Enterprises today acquire vast volumes of data from different sources and leverage this information by means of data analysis to support effective decision-making and provide new …
Hadoop is a popular implementation of the MapReduce framework for running data- intensive jobs on clusters of commodity servers. Shuffle, the all-to-all input data fetching …
JM Hyde, WP Seering - MIT Space Engineering Research Center, 1990 - ntrs.nasa.gov
Spacecraft, space-borne robotic systems, and manufacturing equipment often utilize lightweight materials and configurations that give rise to vibration problems. Prior research …
K Chen, J Powers, S Guo, F Tian - IEEE Transactions on …, 2013 - ieeexplore.ieee.org
Running MapReduce programs in the cloud introduces this unique problem: how to optimize resource provisioning to minimize the monetary cost or job finish time for a specific job? We …
Z Tang, X Zhang, K Li, K Li - Future Generation Computer Systems, 2018 - Elsevier
Since MapReduce became an effective and popular programming framework for parallel data processing, key skew in intermediate data has become one of the important system …
One of the most successful techniques in large-scale data-intensive computations is MapReduce programming. MapReduce is based on a divide and conquer approach that …
MapReduce has emerged as a powerful tool for distributed and scalable processing of voluminous data. In this paper, we, for the first time, examine the problem of accommodating …
Abstract Challenges in Big Data analytics stem not only from volume, but also variety: extreme diversity in both data types (eg, text, images, and graphs) and in operations beyond …
Current cluster computing frameworks suffer from load imbalance and limited parallelism due to skewed data distributions, processing times, and machine speeds. We observe that …