Spark is widely used due to its high performance caching mechanism and high scalability,
which still causes uneven workloads and produces useless intermediate caching results …

In the era of Big Data, processing large amounts of data through data-intensive applications,
is presenting a challenge. An in-memory distributed computing system; Apache Spark is …

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 …

Caching is one of the most important techniques for the popular distributed big data
processing framework Spark. For this big data parallel computing framework, which is …

To get good performance for data stored in Object storage services like S3, data analysis
clusters need to cache data locally. Recently these caches have started taking into account …

Modern data processing workloads, such as machine learning and graph processing,
involve iterative computations to converge generated models into higher accuracy. An …

Caching intermediate results in memory, instead of flushing them to disks, actually shortens
the completion of big data analytics, because there is no need to reload them for follow-up …

Memory caches are being used aggressively in today's data analytics systems such as
Spark, Tez, and Piccolo. The significant performance impact of caches and their limited sizes …

Apache Spark caches reusable data into memory/disk. From our preliminary evaluation, we
have found that a memory-and-disk caching is ineffective compared to disk-only caching …

In the context of the Industrial Internet of Things (IIoT), industrial software services have
significantly revolution-ized conventional industrial manufacturing processes by inte-grating …