NDLI: Runtime prediction models for Web-based system resources

Content Provider	IEEE Xplore Digital Library
Author	Casolari, S. Andreolini, M. Colajanni, M.
Copyright Year	2008
Description	Author affiliation: Dept. of Inf. Eng., Univ. of Modena & Reggio Emilia, Modena (Casolari, S.; Andreolini, M.; Colajanni, M.)
Abstract	Several activities of Web-based architectures are managed by algorithms that take runtime decisions on the basis of continuous information about the state of the internal system resources. The problem is that in this extremely dynamic context the observed data points are characterized by high variability, dispersion and noise at different time scales to the extent that existing models cannot guarantee accurate predictions at runtime. In this paper, we evaluate the predictability of the internal resource state and point out the necessity to filter the noise of raw data measures. We then verify that more accurate prediction models are required which take into account the non stationary effects of the data sets, the time series trends and the runtime constraints. To these purposes, we propose a new prediction model, called trend-aware regression. It is specifically designed to deal with on the fly and short-term forecast of time series which originate from filtered data points belonging to internal resources of Web system. The experiment evaluation for different workload scenarios shows that the proposed trend-aware regression model improves the prediction accuracy with respect to popular algorithms based on auto-regressive and linear models, while satisfying the computational constraints of runtime prediction.
Starting Page	1
Ending Page	8
File Size	1609264
Page Count	8
File Format	PDF
ISBN	9781424428175
ISSN	15267539
DOI	10.1109/MASCOT.2008.4770556
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Runtime Predictive models Resource management Performance analysis Noise measurement Frequency Low-frequency noise Engineering management Context modeling Filters
Content Type	Text
Resource Type	Article

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