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.

COPE: traffic engineering in dynamic networks

Abstract: Traffic engineering plays a critical role in determining the performance and reliability of a network. A major challenge in traffic engineering is how to cope with dynamic and unpredictable changes in traffic demand. In this paper, we propose COPE, a class of traffic engineering algorithms that optimize for the expected scenarios while providing a worst-case guarantee for unexpected scenarios. Using extensive evaluations based on real topologies and traffic traces, we show that COPE can achieve efficient resource utilization and avoid network congestion in a wide variety of scenarios.

Mining business databases

Abstract: a phenomenal rate. From the financial sector to telecommunications operations , companies increasingly rely on analysis of huge amounts of data to compete. Although ad hoc mixtures of statistical techniques and file management tools once sufficed for digging through mounds of corporate data, the size of modern data warehouses, the mission-critical nature of the data, and the speed with which analyses need to be made now call for a new approach. A new generation of techniques and tools is emerging to intelligently assist humans in analyzing mountains of data and finding critical nuggets of useful knowledge, and in some cases to perform analyses automatically. These techniques and tools are the subject of the growing field of knowledge discovery in databases (KDD) [5]. KDD is an umbrella term describing a variety of activities for making sense of data. We use the term to describe the overall process of finding useful patterns in data, including not only the data mining step of running specific discovery algorithms but also pre-and postprocessing and a host of other important activities. Our goal here is to provide a brief overview of the key issues in knowledge discovery in an industrial context and outline representative applications. The different data mining methods at the core of the KDD process can have different goals. In general, we distinguish two types: • Verification, in which the system is limited to verifying a user's hypothesis, and • Discovery, in which the system finds new patterns. Ad hoc techniques—no longer adequate for sifting through vast collections of data—are giving way to data mining and knowledge discovery for turning corporate data into competitive business advantage.

Properties and prediction of flow statistics from sampled packet streams

Abstract: Many routers can generate and export statistics on flows of packets that traverse them. Increasingly, high end routers form flow statistics from only a sampled packet stream in order to manage resource consumption involved.This paper addresses three questions. Firstly: what are the downstream consequences for the measurement infrastructure? Long traffic flows will be split up if the time between sampled packets exceeds the flow timeout. Using packet header traces we show that flows generated by increasingly prevalent peer-to-peer applicalions are vulnerable to this effect.Secondly: can the volume of packet-sampled flow statistics be easily determined? We develop a simple model that predicts both the export rate of flow packet-sampled flow statistics and the number of active flows. It uses unsampled flow statistics---those commonly currently collected--as its data, i.e., it does not rely on having packet header traces available.Thirdly: what properties of the original traffic stream can be inferred from the packet sampled flow statistics? We show that as well as estimating total bytes and packets, one can also infer more detail, specifically the number and average length of flows in the unsampled traffic stream, even though some flows will have no packets sampled. We believe that this information is useful, both for understanding source traffic, e.g. the dependence of flow lengths on application type, and also monitoring changes in the composition of the traffic, e.g., a flood of short flows during a DoS attack. In all cases, we evaluate our approach using packet header traces gathered in backbone and campus networks.

Class-based graph anonymization for social network data

Abstract: The recent rise in popularity of social networks, such as Facebook and MySpace, has created large quantities of data about interactions within these networks. Such data contains many private details about individuals so anonymization is required prior to attempts to make the data more widely available for scientific research. Prior work has considered simple graph data to be anonymized by removing all non-graph information and adding or deleting some edges. Since social network data is richer in details about the users and their interactions, loss of details due to anonymization limits the possibility for analysis. We present a new set of techniques for anonymizing social network data based on grouping the entities into classes, and masking the mapping between entities and the nodes that represent them in the anonymized graph. Our techniques allow queries over the rich data to be evaluated with high accuracy while guaranteeing resilience to certain types of attack. To prevent inference of interactions, we rely on a critical "safety condition" when forming these classes. We demonstrate utility via empirical data from social networking settings. We give examples of complex queries that may be posed and show that they can be answered over the anonymized data efficiently and accurately.

Handbook of massive data sets

Abstract: Preface.- Part I: Internet and the World Wide Web.- Part II: Massive Graphs.- Part III: String Processing and Data Compression.- Part IV: External Memory Algorithms and Data Structures.- Part V: Optimization.- Part VI: Data Management.- Part VII: Architecture Issues.- Part VIII: Applications.- Index.