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.

Improving end-to-end performance of the Web using server volumes and proxy filters

Abstract: The rapid growth of the World Wide Web has caused serious performance degradation on the Internet. This paper offers an end-to-end approach to improving Web performance by collectively examining the Web components --- clients, proxies, servers, and the network. Our goal is to reduce user-perceived latency and the number of TCP connections, improve cache coherency and cache replacement, and enable prefetching of resources that are likely to be accessed in the near future. In our scheme, server response messages include piggybacked information customized to the requesting proxy. Our enhancement to the existing request-response protocol does not require per-proxy state at a server, and a very small amount of transient per-server state at the proxy, and can be implemented without changes to HTTP 1.1. The server groups related resources into volumes (based on access patterns and the file system's directory structure) and applies a proxy-generated filter (indicating the type of information of interest to the proxy) to tailor the piggyback information. We present efficient data structures for constructing server volumes and applying proxy filters, and a transparent way to perform volume maintenance and piggyback generation at a router along the path between the proxy and the server. We demonstrate the effectiveness of our end-to-end approach by evaluating various volume construction and filtering techniques across a collection of large client and server logs.

Learn more, sample less: control of volume and variance in network measurement

Abstract: This paper deals with sampling objects from a large stream. Each object possesses a size, and the aim is to be able to estimate the total size of an arbitrary subset of objects whose composition is not known at the time of sampling. This problem is motivated from network measurements in which the objects are flow records exported by routers and the sizes are the number of packet or bytes reported in the record. Subsets of interest could be flows from a certain customer or flows from a worm attack. This paper introduces threshold sampling as a sampling scheme that optimally controls the expected volume of samples and the variance of estimators over any classification of flows. It provides algorithms for dynamic control of sample volumes and evaluates them on flow data gathered from a commercial Internet Protocol (IP) network. The algorithms are simple to implement and robust to variation in network conditions. The work reported here has been applied in the measurement infrastructure of the commercial IP network. To not have employed sampling would have entailed an order of magnitude greater capital expenditure to accommodate the measurement traffic and its processing.

Discrete Logarithms: The Past and the Future

Abstract: The first practical public key cryptosystem to be published, the Diffie–Hellman key exchange algorithm, was based on the assumption that discrete logarithms are hard to compute. This intractability hypothesis is also the foundation for the presumed security of a variety of other public key schemes. While there have been substantial advances in discrete log algorithms in the last two decades, in general the discrete log still appears to be hard, especially for some groups, such as those from elliptic curves. Unfortunately no proofs of hardness are available in this area, so it is necessary to rely on experience and intuition in judging what parameters to use for cryptosystems. This paper presents a brief survey of the current state of the art in discrete logs.