Techniques for Processing TCP/IP Flow Content in Network Switches at Gigabit Line Rates

The growth of the Internet has enabled it to become a critical component used by businesses, governments and individuals. While most of the traffic on the Internet is legitimate, a proportion of the traffic includes worms, computer viruses, network intrusions, computer espionage, security breaches and illegal behavior. This rogue traffic causes computer and network outages, reduces network throughput, and costs governments and companies billions of dollars each year. This dissertation investigates the problems associated with TCP stream processing in high-speed networks. It describes an architecture that simplifies the processing of TCP data streams in these environments and presents a hardware circuit capable of TCP stream processing on multi-gigabit networks for millions of simultaneous network connections. Live Internet traffic is analyzed using this new TCP processing circuit

Improving the Performance of Wide Area Networks

Research in to the performance of wide area data networks is described in this thesis. A model of wide area network packet delays is developed and used to direct the research in to methods of improving performance. Wide area networks are slow and expensive compared to the computer systems that rely on them for communication. Typically data networks are packet switched in order to make efficient use of resources. This can lead to contention, and the mechanisms for resolving contention can bring about further delays when demand for resources is high. In this thesis, network users are viewed as interacting decision makers with conflicting interests, and Game Theory is used to analyse the effects users have on each other’s performance. It is asserted in this thesis that wide area network performance is an ethical issue as well as a technical one. Compression is examined as a technique for reducing network traffic load. While load reductions can reduce the time packets spend waiting in buffer queues experimental results show the compression process itself can present a bottleneck if CPU resources are limited. The other inhibiting factor with regard to wide area network performance is the time it takes for a signal to propagate through a transmission medium. Propagation delays are bounded by the speed of light and becomes significant as the distance between computer systems increases. Mirrors and Caches are methods of bringing data closer to the user, thereby reducing propagation delays and capping traffic loads on long haul communication facilities. The performance benefits of replicating data within a wide area network environment are studied in this thesis

Performance analysis of a database caching system in a grid environment

Tese de mestrado. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 200

Reliable Server Pooling - Evaluierung, Optimierung und Erweiterung einer neuen IETF-Architektur

The Reliable Server Pooling (RSerPool) architecture currently under standardization by the IETF RSerPool Working Group is an overlay network framework to provide server replication and session failover capabilities to applications using it. These functionalities as such are not new, but their combination into one generic, application-independent framework is. Initial goal of this thesis is to gain insight into the complex RSerPool mechanisms by performing experimental and simulative proof-of-concept tests. The further goals are to systematically validate the RSerPool architecture and its protocols, provide improvements and optimizations where necessary and propose extensions if useful. Based on these evaluations, recommendations to implementers and users of RSerPool should be provided, giving guidelines for the tuning of system parameters and the appropriate configuration of application scenarios. In particular, it is also a goal to transfer insights, optimizations and extensions of the RSerPool protocols from simulation to reality and also to bring the achievements from research into application by supporting and contributing relevant results to the IETF's ongoing RSerPool standardization process. To achieve the described goals, a prototype implementation as well as a simulation model are designed and realized at first. Using a generic application model and appropriate performance metrics, the performance of RSerPool systems in failure-free and server failure scenarios is systematically evaluated in order to identify critical parameter ranges and problematic protocol behaviour. Improvements developed as result of these performance analyses are evaluated and finally contributed into the standardization process of RSerPool

Statistical learning in network architecture

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 167-[177]).The Internet has become a ubiquitous substrate for communication in all parts of society. However, many original assumptions underlying its design are changing. Amid problems of scale, complexity, trust and security, the modern Internet accommodates increasingly critical services. Operators face a security arms race while balancing policy constraints, network demands and commercial relationships. This thesis espouses learning to embrace the Internet's inherent complexity, address diverse problems and provide a component of the network's continued evolution. Malicious nodes, cooperative competition and lack of instrumentation on the Internet imply an environment with partial information. Learning is thus an attractive and principled means to ensure generality and reconcile noisy, missing or conflicting data. We use learning to capitalize on under-utilized information and infer behavior more reliably, and on faster time-scales, than humans with only local perspective. Yet the intrinsic dynamic and distributed nature of networks presents interesting challenges to learning. In pursuit of viable solutions to several real-world Internet performance and security problems, we apply statistical learning methods as well as develop new, network-specific algorithms as a step toward overcoming these challenges. Throughout, we reconcile including intelligence at different points in the network with the end-to-end arguments. We first consider learning as an end-node optimization for efficient peer-to-peer overlay neighbor selection and agent-centric latency prediction. We then turn to security and use learning to exploit fundamental weaknesses in malicious traffic streams. Our method is both adaptable and not easily subvertible. Next, we show that certain security and optimization problems require collaboration, global scope and broad views.(cont.) We employ ensembles of weak classifiers within the network core to mitigate IP source address forgery attacks, thereby removing incentive and coordination issues surrounding existing practice. Finally, we argue for learning within the routing plane as a means to directly optimize and balance provider and user objectives. This thesis thus serves first to validate the potential for using learning methods to address several distinct problems on the Internet and second to illuminate design principles in building such intelligent systems in network architecture.by Robert Edward Beverly, IV.Ph.D

Sixth Goddard Conference on Mass Storage Systems and Technologies Held in Cooperation with the Fifteenth IEEE Symposium on Mass Storage Systems

This document contains copies of those technical papers received in time for publication prior to the Sixth Goddard Conference on Mass Storage Systems and Technologies which is being held in cooperation with the Fifteenth IEEE Symposium on Mass Storage Systems at the University of Maryland-University College Inn and Conference Center March 23-26, 1998. As one of an ongoing series, this Conference continues to provide a forum for discussion of issues relevant to the management of large volumes of data. The Conference encourages all interested organizations to discuss long term mass storage requirements and experiences in fielding solutions. Emphasis is on current and future practical solutions addressing issues in data management, storage systems and media, data acquisition, long term retention of data, and data distribution. This year's discussion topics include architecture, tape optimization, new technology, performance, standards, site reports, vendor solutions. Tutorials will be available on shared file systems, file system backups, data mining, and the dynamics of obsolescence

NASA Tech Briefs, December 1990

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

Decentralized Network Based Mobility Management: Framework, System Design and Evaluation

wird nachgereich

NASA Tech Briefs, August 2000

Topics include: Simulation/Virtual Reality; Test and Measurement; Computer-Aided Design and Engineering; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Medical Design

Prediction and optimization techniques for performance enhancement of vehicular ad-hoc networks

Imperial Users onl