The increasing demands of big data applications have led researchers and practitioners to
turn to in-memory computing to speed processing. For instance, the Apache Spark …

In last decade, data analytics have rapidly progressed from traditional disk-based
processing to modern in-memory processing. However, little effort has been devoted at …

Hadoop provides a scalable solution on traditional cluster-based Big Data platforms but
imposes performance overheads due to only supporting on-disk data. Data Analytic …

While cluster computing frameworks are continuously evolving to provide real-time data
analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics …

Emerging big data frameworks requires computational resources and memory subsystems
that can naturally scale to manage massive amounts of diverse data. Given the large size …

The emergence of big data frameworks requires computational and memory resources that
can naturally scale to manage massive amounts of diverse data. It is currently unclear …

The performance of in-memory based data analytic frameworks such as Spark is
significantly affected by how data is partitioned. This is because the partitioning effectively …

Spark is a memory-based computing framework which has a better ability of computing and
fault tolerance, supports batch, interactive, iterative and flow calculations. In this paper, we …

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

For data-intensive computing, the low throughput of the existing disk-bound storage systems
is a major bottleneck. Recent emergence of the in-memory file systems with heterogeneous …