Adaptive Monitoring for Continuous Performance Model Integration: Unterschied zwischen den Versionen
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|betreuer=Manar Mazkatli | |betreuer=Manar Mazkatli | ||
|termin=Institutsseminar/2019-07-12 | |termin=Institutsseminar/2019-07-12 | ||
|kurzfassung=Performance Models (PMs) can be used to predict software performance and evaluate the alternatives at the design stage. Building such models manually is a time consuming and not suitable for agile development process where quick releases have to be generated in short cycles. Existing approaches that extract PMs | |kurzfassung=Performance Models (PMs) can be used to predict software performance and evaluate the alternatives at the design stage. Building such models manually is a time consuming and not suitable for agile development process where quick releases have to be generated in short cycles. To benefit from model-based performance prediction during agile software development the developers tend to extract PMs automatically. Existing approaches that extract PMs based on reverse-engineering and/or measurement techniques require to monitor and analyze the whole system after each iteration, which will cause a high monitoring overhead. | ||
The Continuous Integration of Performance Models (CIPM) approach address this problem by updating the PMs and calibrate it incrementally based on the adaptive monitoring of the changed parts of the code. | |||
In this work, we introduced an adaptive monitoring approach for performance model integration, which instruments automatically only the changed parts of the source code using specific pre-defined probes types. Then it monitors the system adaptively. The resulting measurements are used by CIPM to estimate PM parameters incrementally. | |||
The evaluation confirmed that our approach can reduce the monitoring overhead to 50%. | The evaluation confirmed that our approach can reduce the monitoring overhead to 50%. | ||
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Aktuelle Version vom 9. Juli 2019, 11:50 Uhr
| Vortragende(r) | Noureddine Dahmane | |
|---|---|---|
| Vortragstyp | Masterarbeit | |
| Betreuer(in) | Manar Mazkatli | |
| Termin | Fr 12. Juli 2019 | |
| Vortragssprache | ||
| Vortragsmodus | ||
| Kurzfassung | Performance Models (PMs) can be used to predict software performance and evaluate the alternatives at the design stage. Building such models manually is a time consuming and not suitable for agile development process where quick releases have to be generated in short cycles. To benefit from model-based performance prediction during agile software development the developers tend to extract PMs automatically. Existing approaches that extract PMs based on reverse-engineering and/or measurement techniques require to monitor and analyze the whole system after each iteration, which will cause a high monitoring overhead.
The Continuous Integration of Performance Models (CIPM) approach address this problem by updating the PMs and calibrate it incrementally based on the adaptive monitoring of the changed parts of the code. In this work, we introduced an adaptive monitoring approach for performance model integration, which instruments automatically only the changed parts of the source code using specific pre-defined probes types. Then it monitors the system adaptively. The resulting measurements are used by CIPM to estimate PM parameters incrementally. The evaluation confirmed that our approach can reduce the monitoring overhead to 50%. | |