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BIFOLD Colloquium 2022/06/13

Perspectives on Data Stream Processing for Resource-constrained Devices


Speaker: Gabriele Mencagli, University of Pisa
Venue: Virtual
Date & Time: June, 13, 2022; 4:00pm


Registration: If you are interested in participating, please contact: coordination@bifold.berlin

Abstract:
Resource-constrained devices are widely diffused in highly-distributed computing environments like IoT platforms, edge and far-edge computing infrastructures, and fog scenarios. Such devices are usually equipped with low-power CPUs, and they often include integrated co-processors (e.g., GPUs and FPGAs) available as System-on-Chip architectures. Data Stream Processing is a hot research topic focusing on efficient data analysis techniques and processing systems to extract analytics, knowledge and, in general, perform generic computations on unbounded sequences of data flows arriving at high speed. Tradition streaming systems (e.g., Apache Storm, Flink, Spark Streaming) target scale-out scenarios (i.e., clusters of homogeneous high-end servers and Clouds).

The transition to efficiently support resource-constrained devices advocates to profitably configure the processing model of the existing streaming systems to fit at best the new hardware or, alternatively, to design new frameworks from scratch. This talk will show the research directions currently followed by the Parallel Programming Models (PPMs) group at the University of Pisa, Department of Computer Science.

Speaker:
Gabriele Mencagli is Associate Professor at the Department of Computer Science, University of Pisa, Italy. He is member of the Parallel Programming Models group, doing research in High Performance Computing, Parallel Programming and Data Stream Processing.
The main contribution of the group is the development of novel parallel programming frameworks for multicores and heterogeneous systems, with special focus on high-level abstractions (parallel patterns) for easing the effort to develop efficient parallel software on different kinds of hardware resources.