Performance modeling and analysis
Performance modeling and analysis techniques are used to express and analyze the performance of specific system configurations. Techniques are targeted to the type of systems and performance requirements at hand, ranging from analytical modeling and reasoning about performance bounds to discrete-event simulation and stochastic reasoning about expected performance.
Performance modeling is typically done using a combination of knowledge-driven modeling and data-driven modeling. Knowledge-driven modeling builds on the expert knowledge of domain specialists. Data-driven modeling creates models through regression or model learning from data collected from prototypes, tests or systems in operation. Whereas domain models are typically developed during system architecting, performance aspect models are mostly developed and used during later phases, primarily design, but also for verification and validation. Model-driven system performance engineering requires models with rigorous mathematical foundations and tool support.
A common technique to analyze performance of specific system behaviors is simulation, for example using POOSL. Analytical analysis is used to derive performance estimates or bounds, e.g., in LSAT. Model checking is used to exhaustively verify the performance properties on a system model. Property verification can also be applied to execution or model traces (Gantt charts) built from actions, events and signals, where timing properties are captured in formal logic as used, for example, in TRACE.
A blueprint for system-level performance modeling of software-intensive embedded systems; M. Hendriks, T. Basten, J. Verriet, M. Brassé, L. Somers: International Journal on Software Tools for Technology Transfer, pages 1–20, 2014.
Performance Prediction for Families of Data-Intensive Software Applications; J. Verriet, R. Dankers, L. Somers: Companion of the 2018 ACM/SPEC International Conference on Performance Engineering, pages 189-194, 2018.
Warehouse simulation through model configuration; J. Verriet, R. Hamberg, J. Caarls, B. van Wijngaarden: 27th European Conference on Modelling and Simulation (ECMS 2013), pages 629-635, 2013
Virtual Prototyping of Large-Scale IoT Control Systems Using Domain-Specific Languages; J. Verriet, L. Buit, R. Doornbos, B. Huijbrechts, K. Sevo, J. Sleuters, M. Verberkt: 7th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2019), pages 231-241, 2019
LSAT: Specification and Analysis of Product Logistics in Flexible Manufacturing Systems; B. van der Sanden, Y. Blankenstein et al.: in IEEE 17th International Conference on Automation Science and Engineering, 2021
Publications in collaboration with Eindhoven University of Technology
Scenarios in the Design of Flexible Manufacturing Systems; T. Basten, J. Bastos, R. Medina, B. van der Sanden, M.C.W. Geilen, D. Goswami, M.A. Reniers, S. Stuijk, J.P.M. Voeten: In F. Catthoor, T. Basten, N. Zompakis, M. Geilen, P.G. Kjeldsberg, editors. System-Scenario-based Design Principles and Applications, pages 181-224. Springer Nature, Switzerland, 2020.
Parametric Critical Path Analysis for Event Networks with Minimal and Maximal Time Lags; J. van Pinxten, M.C.W. Geilen, M. Hendriks, T. Basten: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 37(11):2697-2708, 2018. Special Issue ESWEEK 2018. Proc. CODES+ISSS 2018, Torino, Italy, September 30-October 5, 2018.
Partial-Order Reduction for Performance Analysis of Max-Plus Timed Systems; B. van der Sanden, M. Geilen, M. Reniers, T. Basten: In Application of Concurrency to System Design, 18th International Conference, ACSD 2018, Proceedings, pages 40-49. Bratislava, Slovakia, 24-29 June 2018. IEEE Computer Society Press, Los Alamitos, CA, USA, 2018, ACSD 2018 best paper award.