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.

Byzantine quorum systems

Abstract: Quorum systems are well-known tools for ensuring the consistency and availability of replicated data despite the benign failure of data repositories. In this paper we consider the arbitrary (Byzantine) failure of data repositories and present the first study of quorum system requirements and constructions that ensure data availability and consistency despite these failures. We also consider the load associated with our quorum systems, i.e., the minimal access probability of the busiest server. For services subject to arbitrary failures, we demonstrate quorum systems over \(n\) servers with a load of \(O(\frac{1}{\sqrt{n}})\), thus meeting the lower bound on load for benignly fault-tolerant quorum systems. We explore several variations of our quorum systems and extend our constructions to cope with arbitrary client failures.

Approximate XML joins

Abstract: XML is widely recognized as the data interchange standard for tomorrow, because of its ability to represent data from a wide variety sources. Hence, XML is likely to be the format through which data from multiple sources is integrated.In this paper we study the problem of integrating XML data sources through correlations realized as join operations. A challenging aspect of this operation is the XML document structure. Two documents might convey approximately or exactly the same information but may be quite different in structure. Consequently approximate match in structure, in addition to, content has to be folded in the join operation. We quantify approximate match in structure and content using well defined notions of distance. For structure, we propose computationally inexpensive lower and upper bounds for the tree edit distance metric between two trees. We then show how the tree edit distance, and other metrics that quantify distance between trees, can be incorporated in a join framework. We introduce the notion of reference sets to facilitate this operation. Intuitively, a reference set consists of data elements used to project the data space. We characterize what constitutes a good choice of a reference set and we propose sampling based algorithms to identify them. This gives rise to a variety of algorithmic approaches for the problem, which we formulate and analyze. We demonstrate the practical utility of our solutions using large collections of real and synthetic XML data sets.

Towards automated performance diagnosis in a large IPTV network

Abstract: IPTV is increasingly being deployed and offered as a commercial service to residential broadband customers. Compared with traditional ISP networks, an IPTV distribution network (i) typically adopts a hierarchical instead of mesh-like structure, (ii) imposes more stringent requirements on both reliability and performance, (iii) has different distribution protocols (which make heavy use of IP multicast) and traffic patterns, and (iv) faces more serious scalability challenges in managing millions of network elements. These unique characteristics impose tremendous challenges in the effective management of IPTV network and service.In this paper, we focus on characterizing and troubleshooting performance issues in one of the largest IPTV networks in North America. We collect a large amount of measurement data from a wide range of sources, including device usage and error logs, user activity logs, video quality alarms, and customer trouble tickets. We develop a novel diagnosis tool called Giza that is specifically tailored to the enormous scale and hierarchical structure of the IPTV network. Giza applies multi-resolution data analysis to quickly detect and localize regions in the IPTV distribution hierarchy that are experiencing serious performance problems. Giza then uses several statistical data mining techniques to troubleshoot the identified problems and diagnose their root causes. Validation against operational experiences demonstrates the effectiveness of Giza in detecting important performance issues and identifying interesting dependencies. The methodology and algorithms in Giza promise to be of great use in IPTV network operations.

Hierarchical In-Network Data Aggregation with Quality Guarantees

Abstract: Earlier work has demonstrated the effectiveness of in-network data aggregation in order to minimize the amount of messages exchanged during continuous queries in large sensor networks. The key idea is to build an aggregation tree, in which parent nodes aggregate the values received from their children. Nevertheless, for large sensor networks with severe energy constraints the reduction obtained through the aggregation tree might not be sufficient. In this paper we extend prior work on in-network data aggregation to support approximate evaluation of queries to further reduce the number of exchanged messages among the nodes and extend the longevity of the network. A key ingredient to our framework is the notion of the residual mode of operation that is used to eliminate messages from sibling nodes when their cumulative change is small. We introduce a new algorithm, based on potential gains, which adaptively redistributes the error thresholds to those nodes that benefit the most and tries to minimize the total number of transmitted messages in the network. Our experiments demonstrate that our techniques significantly outperform previous approaches and reduce the network traffic by exploiting the super-imposed tree hierarchy.

Hidden Markov modeling of flat fading channels

Abstract: Hidden Markov models (HMMs) are a powerful tool for modeling stochastic random processes. They are general enough to model with high accuracy a large variety of processes and are relatively simple allowing us to compute analytically many important parameters of the process which are very difficult to calculate for other models (such as complex Gaussian processes). Another advantage of using HMMs is the existence of powerful algorithms for fitting them to experimental data and approximating other processes. In this paper, we demonstrate that communication channel fading can be accurately modeled by HMMs, and we find closed-form solutions for the probability distribution of fade duration and the number of level crossings.