Cloud computing is a modern paradigm to provide services through the Internet. Load
balancing is a key aspect of cloud computing and avoids the situation in which some nodes …

Context: In recent years, the valuable knowledge that can be retrieved from petabyte scale
datasets–known as Big Data–led to the development of solutions to process information …

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 …

Recently, increasingly large amounts of data are generated from a variety of sources.
Existing data processing technologies are not suitable to cope with the huge amounts of …

For today's multi-million transistor designs, existing design verification techniques cannot
guarantee that first silicon is designed error free. Therefore, techniques are necessary to …

We analyze Hadoop workloads from three di? erent research clusters from a user-centric
perspective. The goal is to better understand data scientists' use of the system and how well …

In this paper, we present Sailfish, a new Map-Reduce framework for large scale data
processing. The Sailfish design is centered around aggregating intermediate data …

As a widely-used parallel computing framework for big data processing today, the Hadoop
MapReduce framework puts more emphasis on high-throughput of data than on low-latency …

The elapsed time of a parallel job depends on the completion time of its longest running
constituent. We present a static load balancing algorithm that distributes work evenly across …

Traditional parallel algorithms for mining frequent itemsets aim to balance load by equally
partitioning data among a group of computing nodes. We start this study by discovering a …