Paper Info
Title
Using computational intelligence to identify performance bottlenecks in a computer system
Abstract
System administrators have to analyze a number of system parameters to identify performance bottlenecks in a system. The major contribution of this paper is a utility - EvoPerf - which has the ability to autonomously monitor different system-wide parameters, requiring no user intervention, to accurately identify performance based anomalies (or bottlenecks). EvoPerf uses Windows Perfmon utility to collect a number of performance counters from the kernel of Windows OS. Subsequently, we show that artificial intelligence based techniques - using performance counters - can be used successfully to design an accurate and efficient performance monitoring utility. We evaluate feasibility of six classifiers - UCS, GAssist-ADI, GAssist-Int, NN-MLP, NN-RBF and J48 - and conclude that all classifiers provide more than 99% classification accuracy with less than 1% false positives. However, the processing overhead of J48 and neural networks based classifiers is significantly smaller compared with evolutionary classifiers.
Year
DOI
Venue
2010
10.1007/978-3-642-15844-5_31
PPSN (1)
Keywords
Field
DocType
computational intelligence,artificial intelligence,system parameter,performance bottleneck,computer system,windows os,classification accuracy,windows perfmon utility,performance counter,system administrator,efficient performance monitoring utility,different system-wide parameter,false positive,neural network,artificial intelligent
Kernel (linear algebra),Data mining,Microsoft Windows,Computational intelligence,Computer science,Virtual memory,C4.5 algorithm,Artificial intelligence,Artificial neural network,Machine learning,False positive paradox,Network interface
Conference
Volume
ISSN
ISBN
6238
0302-9743
3-642-15843-9
Citations 
PageRank 
References 
0
0.34
16
Authors
3
Name
Order
Citations
PageRank
Faraz Ahmed11248.63
Farrukh Shahzad2554.00
Muddassar Farooq3122183.47