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.

Trajectory sampling for direct traffic observation

Abstract: Traffic measurement is a critical component for the control and engineering of communication networks. We argue that traffic measurement should make it possible to obtain the spatial flow of traffic through the domain, i.e., the paths followed by packets between any ingress and egress point of the domain. Most resource allocation and capacity planning tasks can benefit from such information. Also, traffic measurements should be obtained without a routing model and without knowledge of network state. This allows the traffic measurement process to he resilient to network failures and state uncertainty. We propose a method that allows the direct inference of traffic flows through a domain by observing the trajectories of a subset of all packets traversing the network. The key advantages of the method are that (1) it does not rely on routing state; (2) its implementation cost is small; and (3) the measurement reporting traffic is modest and can be controlled precisely. The key idea of the method is to sample packets based on a hash function computed over the packet content. Using the same hash function will yield the same sample set of packets in the entire domain, and enables us to reconstruct packet trajectories.

Inference for Deterministic Simulation Models: The Bayesian Melding Approach

Abstract: Deterministic simulation models are used in many areas of science, engineering, and policy making. Typically, these are complex models that attempt to capture underlying mechanisms in considerable detail, and they have many user-specified inputs. The inputs are often specified by some form of trial-and-error approach in which plausible values are postulated, the corresponding outputs inspected, and the inputs modified until plausible outputs are obtained. Here we address the issue of more formal inference for such models. A probabilistic approach, called Bayesian synthesis, was shown to suffer from the Borel paradox, according to which the results can depend on the parameterization of the model. We propose a modified approach, called Bayesian melding which takes into full account information and uncertainty about both inputs and outputs to the model, while avoiding the Borel paradox. This is done by recognizing the existence of two priors, one implicit and one explicit, on each input and output; ...

Publius: a robust, tamper-evident, censorship-resistant web publishing system

Abstract: We describe a system that we have designed and implemented for publishing content on the web. Our publishing scheme has the property that it is very difficult for any adversary to censor or modify the content. In addition, the identity of the publisher is protected once the content is posted. Our system differs from others in that we provide tools for updating or deleting the published content, and users can browse the content in the normal point and click manner using a standard web browser and a client-side proxy that we provide. All of our code is freely available.

On the use and performance of content distribution networks

Abstract: Content distribution networks (CDNs) are a mechanism to deliver content to end users on behalf of origin Web sites. Content distribution offloads work from origin servers by serving some or all of the contents of Web pages. We found an order of magnitude increase in the number and percentage of popular origin sites using CDNs between November 1999 and December 2000.In this paper we discuss how CDNs are commonly used on the Web and define a methodology to study how well they perform. A performance study was conducted over a period of months on a set of CDN companies employing the techniques of DNS redirection and URL rewriting to balance load among their servers. Some CDNs generally provide better results than others when we examine results from a set of clients. The performance of one CDN company clearly improved between the two testing periods in our study due to a dramatic increase in the number of distinct servers employed in its network. More generally, the results indicate that use of a DNS lookup in the critical path of a resource retrieval does not generally result in better server choices being made relative to client response time in either average or worst case situations.

Context-sensitive learning methods for text categorization

Abstract: Two recently implemented machine-learning algorithms, RIPPERand sleeping-experts for phrases, are evaluated on a number of large text categorization problems. These algorithms both construct classifiers that allow the “context” of a word w to affect how (or even whether) the presence or absence of w will contribute to a classification. However, RIPPER and sleeping-experts differ radically in many other respects: differences include different notions as to what constitutes a context, different ways of combining contexts to construct a classifier, different methods to search for a combination of contexts, and different criteria as to what contexts should be included in such a combination. In spite of these differences, both RIPPER and sleeping-experts perform extremely well across a wide variety of categorization problems, generally outperforming previously applied learning methods. We view this result as a confirmation of the usefulness of classifiers that represent contextual information.