AT&T Labs

About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.

Where the sidewalk ends: extending the internet as graph using traceroutes from P2P users

Abstract: An accurate Internet topology graph is important in many areas of networking, from deciding ISP business relationships to diagnosing network anomalies. Most Internet mapping efforts have derived the network structure, at the level of interconnected autonomous systems (ASes), from a limited number of either BGP- or traceroute- based data sources. While techniques for charting the topology continue to improve, the growth of the number of vantage points is significantly outpaced by the rapid growth of the Internet.In this paper, we argue that a promising approach to revealing the hidden areas of the Internet topology is through active measurement from an observation platform that scales with the growing Internet. By leveraging measurements performed by an extension to a popular P2P system, we show that this approach indeed exposes significant new topological information. Based on traceroute measurements from more than 992,000 IPs in over 3,700 ASes distributed across the Internet hierarchy, our proposed heuristics identify 23,914 new AS links not visible in the publicly-available BGP data - 12.86% more customer-provider links and 40.99% more peering links, than previously reported. We validate our heuristics using data from a tier-1 ISP and show that they correctly filter out all false links introduced by public IP-to-AS mapping. We have made the identified set of links and their inferred relationships publically available

Stream warehousing with DataDepot

Abstract: We describe DataDepot, a tool for generating warehouses from streaming data feeds, such as network-traffic traces, router alerts, financial tickers, transaction logs, and so on. DataDepot is a streaming data warehouse designed to automate the ingestion of streaming data from a wide variety of sources and to maintain complex materialized views over these sources. As a streaming warehouse, DataDepot is similar to Data Stream Management Systems (DSMSs) with its emphasis on temporal data, best-effort consistency, and real-time response. However, as a data warehouse, DataDepot is designed to store tens to hundreds of terabytes of historical data, allow time windows measured in years or decades, and allow both real-time queries on recent data and deep analyses on historical data. In this paper we discuss the DataDepot architecture, with an emphasis on several of its novel and critical features. DataDepot is currently being used for five very large warehousing projects within ATT one of these warehouses ingests 500 Mbytes per minute (and is growing). We use these installations to illustrate streaming warehouse use and behavior, and design choices made in developing DataDepot. We conclude with a discussion of DataDepot applications and the efficacy of some optimizations.

A statistical information extraction system for Turkish

Abstract: This paper presents the results of a study on information extraction from unrestricted Turkish text using statistical language processing methods. In languages like English, there is a very small number of possible word forms with a given root word. However, languages like Turkish have very productive agglutinative morphology. Thus, it is an issue to build statistical models for specific tasks using the surface forms of the words, mainly because of the data sparseness problem. In order to alleviate this problem, we used additional syntactic information, i.e. the morphological structure of the words. We have successfully applied statistical methods using both the lexical and morphological information to sentence segmentation, topic segmentation, and name tagging tasks. For sentence segmentation, we have modeled the final inflectional groups of the words and combined it with the lexical model, and decreased the error rate to 4.34%, which is 21% better than the result obtained using only the surface forms of the words. For topic segmentation, stems of the words (especially nouns) have been found to be more effective than using the surface forms of the words and we have achieved 10.90% segmentation error rate on our test set according to the weighted TDT-2 segmentation cost metric. This is 32% better than the word-based baseline model. For name tagging, we used four different information sources to model names. Our first information source is based on the surface forms of the words. Then we combined the contextual cues with the lexical model, and obtained some improvement. After this, we modeled the morphological analyses of the words, and finally we modeled the tag sequence, and reached an F-Measure of 91.56%, according to the MUC evaluation criteria. Our results are important in the sense that, using linguistic information, i.e. morphological analyses of the words, and a corpus large enough to train a statistical model significantly improves these basic information extraction tasks for Turkish.

Compact name-independent routing with minimum stretch

Abstract: Given a weighted undirected network with arbitrary node names, we present a compact routing scheme, using a O(√n) space routing table at each node, and routing along paths of stretch 3, that is, at most thrice as long as the shortest paths. This is optimal in a very strong sense. It is known that no compact routing using o(n) space per node can route with stretch below 3. Also, it is known that any stretch below 5 requires Ω(√n) space per node.

Modeling web quality-of-experience on cellular networks

Abstract: Recent studies have shown that web browsing is one of the most prominent cellular applications. It is therefore important for cellular network operators to understand how radio network characteristics (such as signal strength, handovers, load, etc.) influence users' web browsing Quality-of-Experience (web QoE). Understanding the relationship between web QoE and network characteristics is a pre-requisite for cellular network operators to detect when and where degraded network conditions actually impact web QoE. Unfortunately, cellular network operators do not have access to detailed server-side or client-side logs to directly measure web QoE metrics, such as abandonment rate and session length. In this paper, we first devise a machine-learning-based mechanism to infer web QoE metrics from network traces accurately. We then present a large-scale study characterizing the impact of network characteristics on web QoE using a month-long anonymized dataset collected from a major cellular network provider. Our results show that improving signal-to-noise ratio, decreasing load and reducing handovers can improve user experience. We find that web QoE is very sensitive to inter-radio-access-technology (IRAT) handovers. We further find that higher radio data link rate does not necessarily lead to better web QoE. Since many network characteristics are interrelated, we also use machine learning to accurately model the influence of radio network characteristics on user experience metrics. This model can be used by cellular network operators to prioritize the improvement of network factors that most influence web QoE.