Using Search Methods for Selecting and Combining Software Sensors to Improve Fault Detection in Autonomic Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{Shevertalov:2010:SSBSE,
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author = "Maxim Shevertalov and Kevin Lynch and
Edward Stehle and Chris Rorres and Spiros Mancoridis",
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title = "Using Search Methods for Selecting and Combining
Software Sensors to Improve Fault Detection in
Autonomic Systems",
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booktitle = "Second International Symposium on Search Based
Software Engineering (SSBSE 2010)",
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year = "2010",
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month = "7-9 " # sep,
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pages = "120--129",
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isbn13 = "978-1-4244-8341-9",
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keywords = "genetic algorithms, genetic programming, autonomic
system, convex-hull geometric object, fault detection,
genetic-algorithm, genetic-programming,
multidimensional space, random-search approach, search
method, software sensor, fault tolerant computing,
search problems",
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DOI = "doi:10.1109/SSBSE.2010.23",
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abstract = "Fault-detection approaches in autonomic systems
typically rely on runtime software sensors to compute
metrics for CPU load, memory usage, network throughput,
and so on. One detection approach uses data collected
by the run time sensors to construct a convex-hull
geometric object whose interior represents the normal
execution of the monitored application. The approach
detects faults by classifying the current application
state as being either inside or outside of the convex
hull. However, due to the computational complexity of
creating a convex hull in multi-dimensional space, the
convex-hull approach is limited to a few metrics.
Therefore, not all sensors can be used to detect faults
and so some must be dropped or combined with
others.
This paper compares the effectiveness of
genetic-programming, genetic-algorithm, and
random-search approaches in solving the problem of
selecting sensors and combining them into metrics.
These techniques are used to find 8 metrics that are
derived from a set of 21 available sensors. The metrics
are used to detect faults during the execution of a
Java-based HTTP web server. The results of the search
techniques are compared to two hand-crafted solutions
specified by experts.",
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notes = "grammar with five(three) rules. NanoHTTPD Java Ruby
SSH QHull. 'many good solutions exist in the space
being searched'. 'We expect the difference between the
quality of human and computer-generated solutions to
increase as more sophisticated applications begin using
this technique.'
IEEE Computer Society Order Number P4195 BMS Part
Number: CFP1099G-PRT Library of Congress Number
2010933544 http://ssbse.info/2010/program.php Also
known as \cite{5635154}",
- }
Genetic Programming entries for
Maxim Shevertalov
Kevin Lynch
Edward Stehle
Chris Rorres
Spiros Mancoridis
Citations