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.

Multicast-based inference of network-internal characteristics: accuracy of packet loss estimation

Abstract: We explore the use of end-to-end multicast traffic as measurement probes to infer network internal characteristics. We have developed in an earlier paper a maximum likelihood estimator for packet loss rates on individual links based on losses observed by multicast receivers. This technique exploits the inherent correlation between such observations to infer the performance of paths between branch points in the multicast tree spanning the probe source and its receivers. We evaluate through analysis and simulation the accuracy of our estimator under a variety of network conditions. In particular, we report on the error between inferred loss rates and actual loss rates as we vary the network topology, propagation delay, packet drop policy, background traffic mix, and probe traffic type. In all but one case, estimated losses and probe losses agree to within 2 percent on average. We feel this accuracy is enough to reliably identify congested links in a wide-area internetwork.

Deflating the dimensionality curse using multiple fractal dimensions

Abstract: Nearest neighbor queries are important in many settings, including spatial databases (find the k closet cities) and multimedia databases (find the k most similar images). Previous analyses have concluded that nearest neighbor search is hopeless in high dimensions, due to the notorious "curse of dimensionality". However, their precise analysis over real data sets is still an open problem. The typical and often implicit assumption in previous studies is that the data is uniformly distributed, with independence between attributes. However, real data sets overwhelmingly disobey these assumptions; rather, they typically are skewed and exhibit intrinsic ("fractal") dimensionalities that are much lower than their embedding dimension, e.g., due to subtle dependencies between attributes. We show how the Hausdorff and correlation fractal dimensions of a data set can yield extremely accurate formulas that can predict I/O performance to within one standard deviation. The practical contributions of this work are our accurate formulas which can be used for query optimization in spatial and multimedia databases. The theoretical contribution is the 'deflation' of the dimensionality curse. Our theoretical and empirical results show that previous worst-case analysis of nearest neighbor search in high dimensions are over-pessimistic, to the point of being unrealistic. The performance depends critically on the intrinsic ("fractal") dimensionality as opposed to the embedding dimension that the uniformity assumption incorrectly implies.

Web Server Performance Modeling

Abstract: The advent of Web technology has made Web servers core elements of future communication networks. Although the amount of traffic that Web servers must handle has grown explosively during the last decade, the performance limitations and the proper tuning of Web servers are still not well understood. In this paper we present an end-to-end queueing model for the performance of Web servers, encompassing the impacts of client workload characteristics, server harware/software configuration, communication protocols, and interconnect topologies. The model has been implemented in a simulation tool, and performance predictions based on the model are shown to match very well with the performance of a Web server in a test lab environment. The simulation tool forms an excellent basis for development of a Decision Support System for the configuration tuning and sizing of Web servers.

Content-based methods for the management of digital music

Abstract: The literature on content-based music retrieval has largely finessed acoustic issues by using MIDI format music. This paper however considers content-based classification and retrieval of a typical (MPEG layer III) digital music archive. Two statistical techniques are investigated and appraised. Gaussian mixture modelling performs well with an accuracy of 92% on a music classification task. A tree-based vector quantization scheme offers marginally worse performance in a faster, scalable framework. Good results are also reported for music retrieval-by-similarity using the same techniques. Mel-frequency cepstral coefficients parameterize the audio well, though are slow to compute from the compressed domain. A new parameterization (MP3CEP), based on a partial decompression of MPEG layer III audio, is therefore proposed to facilitate music processing at user-interactive speeds. Overall, the techniques described provide useful tools in the management of a typical digital music library.