Like other emerging fields, Stream Processing Engines (SPEs) pose several challenges to
the researchers such as resource awareness, dynamic configurations, heterogeneous …

Like other emerging fields, Stream Processing Engines (SPEs) pose several challenges to
the researchers eg, resource awareness, dynamic configurations, heterogeneous clusters …

The continual increase in the amount of generated data by social media, IoT devices, and
monitoring systems have motivated the use of Distributed Data Stream Processing (DSP) …

With ever-accelerating data creation rates in Big Data applications, there is a need for
efficient stream processing engines. Apache Storm has been of interest in both academia …

To efficiently handle a large volume of data, scheduling algorithms in stream processing
systems need to minimise the data movement between communicating tasks to improve …

The essential component of the Big Data system is the processing frameworks and engines
responsible for crunching the data. To cope with the growing computing demands of real …

Storm has been a popular distributed real-time computation system for stream data
processing, which currently provides an even scheduler to distribute all executors and …

Stream processing is a new memory computing paradigm that deals with dynamic data
streams efficiently. Storm is one of the stream processing frameworks, but the default stream …

Abstract Existing Data Stream Processing (DSP) systems perform poorly while encountering
heavy workloads, particularly on clustered set of (heterogeneous) computers. Elasticity and …

The era of big data has led to the emergence of new systems for real-time distributed stream
processing, eg, Apache Storm is one of the most popular stream processing systems in …