Hadoop has become the de-facto large-scale data processing framework for modern
analytics applications. A major obstacle for sustaining high performance and scalability in …

The ever-growing data storage and I/O demands of modern large-scale data analytics are
challenging the current distributed storage systems. A promising trend is to exploit the recent …

Data-intensive platforms such as Hadoop and Spark are routinely used to process massive
amounts of data residing on distributed file systems like HDFS. Increasing memory sizes and …

Enterprises are increasingly moving their big data analytics to the cloud with the goal of
reducing costs without sacrificing application performance. Cloud service providers offer …

The viability and benefits of running MapReduce over modern High Performance Computing
(HPC) clusters, with high performance interconnects and parallel file systems, have attracted …

A promising trend in storage management for big data frameworks, such as Hadoop and
Spark, is the emergence of heterogeneous and hybrid storage systems that employ different …

With high performance interconnects and parallel file systems, running MapReduce over
modern High Performance Computing (HPC) clusters has attracted much attention due to its …

Due to their scalability and low cost, object-based storage systems are an attractive storage
solution and widely deployed. To gain valuable insight from the data residing in object …

Extracting value from data stored in object stores, such as OpenStack Swift and Amazon S3,
can be problematicin common scenarios where analytics frameworks and objectstores run …

Enterprise Hadoop applications now routinely comprise complex workflows that are
managed by specialized workflow schedulers such as Oozie. The resources are assumed to …