Proactive identification of performance problems
Publication
, Journal Article
Duan, S; Babu, S
Published in: Proceedings of the ACM SIGMOD International Conference on Management of Data
December 1, 2006
We propose to demonstrate Fa, an automated tool for timely and accurate prediction of Service-Level-Agreement (SLA) violations caused by performance problems in database systems. Fa periodically collects performance data at three levels: applications, database server, and operating system. This data is used to construct probabilistic models for predicting SLA violations. Fa currently uses graphical Bayesian network models because of their ability to support a wide range of inferences, including prediction and diagnosis, as well as their support for interactive visualization and presentation of complex system behavior in intuitive ways. Copyright 2006 ACM.
Duke Scholars
Published In
Proceedings of the ACM SIGMOD International Conference on Management of Data
DOI
ISSN
0730-8078
Publication Date
December 1, 2006
Start / End Page
766 / 768
Citation
APA
Chicago
ICMJE
MLA
NLM
Duan, S., & Babu, S. (2006). Proactive identification of performance problems. Proceedings of the ACM SIGMOD International Conference on Management of Data, 766–768. https://doi.org/10.1145/1142473.1142582
Duan, S., and S. Babu. “Proactive identification of performance problems.” Proceedings of the ACM SIGMOD International Conference on Management of Data, December 1, 2006, 766–68. https://doi.org/10.1145/1142473.1142582.
Duan S, Babu S. Proactive identification of performance problems. Proceedings of the ACM SIGMOD International Conference on Management of Data. 2006 Dec 1;766–8.
Duan, S., and S. Babu. “Proactive identification of performance problems.” Proceedings of the ACM SIGMOD International Conference on Management of Data, Dec. 2006, pp. 766–68. Scopus, doi:10.1145/1142473.1142582.
Duan S, Babu S. Proactive identification of performance problems. Proceedings of the ACM SIGMOD International Conference on Management of Data. 2006 Dec 1;766–768.
Published In
Proceedings of the ACM SIGMOD International Conference on Management of Data
DOI
ISSN
0730-8078
Publication Date
December 1, 2006
Start / End Page
766 / 768