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.

Prefetching hyperlinks

Abstract: This paper develops a new method for prefetching Web pages into the client cache. Clients send reference information to Web servers, which aggregate the reference information in near-real-time and then disperse the aggregated information to all clients, piggybacked on GET responses. The information indicates how often hyperlink URLs embedded in pages have been previously accessed relative to the embedding page. Based on knowledge about which hyperlinks are generally popular, clients initiate prefetching of the hyperlinks and their embedded images according to any algorithm they prefer. Both client and server may cap the prefetching mechanism's space overhead and waste of network resources due to speculation. The result of these differences is improved prefetching: lower client latency (by 52.3%) and less wasted network bandwidth (24.0%).

Random evolution in massive graphs

Abstract: Many massive graphs (such as WWW graphs and Call graphs) share certain universal characteristics which can be described by the so-called the "power law" In this paper, we first briefly survey the history and previous work on power law graphs Then we give four evolution models for generating power law graphs by adding one node/edge at a time We show that for any given edge density and desired distributions for in-degrees and out-degrees (not necessarily the same, but adhered to certain general conditions), the resulting graph almost surely satisfy the power law and the in/out-degree conditions We show that our most general directed and undirected models include nearly all known models as special cases In addition, we consider another crucial aspect of massive graphs that is called "scale-free" in the sense that the frequency of sampling (wrt the growth rate) is independent of the parameter of the resulting power law graphs We show that our evolution models generate scale-free power law graphs

Layered quality adaptation for Internet video streaming

Abstract: Streaming audio and video applications are becoming increasingly popular on the Internet, and the lack of effective congestion control in such applications is now a cause for significant concern. The problem is one of adapting the compression without requiring video servers to reencode the data, and fitting the resulting stream into the rapidly varying available bandwidth. At the same time, rapid fluctuations in quality will be disturbing to the users and should be avoided. We present a mechanism for using layered video in the context of unicast congestion control. This quality adaptation mechanism adds and drops layers of the video stream to perform long-term coarse-grain adaptation, while using a TCP-friendly congestion control mechanism to react to congestion on very short timescales. The mismatches between the two timescales are absorbed using buffering at the receiver. We present an efficient scheme for the distribution of available bandwidth among the active layers. Our scheme allows the server to trade short-term improvement for long-term smoothing of quality. We discuss the issues involved in implementing and tuning such a mechanism, and present our simulation results.

Multicast topology inference from measured end-to-end loss

Abstract: The use of multicast inference on end-to-end measurement has been proposed as a means to infer network internal characteristics such as packet link loss rate and delay. We propose three types of algorithm that use loss measurements to infer the underlying multicast topology: (i) a grouping estimator that exploits the monotonicity of loss rates with increasing path length; (ii) a maximum-likelihood estimator (MLE); and (iii) a Bayesian estimator. We establish their consistency, compare their complexity and accuracy, and analyze the modes of failure and their asymptotic probabilities.