look at the Old Problem

In this paper, we revisit the question of how much buffer an IP router should allocate for its output link. For a long time, the intuitive answer of setting the buffer size to the bitrate-delay product has been widely regarded as reasonable. Recent studies of interaction between queueing at IP routers and TCP congestion control proposed alternative answers. First, we expose and explain contradictions between existing guidelines for link buffer sizing. Then, we argue that the problem of link buffer sizing needs a different formulation. In particular, the chosen buffer size should accommodate not only common versions of TCP but also UDP traffic. Besides, our new formulation of the problem contains an explicit constraint of not engaging IP routers in any additional signaling. We conclude the paper by outlining a promising direction for solving the reformulated problem

An advanced scheme for queue management inTCP/IP networks

Active Queue Management (AQM) is a key congestion control scheme that aims to find a balance between keeping high link utilization, minimizing queuing delays, and ensuring a fair share of the bandwidth between the competing flows. Traditional AQM mechanisms use only information that is present at the intermediate nodes (routers). They do not take into account the particularities of the flows composing the traffic. In this paper, we make use of a mechanism, called Explicit RTT Notification (ERN), that shares with routers information about the Round Trip Times (RTTs) of the flows. We propose a new fuzzy logic based AQM controller that relies on the RTTs of the flows to improve fairness between them. The performances of the new proposed method, FuzzyRTT, is examined and compared to existing schemes via simulation experiments

Selecting the Buffer Size for an IP Network Link

In this paper, we revisit the problem of selecting the buffer size for an IP network link. After a comprehensive overview of issues relevant to the link buffer sizing, we examine usefulness of existing guidelines for choosing the buffer size. Our analysis shows that the existing recommendations not only are difficult to implement in the context of IP networks but also can severely hurt interactive distributed applications. Then, we argue that the networking research community should change its way of thinking about the link buffer sizing problem: the focus should shift from optimizing performance for applications of a particular type to maximizing diversity of application types that IP networks can support effectively. To achieve this new objective, we propose using small buffers for IP network links

TCP Libra: Exploring RTT-Fairness for TCP

The majority of Internet users rely on the Transmission Control Protocol (TCP) to download large multimedia files from remote servers (e.g. P2P file sharing). TCP has been advertised as a fair-share protocol. However, when session round-trip-times (RTTs) radically differ from each other, the share (of the bottleneck link) may be anything but fair. This motivates us to explore a new TCP, TCP Libra, that guarantees fair sharing regardless of RTT. TCP Libra is source only based and thus easy to deploy. Via analytic modeling and simulations we show that TCP Libra achieves fairness while maintaining efficiency and friendliness to TCP New Reno. A comparison with other TCP versions that have been reported as RTT-fair in the literature is also carried out

Parameter self-tuning in internet congestion control

Active Queue Management (AQM) aims to achieve high link utilization, low queuing delay and low loss rate in routers. However, it is difficult to adapt AQM parameters to constantly provide desirable transient and steady-state performance under highly dynamic network scenarios. They need to be a trade-off made between queuing delay and utilization. The queue size would become unstable when round-trip time or link capacity increases, or would be unnecessarily large when round-trip time or link capacity decreases. Effective ways of adapting AQM parameters to obtain good performance have remained a critical unsolved problem during the last fifteen years. This thesis firstly investigates existing AQM algorithms and their performance. Based on a previously developed dynamic model of TCP behaviour and a linear feedback model of TCP/RED, Auto-Parameterization RED (AP-RED) is proposed which unveils the mechanism of adapting RED parameters according to measurable network conditions. Another algorithm of Statistical Tuning RED (ST-RED) is developed for systematically tuning four key RED parameters to control the local stability in response to the detected change in the variance of the queue size. Under variable network scenarios like round-trip time, link capacity and traffic load, no manual parameter configuration is needed. The proposed ST-RED can adjust corresponding parameters rapidly to maintain stable performance and keep queuing delay as low as possible. Thus the sensitivity of RED's performance to different network scenarios is removed. This Statistical Tuning algorithm can be applied to a PI controller for AQM and a Statistical Tuning PI (ST-PI) controller is also developed. The implementation of ST-RED and ST-PI is relatively straightforward. Simulation results demonstrate the feasibility of ST-RED and ST-PI and their capabilities to provide desirable transient and steady-state performance under extensively varying network conditions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Efficient techniques for end-to-end bandwidth estimation: performance evaluations and scalable deployment

Several applications, services, and protocols are conjectured to benefit from the knowledge of the end-to-end available bandwidth on a given Internet path. Unfortunately, despite the availability of several bandwidth estimation techniques, there has been only a limited adoption of these in contemporary applications. We identify two issues that contribute to this state of affairs. First, there is a lack of comprehensive evaluations that can help application developers in calibrating the relative performance of these tools--this is especially limiting since the performance of these tools depends on algorithmic, implementation, as well as temporal aspects of probing for available bandwidth. Second, most existing bandwidth estimation tools impose a large probing overhead on the paths over which bandwidth is measured. This can be a significant deterrent for deploying these tools in distributed infrastructures that need to measure bandwidth on several paths periodically. In this dissertation, we address the two issues raised above by making the following contributions: We conduct the first comprehensive black-box evaluation of a large suite of prominent available bandwidth estimation tools on a high-speed network. In this evaluation,we also illustrate the impact that technological and implementation limitations can have on the performance of bandwidth-estimation tools. We conduct the first comprehensive evaluation of available bandwidth estimation algorithms, independent of systemic and implementation biases. In this evaluation, we also illustrate the impact temporal factor such as measurement timescales have on the observed relative performance of bandwidth-estimation tools. We demonstrate that temporal properties can significantly impact the AB estimation process. We redesign the interfaces of existing bandwidth-estimation tools to allow temporal parameters to be explicitly specified and controlled. We design AB inference schemes which can be used to scalably and collaboratively infer the available bandwidth for a large set of end-to-end paths. These schemes allow an operator to select the desired operating point in the trade-off between accuracy and overhead of AB estimation. We further demonstrate that in order to monitor the bandwidth on all paths of a network we do not need access to per-hop bandwidth estimates and can simply rely on end-to-end bandwidth estimates

New strategies for efficient and practical genetic programming.

2006/2007In the last decades, engineers and decision makers expressed a growing interest in the development of effective modeling and simulation methods to understand or predict the behavior of many phenomena in science and engineering. Many of these phenomena are translated in mathematical models for convenience and to carry out an easy interpretation. Methods commonly employed for this purpose include, for example, Neural Networks, Simulated Annealing, Genetic Algorithms, Tabu search, and so on. These methods all seek for the optimal or near optimal values of a predefined set of parameters of a model built a priori. But in this case, a suitable model should be known beforehand. When the form of this model cannot be found, the problem can be seen from another level where the goal is to find a program or a mathematical representation which can solve the problem. According to this idea the modeling step is performed automatically thanks to a quality criterion which drives the building process. In this thesis, we focus on the Genetic Programming (GP) approach as an automatic method for creating computer programs by means of artificial evolution based upon the original contributions of Darwin and Mendel. While GP has proven to be a powerful means for coping with problems in which finding a solution and its representation is difficult, its practical applicability is still severely limited by several factors. First, the GP approach is inherently a stochastic process. It means there is no guarantee to obtain a satisfactory solution at the end of the evolutionary loop. Second, the performances on a given problem may be strongly dependent on a broad range of parameters, including the number of variables involved, the quantity of data for each variable, the size and composition of the initial population, the number of generations and so on. On the contrary, when one uses Genetic Programming to solve a problem, he has two expectancies: on the one hand, maximize the probability to obtain an acceptable solution, and on the other hand, minimize the amount of computational resources to get this solution. Initially we present innovative and challenging applications related to several fields in science (computer science and mechanical science) which participate greatly in the experience gained in the GP field. Then we propose new strategies for improving the performances of the GP approach in terms of efficiency and accuracy. We probe our approach on a large set of benchmark problems in three different domains. Furthermore we introduce a new approach based on GP dedicated to symbolic regression of multivariate data-sets where the underlying phenomenon is best characterized by a discontinuous function. These contributions aim to provide a better understanding of the key features and the underlying relationships which make enhancements successful in improving the original algorithm.Negli ultimi anni, ingegneri e progettisti hanno espresso un interesse crescente nello sviluppo di nuovi metodi di simulazione e di modellazione per comprendere e predire il comportamento di diversi fenomeni sia in ambito scientifico che ingegneristico. Molti di questi fenomeni vengono descritti attraverso modelli matematici che ne facilitano l'interpretazione. A questo fine, i metodi più comunemente impiegati sono, le tecniche basate sui Reti Neurali, Simulated Annealing, gli Algoritmi Genetici, la ricerca Tabu, ecc. Questi metodi vanno a determinare i valori ottimali o quasi ottimali dei parametri di un modello costruito a priori. E evidente che in tal caso, si dovrebbe conoscere in anticipo un modello idoneo. Quando ciò non è possibile, il problema deve essere considerato da un altro punto di vista: l'obiettivo è trovare un programma o una rappresentazione matematica che possano risolvere il problema. A questo scopo, la fase di modellazione è svolta automaticamente in funzione di un criterio qualitativo che guida il processo di ricerca. Il tema di ricerca di questa tesi è la programmazione genetica (“Genetic Programming” che chiameremo GP) e le sue applicazioni. La programmazione genetica si può definire come un metodo automatico per la generazione di programmi attraverso una simulazione artificiale dei principi relativi all'evoluzione naturale basata sui contributi originali di Darwin e di Mendel. La programmazione genetica ha dimostrato di essere un potente mezzo per affrontare quei problemi in cui trovare una soluzione e la sua rappresentazione è difficile. Però la sua applicabilità rimane severamente limitata da diversi fattori. In primo luogo, il metodo GP è inerentemente un processo stocastico. Ciò significa che non garantisce che una soluzione soddisfacente sarà trovata alla fine del ciclo evolutivo. In secondo luogo, le prestazioni su un dato problema dipendono fortemente da una vasta gamma di parametri, compresi il numero di variabili impiegate, la quantità di dati per ogni variabile, la dimensione e la composizione della popolazione iniziale, il numero di generazioni e così via. Al contrario, un utente della programmazione genetica ha due aspettative: da una parte, massimizzare la probabilità di ottenere una soluzione accettabile, e dall'altra, minimizzare la quantità di risorse di calcolo per ottenerla. Nella fase iniziale di questo lavoro sono state considerate delle applicazioni particolarmente innovative relative a diversi campi della scienza (informatica e meccanica) che hanno contributo notevolmente all'esperienza acquisita nel campo della programmazione genetica. In questa tesi si propone un nuovo procedimento con lo scopo di migliorare le prestazioni della programmazione genetica in termini di efficienza ed accuratezza. Abbiamo testato il nostro approccio su un ampio insieme di benchmarks in tre domini applicativi diversi. Si propone inoltre una tecnica basata sul GP per la regressione simbolica di data-set multivariati dove il fenomeno di fondo è caratterizzato da una funzione discontinua. Questi contributi cercano di fornire una comprensione migliore degli elementi chiave e dei meccanismi interni che hanno consentito il miglioramento dell'algoritmo originale.XX Ciclo198

Real world evaluation of techniques for mitigating the impact of packet losses on TCP performance

The real-world impact of network losses on the performance of Transmission Control Protocol (TCP), the dominant transport protocol used for Internet data transfer, is not well understood. A detailed understanding of this impact and the efficiency of TCP in dealing with losses would prove useful for optimizing TCP design. Past work in this area is limited in its accuracy, depth of analysis, and scale. In this dissertation, we make three main contributions to address these issues: (i) design a methodology for in-depth and accurate passive analysis of TCP traces, (ii) systematically evaluate the impact of design parameters associated with TCP loss detection/recovery mechanisms on its performance, and (iii) systematically evaluate the ability of Delay Based Congestion Estimators (DBCEs) to predict losses and help avoid them. We develop a passive analysis tool, TCPdebug, which accurately tracks TCP sender state for many prominent OSes (Windows, Linux, Solaris, and FreeBSD/MacOS) and accurately classifies segments that appear out-of-sequence in a TCP trace. This tool has been extensively validated using controlled lab experiments as well as against real Internet connections. Its accuracy exceeds 99%, which is double the accuracy of current loss classification tools. Using TCPdebug, we analyze traces of more than 2.8 million Internet connections to study the efficiency of current TCP loss detection/recovery mechanisms. Using models to capture the impact of configuration of these mechanisms on the durations of TCP connections, we find that the recommended as well as widely implemented configurations for these mechanisms are fairly sub-optimal. Our analysis suggests that the durations of up to 40% of Internet connections can be reduced by more than 10% by reconfiguring prominent TCP stacks. Finally, we investigate the ability of several popular Delay Based Connection Estimators (DBCEs) to predict (and help avoid) losses using estimates of network queuing delay. We find that aggressive predictors work much better than conservative predictors. We also study the impact of connection characteristics--such as packet loss rate, flight size, and throughput--on the performance of a DBCE. We find that high-throughput connections benefit the most from any DBCE. This indicates that DBCEs hold significant promise for future high-speed networks