[hpc-announce] IET CPS Special issue on Model-Driven System-Performance Engineering for CPS - Final CfP

[Basten, Twan]

***  Final Call for Papers ***

*** IET Cyber-physical Systems Special Issue: Model-Driven System-Performance Engineering for CPS ***
https://urldefense.us/v3/__https://ietresearch.onlinelibrary.wiley.com/hub/journal/23983396/homepage/call-for-papers/si-2024-000768__;!!G_uCfscf7eWS!emRKnU_eXcnsmP3y4W9eg2G0DmQSOdQ154GU8pGIOE1bgNBgrfsyVy_et3g0-NbVCgts0mdPNpKoXZuCA9N7ytBeHA$ 

Submission deadline: Friday, 1 November 2024 Expected Publication Month: June 2025

System performance refers to the amount of useful work done by a system within predefined quality constraints. System performance often brings the competitive advantage for cyber-physical systems in domains like autonomous driving, chip manufacturing and production systems in general, healthcare, the smart grid, precision agriculture, and so on. To meet market demands for product and system quality, system customization, and a low total cost of ownership, systems need to meet ever more ambitious targets relating to system performance. Performance is a cross-cutting system-level concern, with intricate relations to other system-level concerns like quality, cost, energy efficiency, security, reliability, and customizability. Model-driven system-performance engineering (MD-SysPE) for CPS is essential to improve time-to-quality and the cost-performance ratio of complex systems.

This special issue invites any contributions in model-driven system-performance engineering for CPS that are of interest to the academic and industrial CPS community at large. Original research papers, industrial applications and case studies, and surveys on relevant topics are welcome.

Topics for this call for papers include but are not restricted to:
* Multi-domain modelling, analysis, and optimization of performance aspects
* Performance views in system architecture
* Modelling and analysis of trade-offs with other system qualities
* Modelling and analysis across abstraction levels
* Design-space exploration methods
* Synthesis methods targeting performance
* Scheduling, control in relation to performance
* Time-predictable (software) execution
* Data-driven performance analysis and optimization
* AI methods for performance analysis, optimization, diagnostics
* Performance monitoring
* Run-time adaptation and optimization
* Performance debugging and diagnostics
* Model learning for performance
* Performance validation, verification, and testing

Guest Editors:

Twan Basten
Eindhoven University of Technology
the Netherlands

Benny Åkesson
TNO, ESI & University of Amsterdam
the Netherlands

Arvind Easwaran
Nanyang Technological University
Singapore

Marilyn Wolf
University of Nebraska-Lincoln
United States