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.

Set cover algorithms for very large datasets

Abstract: The problem of Set Cover - to find the smallest subcollection of sets that covers some universe - is at the heart of many data and analysis tasks. It arises in a wide range of settings, including operations research, machine learning, planning, data quality and data mining. Although finding an optimal solution is NP-hard, the greedy algorithm is widely used, and typically finds solutions that are close to optimal. However, a direct implementation of the greedy approach, which picks the set with the largest number of uncovered items at each step, does not behave well when the input is very large and disk resident. The greedy algorithm must make many random accesses to disk, which are unpredictable and costly in comparison to linear scans. In order to scale Set Cover to large datasets, we provide a new algorithm which finds a solution that is provably close to that of greedy, but which is much more efficient to implement using modern disk technology. Our experiments show a ten-fold improvement in speed on moderately-sized datasets, and an even greater improvement on larger datasets.

Exploiting mobility in proportional fair cellular scheduling: measurements and algorithms

Abstract: Proportional Fair (PF) scheduling algorithms are the de facto standard in cellular networks. They exploit the users' channel state diversity (induced by fast-fading) and are optimal for stationary channel state distributions and an infinite time-horizon. However, mobile users experience a nonstationary channel, due to slow-fading (on the order of seconds), and are associated with base stations for short periods. Hence, we develop the Predictive Finite-horizon PF Scheduling ((PF)2S) Framework that exploits mobility. We present extensive channel measurement results from a 3G network and characterize mobility-induced channel state trends. We show that a user's channel state is highly reproducible and leverage that to develop a data rate prediction mechanism. We then present a few channel allocation estimation algorithms that exploit the prediction mechanism. Our trace-based simulations consider instances of the ((PF)2S) Framework composed of combinations of prediction and channel allocation estimation algorithms. They indicate that the framework can increase the throughput by 15%-55% compared to traditional PF schedulers, while improving fairness.

Global optimization by continuous grasp

Abstract: We introduce a novel global optimization method called Continuous GRASP (C-GRASP) which extends Feo and Resende’s greedy randomized adaptive search procedure (GRASP) from the domain of discrete optimization to that of continuous global optimization. This stochastic local search method is simple to implement, is widely applicable, and does not make use of derivative information, thus making it a well-suited approach for solving global optimization problems. We illustrate the effectiveness of the procedure on a set of standard test problems as well as two hard global optimization problems.

Using lightweight modeling to understand chord

Abstract: Correctness of the Chord ring-maintenance protocol would mean that the protocol can eventually repair all disruptions in the ring structure, given ample time and no further disruptions while it is working. In other words, it is "eventual reachability." Under the same assumptions about failure behavior as made in the Chord papers, no published version of Chord is correct. This result is based on modeling the protocol in Alloy and analyzing it with the Alloy Analyzer. By combining the right selection of pseudocode and textual hints from several papers, and fixing flaws revealed by analysis, it is possible to get a version that may be correct. The paper also discusses the significance of these results, describes briefly how Alloy is used to model and reason about Chord, and compares Alloy analysis to model-checking.

Rethinking the progress bar

Abstract: Progress bars are prevalent in modern user interfaces. Typically, a linear function is employed such that the progress of the bar is directly proportional to how much work has been completed. However, numerous factors cause progress bars to proceed at non-linear rates. Additionally, humans perceive time in a non-linear way. This paper explores the impact of various progress bar behaviors on user perception of process duration. The results are used to suggest several design considerations that can make progress bars appear faster and ultimately improve users' computing experience.