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Institution

AT&T Labs

Company
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.


Papers
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Journal ArticleDOI
TL;DR: The model is compared with simulations, the accuracy of the asymptotic approximations are examined, the increase in bandwidth needed to satisfy the tail-probability performance objective as compared with the mean objective, and regimes where statistical gain can and cannot be realized are shown.
Abstract: Simple and robust engineering rules for dimensioning bandwidth for elastic data traffic are derived for a single bottleneck link via normal approximations for a closed-queueing network (CQN) model in heavy traffic. Elastic data applications adapt to available bandwidth via a feedback control such as the transmission control protocol (TCP) or the available bit rate transfer capability in asynchronous transfer mode. The dimensioning rules satisfy a performance objective based on the mean or tail probability of the per-flow bandwidth. For the mean objective, we obtain a simple expression for the effective bandwidth of an elastic source. We provide a new derivation of the normal approximation in CQNs using more accurate asymptotic expansions and give an explicit estimate of the error in the normal approximation. A CQN model was chosen to obtain the desirable property that the results depend on the distribution of the file sizes only via the mean, and not the heavy-tail characteristics. We view the exogenous "load" in terms of the file sizes and consider the resulting flow of packets as dependent on the presence of other flows and the closed-loop controls. We compare the model with simulations, examine the accuracy of the asymptotic approximations, quantify the increase in bandwidth needed to satisfy the tail-probability performance objective as compared with the mean objective, and show regimes where statistical gain can and cannot be realized.

107 citations

Proceedings ArticleDOI
07 Oct 2002
TL;DR: A fast and memory efficient algorithm that generates a manifold triangular mesh S with or without boundary passing through a set of unorganized points P/spl sub//spl Rscr//sup 3/ with no other additional information is presented.
Abstract: We present a fast and memory efficient algorithm that generates a manifold triangular mesh S with or without boundary passing through a set of unorganized points P/spl sub//spl Rscr//sup 3/ with no other additional information. Nothing is assumed about the geometry or topology of the sampled manifold model, except for its reasonable smoothness. The speed of our algorithm is derived from a projection-based approach we use to determine the incident faces on a point. Our algorithm has successfully reconstructed the surfaces of unorganized point clouds of sizes varying from 10,000 to 100,000 in about 3-30 seconds on a 250 MHz, R10000 SGI Onyx2. Our technique can be specialized for different kinds of input and applications. For example, our algorithm can be specialized to handle data from height fields like terrain and range scan, even in the presence of noise. We have successfully generated meshes for range scan data of size 900,000 points in less than 40 seconds.

107 citations

Journal ArticleDOI
TL;DR: An approach for intelligent content placement that scales to large library sizes e.g., 100 Ks of videos by employing a Lagrangian relaxation-based decomposition technique combined with integer rounding and investigating the tradeoff between disk space and network bandwidth.
Abstract: IPTV service providers offering Video-on-Demand currently use servers at each metropolitan office to store all the videos in their library. With the rapid increase in library sizes, it will soon become infeasible to replicate the entire library at each office. We present an approach for intelligent content placement that scales to large library sizes (e.g., 100 Ks of videos). We formulate the problem as a mixed integer program (MIP) that takes into account constraints such as disk space, link bandwidth, and content popularity. To overcome the challenges of scale, we employ a Lagrangian relaxation-based decomposition technique combined with integer rounding. Our technique finds a near-optimal solution (e.g., within 1%–2%) with orders of magnitude speedup relative to solving even the linear programming (LP) relaxation via standard software. We also present simple strategies to address practical issues such as popularity estimation, content updates, short-term popularity fluctuation, and frequency of placement updates. Using traces from an operational system, we show that our approach significantly outperforms simpler placement strategies. For instance, our MIP-based solution can serve all requests using only half the link bandwidth used by least recently used (LRU) or least frequently used (LFU) cache replacement policies. We also investigate the tradeoff between disk space and network bandwidth.

106 citations

Journal ArticleDOI
TL;DR: This paper considers the case of network nodes that use a priority-service discipline to support multiple classes of service to determine an appropriate notion of effective bandwidths, and uses large-buffer asymptotics (large deviations principles) for workload tail probabilities as a theoretical basis.
Abstract: The notion of effective bandwidths has provided a useful practical framework for connection admission control and capacity planning in high-speed communication networks. The associated admissible set with a single linear boundary makes it possible to apply stochastic-loss-network (generalized-Erlang) models for capacity planning. We consider the case of network nodes that use a priority-service discipline to support multiple classes of service, and we wish to determine an appropriate notion of effective bandwidths. Just as was done previously for the first-in first-out (FIFO) discipline, we use large-buffer asymptotics (large deviations principles) for workload tail probabilities as a theoretical basis. We let each priority class have its own buffer and its own constraint on the probability of buffer overflow. Unfortunately, however, this leads to a constraint for each priority class. Moreover, the large-buffer asymptotic theory with priority classes does not produce an admissible set with linear boundaries, but we show that it nearly does and that a natural bound on the admissible set does have this property. We propose it as an approximation for priority classes; then there is one linear constraint for each priority class. This linear-admissible-set structure implies a new notion of effective bandwidths, where a given connection is associated with multiple effective bandwidths: one for the priority level of the given connection and one for each lower priority level. This structure can be used regardless of whether the individual effective bandwidths are determined by large-buffer asymptotics or by some other method.

106 citations

Journal ArticleDOI
TL;DR: This paper reviews natural language systems for generation, understanding and dialogue, focusing on the requirements and limitations these systems and user models place on each other and proposes avenues for future research.
Abstract: The fields of user modeling and natural language processing have been closely linked since the early days of user modeling. Natural language systems consult user models in order to improve their understanding of users' requirements and to generate appropriate and relevant responses. At the same time, the information natural language systems obtain from their users is expected to increase the accuracy of their user models. In this paper, we review natural language systems for generation, understanding and dialogue, focusing on the requirements and limitations these systems and user models place on each other. We then propose avenues for future research.

106 citations


Authors

Showing all 1881 results

NameH-indexPapersCitations
Yoshua Bengio2021033420313
Scott Shenker150454118017
Paul Shala Henry13731835971
Peter Stone130122979713
Yann LeCun121369171211
Louis E. Brus11334763052
Jennifer Rexford10239445277
Andreas F. Molisch9677747530
Vern Paxson9326748382
Lorrie Faith Cranor9232628728
Ward Whitt8942429938
Lawrence R. Rabiner8837870445
Thomas E. Graedel8634827860
William W. Cohen8538431495
Michael K. Reiter8438030267
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20225
202133
202069
201971
2018100
201791