Trace-context sensitive performance profiling for enterprise software applications

6Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Software response time distributions can be of high variance and multi-modal. Such characteristics reduce confidence or applicability in various statistical evaluations. We contribute an approach to correlating response times to their corresponding operation execution sequence. This provides calling-context sensitive timing behavior models. The approach is based on three equivalence relations: caller-context, stack-context, and trace-context equivalence. To prevent model size explosion, a tree-based hierarchy provides timing behavior models that provide a trade-off between timing behavior model size and the amount of calling-context information considered. In the case study, our approach provides response time distributions with significantly lower standard deviation, compared to using less or no calling-context information. An example from a performance analysis of an industry system demonstrates that multi-modal distributions can be replaced by multiple unimodal distributions using trace-context analysis. © 2008 Springer-Verlag Berlin Heidelberg.

Cite

CITATION STYLE

APA

Rohr, M., Van Hoorn, A., Giesecke, S., Matevska, J., Hasselbring, W., & Alekseev, S. (2008). Trace-context sensitive performance profiling for enterprise software applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5119 LNCS, pp. 283–302). Springer Verlag. https://doi.org/10.1007/978-3-540-69814-2_18

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free