The rapid growth in data yields challenges to process data efficiently using current high-
performance server architectures such as big Xeon cores. Furthermore, physical design …

Emerging big data applications require a significant amount of server computational power.
However, the rapid growth in the data yields challenges to process them efficiently using …

There is growing interest to replace traditional servers with low-power multicore systems
such as ARM Cortex-A9. However, such systems are typically provisioned for mobile …

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 …

Today, huge amounts of data are being collected in many areas, which create new
opportunities to understand phenomena in meteorology, health, finance, and many other …

Emerging scale-out workloads require extensive amounts of computational resources.
However, data centers using modern server hardware face physical constraints in space …

The traditional low-power embedded processors such as ARM and Atom are entering the
high-performance server market. At the same time, big data analytics are emerging and …

This paper explores the design and optimization implications for systems targeted at Big
Data workloads. We confirm that these workloads differ from workloads typically run on more …

Emerging scale-out workloads require extensive amounts of computational resources.
However, data centers using modern server hardware face physical constraints in space …