Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses
Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process …

Datacenter-scale computing for analytics workloads is increasingly common. High
operational costs force heterogeneous applications to share cluster resources for achieving …

Cloud computing promises great efficiencies by multiplexing resources among disparate
customers. For example, Amazon's Elastic Compute Cloud (EC2), Microsoft Azure, Google's …

There has been a recent industrial effort to develop multi-resource hierarchical schedulers.
However, the existing implementations have some shortcomings in that they might leave …

Big data analytics has attracted close attention from both industry and academic because of
its great benefits in cost reduction and better decision making. As the fast growth of various …

Multi-resource allocation in data centers determines a network and service management
task of crucial importance. While, traditionally computing systems are shared based on a …

Virtualized environments are attractive because they simplify cluster management, while
facilitating cost-effective workload consolidation. As a result, virtual machines in public …

Over the past decade, Apache Hadoop has become a leading framework for big data
processing. Single board computer (SBC) clusters, predominantly adopting Raspberry Pi …

Hadoop is an emerging framework for parallel big data processing. While becoming
popular, Hadoop is too complex for regular users to fully understand all the system …

The rise of Internet of Things sensors, social networking and mobile devices has led to an
explosion of available data. Gaining insights into this data has led to the area of Big Data …