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SI: Model-Driven Performance Engg in CPS 2025 : IET CPS Theory & Applications, Special Issue: Model-Driven System-Performance Engineering for CPS

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Link: https://ietresearch.onlinelibrary.wiley.com/hub/journal/23983396/homepage/call-for-papers/si-2024-000768
 
When Aug 30, 2024 - Nov 1, 2024
Where N/A
Submission Deadline Nov 1, 2024
Categories    CPS   system engineering   model-driven performance
 

Call For Papers

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

Submission Site: https://ietresearch.onlinelibrary.wiley.com/hub/journal/23983396/homepage/call-for-papers/si-2024-000768

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