Regression-based utilization prediction algorithms

Authors: I. J. Davis H. Hemmati R. C. Holt Michael W. Godfrey D. M. Neuse S. Mankovskii

Venue: 2013 Conference of the Center for Advanced Studies on Collaborative Research, pp. 106–120, 2014

Year: 2014

Abstract: Predicting future behavior reliably and efficiently is vital for systems that manage virtual services. Such systems must be able to balance loads within a cloud environment to ensure that service level agreements (SLAs) are met at a reasonable expense. These virtual services while often comparatively idle are occasionally heavily utilized. Standard approaches to modeling system behavior (by analyzing the totality of the observed data, such as regression based approaches) tend to predict average rather than exceptional system behavior and may ignore important patterns of change over time. Consequently, such approaches are of limited use in providing warnings of future peak utilization within a cloud environment. Skewing predictions to better fit peak utilizations, results in poor fitting to low utilizations, which compromises the ability to accurately predict peak utilizations, due to false positives.

BibTeX:

@inproceedings{i.j.davis2014rupa,
    author = "I. J. Davis and H. Hemmati and R. C. Holt and Michael W. Godfrey and D. M. Neuse and S. Mankovskii",
    title = "Regression-based utilization prediction algorithms",
    year = "2014",
    pages = "106–120",
    booktitle = "Proceedings of the 2013 Conference of the Center for Advanced Studies on Collaborative Research"
}

Plain Text:

I. J. Davis, H. Hemmati, R. C. Holt, Michael W. Godfrey, D. M. Neuse, and S. Mankovskii, "Regression-based utilization prediction algorithms," 2013 Conference of the Center for Advanced Studies on Collaborative Research, pp. 106–120