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.

Moment-method estimation of the Ricean K-factor

Abstract: In many radio propagation environments, the time-varying envelope of the received signal can be statistically described by a Ricean distribution. Traditional methods for estimating the Ricean K-factor from measured power versus time are relatively cumbersome and time consuming. We describe a simple and rapid approach wherein the K-factor is an exact function of moments estimated from time-series data. Comparisons with empirical distributions for fixed wireless paths validate the method.

Virtual routers on the move: live router migration as a network-management primitive

Abstract: The complexity of network management is widely recognized as one of the biggest challenges facing the Internet today. Point solutions for individual problems further increase system complexity while not addressing the underlying causes. In this paper, we argue that many network-management problems stem from the same root cause---the need to maintain consistency between the physical and logical configuration of the routers. Hence, we propose VROOM (Virtual ROuters On the Move), a new network-management primitive that avoids unnecessary changes to the logical topology by allowing (virtual) routers to freely move from one physical node to another. In addition to simplifying existing network-management tasks like planned maintenance and service deployment, VROOM can also help tackle emerging challenges such as reducing energy consumption. We present the design, implementation, and evaluation of novel migration techniques for virtual routers with either hardware or software data planes. Our evaluation shows that VROOM is transparent to routing protocols and results in no performance impact on the data traffic when a hardware-based data plane is used.

Game theory, on-line prediction and boosting

Abstract: We study the close connections between game theory, on-line prediction and boosting. After a brief review of game theory, we describe an algorithm for learning to play repeated games based on the on-line prediction methods of Littlestone and Warmuth. The analysis of this algorithm yields a simple proof of von Neumann’s famous minmax theorem, as well as a provable method of approximately solving a game. We then show that the on-line prediction model is obtained by applying this gameplaying algorithm to an appropriate choice of game and that boosting is obtained by applying the same algorithm to the “dual” of this game.

WordsEye: an automatic text-to-scene conversion system

Abstract: Natural language is an easy and effective medium for describing visual ideas and mental images. Thus, we foresee the emergence of language-based 3D scene generation systems to let ordinary users quickly create 3D scenes without having to learn special software, acquire artistic skills, or even touch a desktop window-oriented interface. WordsEye is such a system for automatically converting text into representative 3D scenes. WordsEye relies on a large database of 3D models and poses to depict entities and actions. Every 3D model can have associated shape displacements, spatial tags, and functional properties to be used in the depiction process. We describe the linguistic analysis and depiction techniques used by WordsEye along with some general strategies by which more abstract concepts are made depictable.

A first-principles approach to understanding the internet's router-level topology

Abstract: A detailed understanding of the many facets of the Internet's topological structure is critical for evaluating the performance of networking protocols, for assessing the effectiveness of proposed techniques to protect the network from nefarious intrusions and attacks, or for developing improved designs for resource provisioning. Previous studies of topology have focused on interpreting measurements or on phenomenological descriptions and evaluation of graph-theoretic properties of topology generators. We propose a complementary approach of combining a more subtle use of statistics and graph theory with a first-principles theory of router-level topology that reflects practical constraints and tradeoffs. While there is an inevitable tradeoff between model complexity and fidelity, a challenge is to distill from the seemingly endless list of potentially relevant technological and economic issues the features that are most essential to a solid understanding of the intrinsic fundamentals of network topology. We claim that very simple models that incorporate hard technological constraints on router and link bandwidth and connectivity, together with abstract models of user demand and network performance, can successfully address this challenge and further resolve much of the confusion and controversy that has surrounded topology generation and evaluation.