Authors: Ian Davis Hadi Hemmati Richard C. Holt Michael W. Godfrey Douglas Neuse Serge Mankovskii
Venue: 2013 5th International Workshop on Principles of Engineering Service-Oriented Systems (PESOS) , pp. 37-40, 2013
Year: 2013
Abstract: Predicting future behavior reliably and efficiently is key for systems that manage virtual services; such systems must be able to balance loads within a cloud environment to ensure that service level agreements are met at a reasonable expense. In principle accurate predictions can be achieved by mining a variety of data sources, which describe the historic behavior of the services, the requirements of the programs running on them, and the evolving demands placed on the cloud by end users. Of particular importance is accurate prediction of maximal loads likely to be observed in the short term. However, standard approaches to modeling system behavior, by analyzing the totality of the observed data, tend to predict average rather than exceptional system behavior and ignore important patterns of change over time. In this paper, we study the ability of a simple multivariate linear regression for forecasting of peak CPU utilization (storms) in an industrial cloud environment. We also propose several modifications to the standard linear regression to adjust it for storm prediction.
BibTeX:
@inproceedings{iandavis2013spiac,
author = "Ian Davis and Hadi Hemmati and Richard C. Holt and Michael W. Godfrey and Douglas Neuse and Serge Mankovskii",
title = "Storm prediction in a cloud",
year = "2013",
pages = "37-40",
booktitle = "Proceedings of 2013 5th International Workshop on Principles of Engineering Service-Oriented Systems
(PESOS)
"
}
Plain Text:
Ian Davis, Hadi Hemmati, Richard C. Holt, Michael W. Godfrey, Douglas Neuse, and Serge Mankovskii, "Storm prediction in a cloud," 2013 5th International Workshop on Principles of Engineering Service-Oriented Systems (PESOS)
, pp. 37-40