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.

Dasu: pushing experiments to the internet's edge

Abstract: We present Dasu, a measurement experimentation platform for the Internet's edge. Dasu supports both controlled network experimentation and broadband characterization, building on public interest on the latter to gain the adoption necessary for the former. We discuss some of the challenges we faced building a platform for the Internet's edge, describe our current design and implementation, and illustrate the unique perspective it brings to Internet measurement. Dasu has been publicly available since July 2010 and has been installed by over 90,000 users with a heterogeneous set of connections spreading across 1,802 networks and 147 countries.

Transmission of 8 DWDM channels at 20 Gb/s over 160 km of standard fiber using a cascade of semiconductor optical amplifiers

Abstract: Eight DWDM channels in the wavelength region 1558-1570 nm, spaced at 200 GHz, are modulated at 20 Gb/s and transmitted over 160 km of SSMF (four 40-km spans) using four in-line SOA's. Transmitter booster-amplifiers and receiver preamplifiers are also SOA's, Q-factors better than 17 dB, corresponding to a BER of less than 3/spl middot/10/sup -13/, have been measured for all channels.

Characterizing geospatial dynamics of application usage in a 3G cellular data network

Abstract: Recent studies on cellular network measurement have provided the evidence that significant geospatial correlations, in terms of traffic volume and application access, exist in cellular network usage. Such geospatial correlation patterns provide local optimization opportunities to cellular network operators for handling the explosive growth in the traffic volume observed in recent years. To the best of our knowledge, in this paper, we provide the first fine-grained characterization of the geospatial dynamics of application usage in a 3G cellular data network. Our analysis is based on two simultaneously collected traces from the radio access network (containing location records) and the core network (containing traffic records) of a tier-1 cellular network in the United States. To better understand the application usage in our data, we first cluster cell locations based on their application distributions and then study the geospatial dynamics of application usage across different geographical regions. The results of our measurement study present cellular network operators with fine-grained insights that can be leveraged to tune network parameter settings.

Architectures and Protocols for Capacity Efficient, Highly Dynamic and Highly Resilient Core Networks [Invited]

Abstract: The Core Optical Networks (CORONET) program addresses the development of architectures, protocols, and network control and management to support the future advanced requirements of both commercial and government networks, with a focus on highly dynamic and highly resilient multi-terabit core networks. CORONET encompasses a global network supporting a combination of IP and wavelength services. Satisfying the aggressive requirements of the program required a comprehensive approach addressing connection setup, restoration, quality of service, network design, and nodal architecture. This paper addresses the major innovations developed in Phase 1 of the program by the team led by Telcordia and AT&T. The ultimate goal is to transfer the technology to commercial and government networks for deployment in the next few years.

Update rules for parameter estimation in Bayesian networks

Abstract: This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [13]. We provide a unified framework for parameter estimation that encompasses both on-line learning, where the model is continuously adapted to new data cases as they arrive, and the more traditional batch learning, where a pre-accumulated set of samples is used in a one-time model selection process. In the batch case, our framework encompasses both the gradient projection algorithm [2, 3] and the EM algorithm [15] for Bayesian networks. The framework also leads to new on-line and batch parameter update schemes, including a parameterized version of EM. We provide both empirical and theoretical results indicating that parameterized EM allows faster convergence to the maximum likelihood parameters than does standard EM.