The continuous increase in volume, variety and velocity of Big Data exposes datacenter
resource scaling to an energy utilization problem. Traditionally, datacenters employ x86-64 …

As the size of Big Data workloads keeps increasing, the evaluation of distributed frameworks
becomes a crucial task in order to identify potential performance bottlenecks that may delay …

Given the large and growing volume of big data and frequent use of complex analytical
queries, understanding energy efficiency of query processing has become a critical research …

The ever growing needs of Big Data applications are demanding challenging capabilities
which cannot be handled easily by traditional systems, and thus more and more …

MapReduce has emerged as a prominent programming model for data-intensive
computation. In this work, we study power-aware MapReduce scheduling in the speed …

We propose a new Exokernel-like layer to allow mutually untrusting physically deployed
services to efficiently share the resources of a data center. We believe that such a layer …

Hadoop is an open source software framework of distributed processing of big data. There
are many kinds of services in Hadoop ecosystem, such as HDFS, Map-Reduce, HBase …

Motivated by the explosion of Big Data analytics, performance improvements in low-power
(wimpy) systems and the increasing energy efficiency of GPUs, this paper presents a time …

Over the last few years, Big Data analysis (ie, crunching enormous amounts of data from
different sources to extract useful knowledge for improving business objectives) has …

This technique improves energy efficiency of MapKeduce system by using system
performance model without changing any component of the Mapleduce system. This …