By combining edge computing and parallel computing, distributed edge computing has
emerged as a new paradigm to exploit the booming IoT devices at the edge. To accelerate …

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) …

Current infrastructures for developing big-data applications are able to process-via big-data
analytics-huge amounts of data, using clusters of machines that collaborate to perform …

Data Streams occur naturally in several observational settings and often need to be
processed with a low latency. Streams pose unique challenges: they have no preset …

Driven by the rapidly increasing demand for handling real-time data streams, many data
stream processing (DSP) systems have been proposed. Regardless of the different …

By integrating edge computing with parallel computing, distributed edge computing (DEC)
makes use of distributed devices in edge networks to perform computing in parallel, which …

By combing edge computing and parallel computing, distributed edge computing has
emerged as a new paradigm to accelerate computation at the edge. Considering the …

Motivated by emerging big streaming data processing paradigms (eg, Twitter Storm,
Streaming MapReduce), we investigate the problem of scheduling graphs over a large …

We consider the problem of scheduling VMs (Virtual Machines) in a multi-server system
motivated by cloud computing applications. VMs arrive dynamically over time and require …

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