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17 May 2004TL;DR: In this article, the authors identify the application level signatures by examining some available documentations, and packet-level traces, and then utilize the identified signatures to develop online filters that can efficiently and accurately track the P2P traffic even on high-speed network links.
Abstract: The ability to accurately identify the network traffic associated with different P2P applications is important to a broad range of network operations including application-specific traffic engineering, capacity planning, provisioning, service differentiation,etc. However, traditional traffic to higher-level application mapping techniques such as default server TCP or UDP network-port baseddisambiguation is highly inaccurate for some P2P applications.In this paper, we provide an efficient approach for identifying the P2P application traffic through application level signatures. We firstidentify the application level signatures by examining some available documentations, and packet-level traces. We then utilize the identified signatures to develop online filters that can efficiently and accurately track the P2P traffic even on high-speed network links.We examine the performance of our application-level identification approach using five popular P2P protocols. Our measurements show thatour technique achieves less than 5% false positive and false negative ratios in most cases. We also show that our approach only requires the examination of the very first few packets (less than 10packets) to identify a P2P connection, which makes our approach highly scalable. Our technique can significantly improve the P2P traffic volume estimates over what pure network port based approaches provide. For instance, we were able to identify 3 times as much traffic for the popular Kazaa P2P protocol, compared to the traditional port-based approach.
856 citations
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01 Jul 2002TL;DR: The design involves both a local mechanism for detecting and controlling an aggregate at a single router, and a cooperative pushback mechanism in which a router can ask upstream routers to control an aggregate.
Abstract: The current Internet infrastructure has very few built-in protection mechanisms, and is therefore vulnerable to attacks and failures. In particular, recent events have illustrated the Internet's vulnerability to both denial of service (DoS) attacks and flash crowds in which one or more links in the network (or servers at the edge of the network) become severely congested. In both DoS attacks and flash crowds the congestion is due neither to a single flow, nor to a general increase in traffic, but to a well-defined subset of the traffic --- an aggregate. This paper proposes mechanisms for detecting and controlling such high bandwidth aggregates. Our design involves both a local mechanism for detecting and controlling an aggregate at a single router, and a cooperative pushback mechanism in which a router can ask upstream routers to control an aggregate. While certainly not a panacea, these mechanisms could provide some needed relief from flash crowds and flooding-style DoS attacks. The presentation in this paper is a first step towards a more rigorous evaluation of these mechanisms.
808 citations
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TL;DR: This article outlines the overall strategy and summarizes a few key innovations of the team that won the first Netflix progress prize.
Abstract: This article outlines the overall strategy and summarizes a few key innovations of the team that won the first Netflix progress prize.
787 citations
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23 Sep 2001TL;DR: Graphviz is a heterogeneous collection of graph drawing tools containing batch layout programs, a platform for incremental layout, customizable graph editors, utility programs useful in graph visualization; and libraries for attributed graphs.
Abstract: Graphviz is a heterogeneous collection of graph drawing tools containing batch layout programs (dot, neato, fdp, twopi); a platform for incremental layout (Dynagraph); customizable graph editors (dotty, Grappa); a server for including graphs in Web pages (WebDot); support for graphs as COM objects (Montage); utility programs useful in graph visualization; and libraries for attributed graphs. The software is available under an Open Source license. The article[1] provides a detailed description of the package.
786 citations
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TL;DR: An on-line algorithm for learning preference functions that is based on Freund and Schapire's "Hedge" algorithm is considered, and it is shown that the problem of finding the ordering that agrees best with a learned preference function is NP-complete.
Abstract: There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order instances given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a two-stage approach in which one first learns by conventional means a binary preference function indicating whether it is advisable to rank one instance before another. Here we consider an on-line algorithm for learning preference functions that is based on Freund and Schapire's "Hedge" algorithm. In the second stage, new instances are ordered so as to maximize agreement with the learned preference function. We show that the problem of finding the ordering that agrees best with a learned preference function is NP-complete. Nevertheless, we describe simple greedy algorithms that are guaranteed to find a good approximation. Finally, we show how metasearch can be formulated as an ordering problem, and present experimental results on learning a combination of "search experts," each of which is a domain-specific query expansion strategy for a web search engine.
779 citations
Authors
Showing all 1881 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yoshua Bengio | 202 | 1033 | 420313 |
Scott Shenker | 150 | 454 | 118017 |
Paul Shala Henry | 137 | 318 | 35971 |
Peter Stone | 130 | 1229 | 79713 |
Yann LeCun | 121 | 369 | 171211 |
Louis E. Brus | 113 | 347 | 63052 |
Jennifer Rexford | 102 | 394 | 45277 |
Andreas F. Molisch | 96 | 777 | 47530 |
Vern Paxson | 93 | 267 | 48382 |
Lorrie Faith Cranor | 92 | 326 | 28728 |
Ward Whitt | 89 | 424 | 29938 |
Lawrence R. Rabiner | 88 | 378 | 70445 |
Thomas E. Graedel | 86 | 348 | 27860 |
William W. Cohen | 85 | 384 | 31495 |
Michael K. Reiter | 84 | 380 | 30267 |