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.

Resource management with hoses: point-to-cloud services for virtual private networks

Abstract: As IP technologies providing both tremendous capacity and the ability to establish dynamic security associations between endpoints emerge, virtual private networks (VPNs) are going through dramatic growth. The number of endpoints per VPN is growing and the communication pattern between endpoints is becoming increasingly hard to predict. Consequently, users are demanding dependable, dynamic connectivity between endpoints, with the network expected to accommodate any traffic matrix, as long as the traffic to the endpoints does not overwhelm the capacity of the respective ingress and egress links. We propose a new service interface, termed a hose, to provide the appropriate performance abstraction. A hose is characterized by the aggregate traffic to and from one endpoint in the VPN to a set of other endpoints in the VPN, and by an associated performance guarantee. Hoses provide important advantages to a VPN customer: (1) flexibility to send traffic to a set of endpoints without having to specify the detailed traffic matrix, and (2) reduction in the size of access links through multiplexing gains obtained from the natural aggregation of the flows between endpoints. As compared with the conventional point-to-point (or customer pipe) model for managing quality of service (QoS), hoses provide reduction in the state information a customer must maintain. On the other hand, hoses would appear to increase the complexity of the already difficult problem of resource management to support QoS. To manage network resources in the face of this increased uncertainty, we consider both conventional statistical multiplexing techniques, and a new resizing technique based on online measurements. To study these performance issues, we run trace-driven simulations, using traffic derived from AT&T's voice network and from a large corporate data network. From the customer's perspective, we find that aggregation of traffic at the hose level provides significant multiplexing gains. From the provider's perspective, we find that the statistical multiplexing and resizing techniques deal effectively with uncertainties about the traffic, providing significant gains over the conventional alternative of a mesh of statically sized customer pipes between endpoints.

On the "dimensionality curse" and the "self-similarity blessing"

Abstract: Spatial queries in high-dimensional spaces have been studied extensively. Among them, nearest neighbor queries are important in many settings, including spatial databases (Find the k closest 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". We show that this may be overpessimistic. We show that what determines the search performance (at least for R-tree-like structures) is the intrinsic dimensionality of the data set and not the dimensionality of the address space (referred to as the embedding dimensionality). The typical (and often implicit) assumption in many 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 the I/O performance to within one standard deviation on multiple real and synthetic data sets.

Performance of RAKE reception in dense multipath channels: implications of spreading bandwidth and selection diversity order

Abstract: We develop an analytical framework to quantify the effects of the spreading bandwidth (BW) on spread spectrum systems operating in dense multipath environments in terms of the receiver performance, the receiver complexity, and the multipath channel parameters. The focus of the paper is to characterize the symbol error probability (SEP) performance of a RAKE receiver tracking the L strongest multipath components in wide-sense stationary uncorrelated scattering (WSSUS) Gaussian channels with frequency-selective fading. Analytical SEP expressions of the RAKE receiver are derived in terms of the number of combined paths, the spreading BW and the multipath spread of the channel. The proposed problem is made analytically tractable by transforming the physical RAKE paths, which are correlated and ordered, into the domain of a "virtual RAKE" receiver with independent virtual paths. This results in a simple derivation of the SEP for a given spreading BW and an arbitrary number of combined paths.

Chapter 4 Voting procedures

Abstract: Voting procedures focus on the aggregation of individuals' preferences to produce collective decisions. In practice, a voting procedure is characterized by ballot responses and the way ballots are tallied to determine winners. Voters are assumed to have clear preferences over candidates and attempt to maximize satisfaction with the election outcome by their ballot responses. Such responses can include strategic misrepresentation of preferences. Voting procedures are formalized by social choice functions, which map ballot response profiles into election outcomes. We discuss broad classes of social choice functions as well as special cases such as plurality rule, approval voting, and Borda's point-count method. The simplest class is voting procedures for two-candidate elections. Conditions for social choice functions are presented for simple majority rule, the class of weighted majority rules, and for what are referred to as hierarchical representative systems. The second main class, which predominates in the literature, embraces all procedures for electing one candidate from three or more contenders. The multicandidate elect-one social choice functions in this broad class are divided into nonranked one-stage procedures, nonranked multistage procedures, ranked voting methods, and positional scoring rules. Nonranked methods include plurality check-one voting and approval voting, where each voter casts either no vote or a full vote for each candidate. On ballots for positional scoring methods, voters rank candidates from most preferred to least preferred. Topics for multicandidate methods include axiomatic characterizations, susceptibility to strategic manipulation, and voting paradoxes that expose questionable aspects of particular procedures. Other social choice functions are designed to elect two or more candidates for committee memberships from a slate of contenders. Proportional representation methods, including systems that elect members sequentially from a single ranked ballot with vote transfers in successive counting stages, are primary examples of this class.

Stochastic attribute-value grammars

Abstract: Probabilistic analogues of regular and context-free grammars are well known in computational linguistics, and currently the subject of intensive research. To date, however, no satisfactory probabilistic analogue of attribute-value grammars has been proposed: previous attempts have failed to define an adequate parameter-estimation algorithm.In the present paper, I define stochastic attribute-value grammars and give an algorithm for computing the maximum-likelihood estimate of their parameters. The estimation algorithm is adapted from Della Pietra, Della Pietra, and Lafferty (1995). To estimate model parameters, it is necessary to compute the expectations of certain functions under random fields. In the application discussed by Della Pietra, Della Pietra, and Lafferty (representing English orthographic constraints), Gibbs sampling can be used to estimate the needed expectations. The fact that attribute-value grammars generate constrained languages makes Gibbs sampling inapplicable, but I show that sampling can be done using the more general Metropolis-Hastings algorithm.