[hpc-announce] Call for Papers: SPE SI on Benchmarking, Experimentation Tools, and Reproducible Practices for Data-Intensive Systems from Edge to Cloud

[Demetris Trihinas]

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Special Issue on *Benchmarking, Experimentation Tools, and Reproducible 
Practices for*
*Data-Intensive Systems from Edge to Cloud *in/Software: Practice and 
Experience/ (Wiley Press)

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As data analytics and machine learning pervade our cities, factories, 
and homes, the computing
infrastructures for data-intensive systems are becoming more 
challenging. That is, the vision of
widely deployed, intelligent, and cyber-physical IoT systems will not be 
implemented with
centralized cloud resources alone. Such resources are simply too far 
away from sensor-equipped
devices and users, leading to high latency, bandwidth bottlenecks, and 
unnecessary energy
consumption. Additionally, there are often privacy and safety requirements mandating
distributed architectures. Thus, new distributed computing paradigms are 
emerging, promising
to provide computing and storage in closer proximity to data sources and 
users.

The emerging distributed computing environments of edge and fog 
computing can provide
additional resources within mobile networks, ISP infrastructures, or 
even low Earth orbit (LEO)
satellites. The resulting diverse and dynamic environments pose 
significant challenges to the
performance, dependability, and efficiency of data-intensive systems 
deployed onto such
infrastructures. Meanwhile, it is far less clear how to best benchmark, 
evaluate, and test the
behavior of systems that span IoT devices, edge nodes, and cloud resources.

For instance, IoT-sensor stream processing systems might be deployed to 
continuously optimize
the operation of urban infrastructures (such as public transport 
systems, water networks, or
medical infrastructures). The behavior of such systems must be assessed 
thoroughly before they
can be deployed to edge and fog infrastructures. Furthermore, these 
systems must be evaluated
reproducibly under the expected computing environment conditions, 
including variations of such
conditions, given the inherently unsteady nature of IoT environments. In 
addition, there are
increasing concerns about the energy consumption and emissions of 
ICT (and particularly
distributed ML-based applications), further warranting close inspection 
of the behavior of new
data-intensive applications.

Despite significant research and development efforts towards better benchmarking,
experimentation tools, and reproducible practices for data-intensive 
systems that span from
edge to cloud, further research is urgently needed. We, thus, invite 
high-quality research papers
on this topic for a special issue of /Software: Practice and Experience/.

*Topics of interest* include but are not limited to:
▪ physical and hybrid IoT testbeds, (co-)simulation and emulation of IoT 
environments,
and testing frameworks for edge and fog computing
▪ programming systems, instrumentation libraries, and domain-specific 
languages for the
configuration of reproducible experiments with data-intensive edge-cloud 
applications
▪ distributed monitoring, tracing, and error detection methods for 
data-intensive systems
that span from devices to clouds
▪ evaluation tools to investigate edge/fog/cloud resource management 
(e.g., scheduling,
dynamic scaling, and live migration)
▪ testing and benchmarking of network technologies and protocols, 
heterogeneous IoT
devices, and varieties of edge/fog/cloud resources
▪ benchmarking and experimentation tools to assess resource usage, power 
consumption,
dependability and fault tolerance, real-time behavior, or security and 
privacy aspects of
data-intensive applications
▪ low-cost, adaptive, and configurable monitoring/tracing/logging 
systems for resource
management, network shaping and/or large-scale application deployment
▪ tools and practices for estimating energy consumption and carbon 
emissions of data-
intensive applications deployed across edge/fog/cloud computing environments
▪ usability of testbeds, testing frameworks, benchmarks, and 
experimentation tools
▪ interoperable formats and repositories for sharing device, network, 
systems and/or
application performance models
▪ representativeness, reproducibility, and repeatability of experiments, 
benchmarks, and
tests in edge/fog/cloud computing environments
▪ legal and privacy aspects of reproducibility and data sharing in 
context of geo-
distributed testing and experimentation
▪ all of the above topics in the context of LEO edge computing (as well 
as LEO edge
computing coupled with terrestrial edge computing)

*Important Dates*
*
*▪ Submission: February 28, 2023
▪ Notification: April 30, 2023
▪ Revision due: May 31, 2023
▪ Notification of final acceptance: June 30, 2023
▪ Submission of final revised manuscript: July 31, 2023
▪ Publication date (tentative): Fall 2023

*Guest Editors
*
Dr. Lauritz Thamsen (contact editor for submission queries)
School of Computing Science,
University of Glasgow, United Kingdom
Email: lauritz.thamsen at glasgow.ac.uk <mailto:lauritz.thamsen at glasgow.ac.uk>

Prof. Dr. David Bermbach
Department of Telecommunication Systems,
Technische Universität Berlin, Germany

Dr. Demetris Trihinas
Department of Computer Science,
University of Nicosia, Cyprus

-- 
Demetris Trihinas, PhD
Lecturer
Department of Computer Science
School of Sciences and Engineering
46 Makedonitissas Avenue, CY-2417
P.O.Box 24005, CY-1700, Nicosia, Cyprus
email: trihinas.d at unic.ac.cy
website: dtrihinas.info <http://dtrihinas.info>