Big data processing systems (eg, Hadoop, Spark, Storm) contain a vast number of
configuration parameters controlling parallelism, I/O behavior, memory settings, and …

Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses
Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process …

In software-defined networks (SDNs), the abstracted control plane is its symbolic
characteristic, whose core component is the software-based controller. The control plane is …

Abstract Uploading all IoT Big Data to a centralized cloud for data analytics is infeasible
because of the excessive latency and bandwidth limitation of the Internet. A promising …

In many domains, the previous decade was characterized by increasing data volumes and
growing complexity of data analyses, creating new demands for batch processing on …

While edge computing is gaining traction, organizations operating in geographically
distributed locations are still using cloud computing to collect and post-process data. In this …

Most programs are repetitive, where similar behavior can be seen at different execution
times. Proposed on-line systems automatically group these similar intervals of execution into …

Abstract Context: Hadoop, Spark, Storm, and Mesos are very well known frameworks in both
research and industrial communities that allow expressing and processing distributed …

Hadoop has become a popular framework for processing data-intensive applications in
cloud environments. A core constituent of Hadoop is the scheduler, which is responsible for …

Right-sizing resource allocation for big-data queries, particularly in serverless environments,
is critical for improving infrastructure operational efficiency, capacity availability, query …