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December 23, 2011
Eitan Frachtenberg, Ali Heydari, Harry Li, Amir Michael, Jacob Na, Avery Nisbet, Pierluigi Sarti

Large-scale datacenters consume megawatts in power and cost hundreds of millions of dollars to equip. Reducing the energy and cost footprint of servers can therefore have substantial impact.

August 15, 2011
Katrin Kirchhoff, Andrei Alexandrescu

Recently, semi-supervised learning algorithms for phonetic classifiers have been proposed that have obtained promising results. Often, these algorithms attempt to satisfy learning criteria that are not inherent in the standard generative or discriminative training procedures for phonetic classifiers.

July 17, 2011
Lars Backstrom, Eytan Bakshy, Jon Kleinberg, Thomas Lento, Itamar Rosenn

An individual’s personal network — their set of social contacts — is a basic object of study in sociology. Studies of personal networks have focused on their size (the number of contacts) and their composition (in terms of categories such as kin and co-workers). Here we propose a new measure for the analysis of personal networks, based on the way in which an individual divides his or her attention across contacts. This allows us to contrast people who focus a large fraction of their interactions on a small set of close friends with people who disperse their attention more widely.

July 1, 2011
Mateusz Berezecki, Eitan Frachtenberg, Michael Paleczny, Ken Steele

Scaling data centers to handle task-parallel workloads requires balancing the cost of hardware, operations, and power. Low-power, low-core-count servers reduce costs in one of these dimensions, but may require additional nodes to provide the required quality of service or increase costs by underutilizing memory and other resources.

June 20, 2011
Rubao Lee, Tian Luo, Yin Huai, Fusheng Wang, Yongqiang He, Xiaodong Zhang

MapReduce has become an effective approach to big data analytics in large cluster systems, where SQL-like queries play important roles to interface between users and systems. However, based on our Face book daily operation results, certain types of queries are executed at an unacceptable low speed by Hive (a production SQL-to-MapReduce translator). In this paper, we demonstrate that existing SQL-to-MapReduce translators that operate in a one-operation-to-one-job mode and do not consider query correlations cannot generate high-performance MapReduce programs for certain queries, due to the mismatch between complex SQL structures and simple MapReduce framework. We propose and develop a system called Y Smart, a correlation aware SQL-to-MapReduce translator. Y Smart applies a set of rules to use the minimal number of MapReduce jobs to execute multiple correlated operations in a complex query. Y Smart can significantly reduce redundant computations, I/O operations and network transfers compared to existing translators. We have implemented Y Smart with intensive evaluation for complex queries on two Amazon EC2 clusters and one Face book production cluster. The results show that Y Smart can outperform Hive and Pig, two widely used SQL-to-MapReduce translators, by more than four times for query execution.

June 12, 2011
Dhruba Borthakur, Joydeep Sen Sarma, Jonathan Gray, Kannan Muthukkaruppan, Nicolas Spiegelberg, Hairong Kuang, Karthik Ranganathan, Dmytro Molkov, Aravind Menon, Samuel Rash, Rodrigo Schmidt, Amitanand Aiyer

Facebook recently deployed Facebook Messages, its first ever user-facing application built on the Apache Hadoop platform. Apache HBase is a database-like layer built on Hadoop designed to support billions of messages per day.