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.

IP fault localization via risk modeling

Abstract: Automated, rapid, and effective fault management is a central goal of large operational IP networks Today's networks suffer from a wide and volatile set of failure modes, where the underlying fault proves difficult to detect and localize, thereby delaying repair One of the main challenges stems from operational reality: IP routing and the underlying optical fiber plant are typically described by disparate data models and housed in distinct network management systems We introduce a fault-localization methodology based on the use of risk models and an associated troubleshooting system, SCORE (Spatial Correlation Engine), which automatically identifies likely root causes across layers In particular, we apply SCORE to the problem of localizing link failures in IP and optical networks In experiments conducted on a tier-1 ISP backbone, SCORE proved remarkably effective at localizing optical link failures using only IP-layer event logs Moreover, SCORE was often able to automatically uncover inconsistencies in the databases that maintain the critical associations between the IP and optical networks

Characterizing and modeling internet traffic dynamics of cellular devices

Abstract: Understanding Internet traffic dynamics in large cellular networks is important for network design, troubleshooting, performance evaluation, and optimization In this paper, we present the results from our study, which is based upon a week-long aggregated flow level mobile device traffic data collected from a major cellular operator's core network In this study, we measure and characterize the spatial and temporal dynamics of mobile Internet traffic We distinguish our study from other related work by conducting the measurement at a larger scale and exploring mobile data traffic patterns along two new dimensions -- device types and applications that generate such traffic patterns Based on the findings of our measurement analysis, we propose a Zipf-like model to capture the volume distribution of application traffic and a Markov model to capture the volume dynamics of aggregate Internet traffic We further customize our models for different device types using an unsupervised clustering algorithm to improve prediction accuracy

Spatio-Temporal Compressive Sensing and Internet Traffic Matrices (Extended Version)

Abstract: Despite advances in measurement technology, it is still challenging to reliably compile large-scale network datasets. For example, because of flaws in the measurement systems or difficulties posed by the measurement problem itself, missing, ambiguous, or indirect data are common. In the case where such data have spatio-temporal structure, it is natural to try to leverage this structure to deal with the challenges posed by the problematic nature of the data. Our work involving network datasets draws on ideas from the area of compressive sensing and matrix completion, where sparsity is exploited in estimating quantities of interest. However, the standard results on compressive sensing are: 1) reliant on conditions that generally do not hold for network datasets; and 2) do not allow us to exploit all we know about their spatio-temporal structure. In this paper, we overcome these limitations with an algorithm that has at its heart the same ideas espoused in compressive sensing, but adapted to the problem of network datasets. We show how this algorithm can be used in a variety of ways, in particular on traffic data, to solve problems such as simple interpolation of missing values, traffic matrix inference from link data, prediction, and anomaly detection. The elegance of the approach lies in the fact that it unifies all of these tasks and allows them to be performed even when as much as 98% of the data is missing.

The computational complexity of knot and link problems

Abstract: We consider the problem of deciding whether a polygonal knot in 3-dimensional Euclidean space is unknotted, ie., capable of being continuously deformed without self-intersection so that it lies in a plane. We show that this problem, UNKNOTTING PROBLEM is in NP. We also consider the problem, SPLITTING PROBLEM of determining whether two or more such polygons can be split, or continuously deformed without self-intersection so that they occupy both sides of a plane without intersecting it. We show that it also is in NP. Finally, we show that the problem of determining the genus of a polygonal knot (a generalization of the problem of determining whether it is unknotted) is in PSPACE. We also give exponential worst-case running time bounds for deterministic algorithms to solve each of these problems. These algorithms are based on the use of normal surfaces and decision procedures due to W. Haken, with recent extensions by W. Jaco and J. L. Tollefson.