The increasing availability of smart devices and sensors has been producing large
data volumes in the form of streams, which need to be processed in real-time to
extract actionable intelligence. In this context, there is a continuous demand for
High-Performance Data Stream Processing Systems to cover a wide spectrum of applications
with high socio-economic impact, like systems for healthcare, emergency management,
surveillance, intelligent transportation, and many others.
High-volume data streams can be efficiently handled through the adoption of novel
high-performance solutions targeting today’s highly parallel hardware. This comprises
multicore platforms and heterogeneous systems equipped with GPU and FPGA co-processors.
The capacity of these heterogeneous computing platforms has grown remarkably over the years,
offering tens of thousands of heterogeneous cores and multiple terabytes of aggregated RAM
that is reaching computing, memory and storage capacity of a large warehouse-scale cluster
of just few years ago.
The Auto-DaSP 2021 workshop aims at collecting scientific contributions from the community
working in the Data Stream Processing (DSP) domain at different levels, both experts in
streaming algorithms and applications and researchers working on stream processing frameworks
and support tools. The focus is on parallel and autonomic models and practical implementations
of DSP applications on parallel hardware and distributed systems, performance management and
optimizations with runtime in parallel/distributed environments.
The expected outcome has a twofold nature: stimulating scientific questions on the
interrelations among the three core parts of the workshop, i.e. autonomic computing,
high-performance, and data stream processing; cross-fertilizing the parallel/distributed
computing and performance engineering domains with the DSP research area. A partial list
of topics of this workshop is as follows:
– Performance characterization and modeling of streaming applications and systems
– Highly parallel models for streaming applications
– Streaming parallel patterns
– Strategies for dynamic operator and query placement
– Autonomic solutions for load management, elasticity, and reconfiguration
– Integration of elasticity and fault tolerance in stream processing systems
– Stream processing on heterogeneous and reconfigurable hardware
– Streaming state management
– Techniques to deal with out-of-order data streams
– Benchmarking of parallel/distributed stream processing systems
– Energy- and power-aware management of stream processing systems
– Applications and use cases in various domains including Smart Cities, Internet of Things,
Finance, Social Media, and Healthcare
* COVID-related Issues
The workshop is co-located with the ICPE 2021 conference, to be held in Rennes, France
(19-23 April, 2021). However, due to the uncertainty around the COVID pandemic, we cannot
guarantee that the workshop will be a live event. Further instructions will be given as
soon as possible.
* Submission Instructions
From 6 to 10 pages double column in ACM format. Easychair link is available in the website of the
workshop.
* Special Issue
The workshop will be supported by a Special Issue in a top-ranked journal. The venue will be
announced soon.
* Important Dates
January 18th, 2021 Paper submission deadline
February 15th, 2021 Paper acceptance notifications
February 22nd, 2021 Camera-ready due
April 19-20, 2021 Workshop days (exact date not decided, yet)
* Workshop Co-Chairs
– Valeria Cardellini, University of Rome Tor Vergata, Italy
– Gabriele Mencagli, University of Pisa, Italy
– Massimo Torquati, University of Pisa, Italy
Looking forward to receiving your excellent submissions soon.
Best regards,
Valeria Cardellini, Gabriele Mencagli and Massimo Torquati