ADMiRe: an algebraic data mining approach to system performance analysis

Performance analysis of computing systems is an increasingly difficult task due to growing system complexity. Traditional tools rely on ad hoc procedures. With these, determining which of the manifold system and workload parameters to examine is often a lengthy and highly speculative process. The an...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 17; no. 7; pp. 888 - 901
Main Authors: Jiang, N., Villafane, R., Hua, K.A., Sawant, A., Prabhakara, A.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.07.2005
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algebra
algorithms for data and knowledge management
Applied sciences
Computer science; control theory; systems
Computer systems performance. Reliability
Councils
Data analysis
Data base management systems
Data mining
Data processing. List processing. Character string processing
Database systems
Exact sciences and technology
Hardware
Index Terms- Data mining
Internet
Memory organisation. Data processing
Performance analysis
performance of systems
Regression analysis
Software
Studies
System performance
Tuning
Web server
Workload
Performance evaluation
Knowledge engineering
Data analysis
Correlation
performance of systems
Information extraction
Systems theory
Regression analysis
Transaction processing
Data mining
Workload
algorithms for data and knowledge management
Storage management
Database
Algebraic method
Internet
Oracle
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:Performance analysis of computing systems is an increasingly difficult task due to growing system complexity. Traditional tools rely on ad hoc procedures. With these, determining which of the manifold system and workload parameters to examine is often a lengthy and highly speculative process. The analysis is often incomplete and, therefore, prone to revealing faulty conclusions and not uncovering useful tuning knowledge. We address this problem by introducing a data mining approach called ADMiRe (analyzer for data mining results). In this scheme, regression analysis is first applied to performance data to discover correlations between various system and workload parameters. The results of this analysis are summarized in sets of regression rules. The user can then formulate intuitive algebraic expressions to manipulate these sets of rules to capture critical information. To demonstrate this approach, we use ADMiRe to analyze an Oracle database system running the TPC-C (Transaction Processing Performance Council) benchmark. The results generated by ADMiRe were confirmed by Oracle experts. We also show that by applying ADMiRe to Microsoft Internet Information Server performance data, we can improve system performance by 20 percent.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2005.103