[hpc-announce] Auto-DaSP CFP: Data Stream Processing workshop

[Gabriele Mencagli]

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Auto-DaSP 2017: Euro-Par 2017 International Workshop
Autonomic Solutions for Parallel and Distributed Data Stream Processing
28-29, August 2017
Santiago de Compostela, Spain
Workshop web page: http://www.di.unipi.it/auto-dasp-17/
Euro-Par web page: http://europar2017.usc.es/
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* Call for Papers
We are living in an ever-more connected world where everyday life
environments are integrated with a proliferation of devices that
continuously produce unbounded data flows that have to be processed “on the
fly” in order to detect operational exceptions, deliver real-time alerts,
and trigger automated actions. This paradigm extends to a wide spectrum of
applications with high socio-economic impact, like systems for healthcare,
emergency management, surveillance, intelligent transportation and many
others.

The data streaming domain belongs to the Big Data ecosystem. High-frequency
data streams featuring time-varying characteristics represent one of the
most challenging aspects in the design of applications and frameworks. This
is especially critical in case of strict performance requirements (e.g.,
throughput and latency) that must be met despite an unexpected workload
variability or the dynamism of the execution environment.

High-performance solutions targeting today’s commodity parallel hardware
are “a must” to enable efficient data stream processsing. This comprises
run-time supports targeting multicores, GPU and FPGA co-processors, and
large-scale distributed-memory systems like clusters, Clouds and recently
Fog infrastructures. However, such solutions need autonomic logics in order
to adapt the framework/applications to changing execution conditions and
workloads. Examples are mechanisms and strategies to adapt the queries, the
operators placement policies, intra-operator parallelism degree, scheduling
strategies, load shedding rate and so forth.

* Topics of interest include, but are not limited to, the following:

  -  Parallel models for streaming applications
  -  Stream processing in Cloud and Fog computing environments
  -  Parallel continuous queries
  -  Sliding-window queries
  -  High-level parallel patterns
  -  Autonomic solutions based on Control Theory and Artificial
Intelligence methods
  -  Strategies for operator and query placement
  -  Stream processing on heterogeneous and reconfigurable hardware
  -  Out-of-order data streams
  -  Burstiness and workload variations
  -  Stream scheduling strategies and load balancing
  -  Adaptive load shedding
  -  Integration of elasticity supports in existing frameworks
  -  Use cases in various domains including Smart Cities, IoT, Finance,
Social Media, and Healthcare

* Submission Instructions
Submissions in PDF format should not exceed 10 pages in the Springer LNCS
style, which can be downloaded from the Springer Web site. The 10 pages
limit is a hard limit. It includes everything (text, figures, references)
and will be strictly enforced by the submission system. Complete LaTeX
sources must be provided for accepted papers. All submitted research papers
will be peer-reviewed. Only contributions that are not submitted elsewhere
or currently under review will be considered. Accepted papers will be
included in the workshop proceedings, published by Springer in the
ARCoSS/LNCS series. Authors of accepted papers will have to sign a Springer
copyright form.

* Special Issue
The best papers presented at the workshop will be invited to contribute to
a special issue on a high quality peer-reviewed indexed journal. The
special issue details will be published soon in the workshop web page.

* Important Dates
May 5, 2017        Paper submission deadline
June 16, 2017        Paper acceptance notifications
October 3, 2017        Camera-ready due
August 28-29, 2017    Workshop day

* 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
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