Data-driven analysis and design
Accurate models are essential in model-driven system performance engineering to ensure the accuracy of analysis results. Data-driven analysis and design techniques enable model learning, model validation and model calibration. System operational data can be used for monitoring performance targets during system operation, for diagnosing unexpected performance degradations, for system updates, and for system (re-)design when developing new generations or variants of the system at hand. Selecting the right data to be collected and lightweight, non-intrusive system instrumentation are essential.
Operational data may be used to improve system performance. It is essential to collect the right data, via lightweight, non-intrusive system instrumentation and timing measurements to minimize the impact on system performance. It is important to ensure timing accuracy (e.g., via clock synchronization) and to consider storage and bandwidth limitations that determine whether the required data can be collected real-time or not.
Checking metric temporal logic with TRACE; M. Hendriks, M. Geilen, A.R.B. Behrouzian, T. Basten, H. Alizadeh, D. Goswami: in 16th International Conference on Application of Concurrency to System Design (ACSD 2016), Torun, Poland, 2016.
Analyzing execution traces: critical-path analysis and distance analysis; M. Hendriks, J. Verriet, T. Basten, B. Theelen, M. Brassé, L. Somers: International Journal on Software Tools for Technology Transfer 19, pages 487–510, 2017
Performance engineering with TRACE; M. Hendriks, T. Basten: Bits & Chips 2018
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.
Clearing the critical software path; Bits & Chips 2021