ASTROLABE: A Rigorous Approach for System-Level Performance Modeling and Analysis
(2016) ACM Transactions on Embedded Computing Systems — p. 1-26 (2016)
Files
No attached file found for this publication.
Details
- Authors
- Abstract
- Building abstract system-level models that faithfully capture performance and functional behavior for embedded systems design is challenging. Unlike functional aspects, performance details are rarely available during the early design phases, and no clear method is known to characterize them. Moreover, once such models are built, they are inherently complex as they mix software models, hardware constraints, and environment abstractions. Their analysis by using traditional performance evaluation methods is reaching the limit. In this article, we present a systematic approach for building stochastic abstract performance models using statistical inference and model calibration, and we propose statistical model checking as a scalable performance evaluation technique for them.
- Affiliations
Citations
Nouri, A., Bozga, M., Molnos, A., Legay, A., & Bensalem, S. (2016). ASTROLABE: A Rigorous Approach for System-Level Performance Modeling and Analysis. ACM Transactions on Embedded Computing Systems, 1-26. https://doi.org/10.1145/2885498 (Original work published 2016)