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A Threshold Autoregressive Model for Software Aging

Authors: Xiu-E Chen; Quan Quan; Yun-Fei Jia; Kai-Yuan Cai;

A Threshold Autoregressive Model for Software Aging

Abstract

Long running software systems such as client-server type systems are known to experience a kind of aging phenomenon called software aging, one in which the accumulation of errors during the execution of software leads to performance degradation and eventually results in failure. To study and counteract the phenomenon of software aging, we collect and log data on several system resource usage and activity parameters of a web server. Based on the experimental results, we argue that software aging process could be divided into four stages: robust stage, transition stage, failure-probable stage and failure stage. A non-linear threshold autoregressive (TAR) model is then proposed to model and forecast the resource usage in these stages. In comparison with AR model, TAR model is more accurate.

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Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Average
Top 10%
Top 10%
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