Distributed data management using MapReduce
MapReduce is a framework for processing and managing large-scale datasets in a
distributed cluster, which has been used for applications such as generating search indexes …
distributed cluster, which has been used for applications such as generating search indexes …
A survey of large-scale analytical query processing in MapReduce
C Doulkeridis, K Nørvåg - The VLDB journal, 2014 - Springer
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 …
information by means of data analysis to support effective decision-making and provide new …
ishuffle: Improving hadoop performance with shuffle-on-write
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 …
intensive jobs on clusters of commodity servers. Shuffle, the all-to-all input data fetching …
Using input command pre-shaping to suppress multiple mode vibration
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 …
lightweight materials and configurations that give rise to vibration problems. Prior research …
CRESP: Towards optimal resource provisioning for MapReduce computing in public clouds
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 …
resource provisioning to minimize the monetary cost or job finish time for a specific job? We …
An intermediate data placement algorithm for load balancing in spark computing environment
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 …
data processing, key skew in intermediate data has become one of the important system …
Mapreduce data skewness handling: a systematic literature review
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 programming. MapReduce is based on a divide and conquer approach that …
Online load balancing for mapreduce with skewed data input
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 …
voluminous data. In this paper, we, for the first time, examine the problem of accommodating …
[PDF][PDF] Managing Skew in Hadoop.
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 …
extreme diversity in both data types (eg, text, images, and graphs) and in operations beyond …
Rock you like a hurricane: Taming skew in large scale analytics
L Bindschaedler, J Malicevic, N Schiper… - Proceedings of the …, 2018 - dl.acm.org
Current cluster computing frameworks suffer from load imbalance and limited parallelism
due to skewed data distributions, processing times, and machine speeds. We observe that …
due to skewed data distributions, processing times, and machine speeds. We observe that …