In-memory cluster computing platforms have gained momentum in the last years, due to their
ability to analyse big amounts of data in parallel. These platforms are complex and difficult-to …

The in-memory data processing framework, Apache Spark, has been stealing the limelight
for low-latency interactive applications, iterative and batch computations. Our early …

In-memory caches play an important role in reducing the load on backend storage servers
for many workloads. Miss ratio curves (MRCs) are an important tool for configuring these …

For over forty years, researchers have demonstrated that operating system memory
managers often fall short in supporting memory-hungry applications. The problem is even …

Memory caches are being aggressively used in today's data-parallel systems such as Spark,
Tez, and Piccolo. However, prevalent systems employ rather simple cache management …

Distributed in-memory processing frameworks accelerate application runs by caching
important datasets in memory. Allocating a suitable cluster configuration for caching these …

Data analytic applications built upon big data processing frameworks such as Apache Spark
are an important class of applications. Many of these applications are not latency-sensitive …

Scheduling plays an important role in improving the performance of big data-parallel
processing. Spark is an in-memory parallel computing framework that uses a multi-threaded …

Optimizing memory cache usage is vital for performance of in-memory data-parallel
frameworks such as Spark. Current data-analytic frameworks utilize the popular Least …

In the era of big data and cloud computing, large amounts of data are generated from user
applications and need to be processed in the datacenter. Data-parallel computing …