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Cloud datacenter workload estimation using error preventive time series forecasting models

  • Jitendra KumarEmail author
  • Ashutosh Kumar Singh
Article
  • 45 Downloads

Abstract

The workload estimation plays a vital role in efficient management of cloud resources. This paper introduces the error preventive score (EPS) in time series forecasting models to improve the prediction accuracy. The EPS analyzes the most recent estimations to capture the forecast error trend and uses it to achieve better forecasts. In addition, we have also proposed two metrics for accuracy evaluation namely predictions in error range (PER) and magnitude of predictions (MoP). These matrices favor a model that has maximum predictions close to actual values by evaluating the error and magnitude of individual forecast. The impact of EPS on the accuracy is evaluated using three workload estimation models. The experimental analysis is carried out over five data traces and performance is measured using correlation coefficient (CoC), sum of elasticity index (SEI), mean squared prediction error (MPE), PER and MoP metrics. The error preventive models achieved maximum improvement upto 183.9%, 95.4% and 100.0% over non error preventive models in CoC, SEI, and MPE respectively. The error preventive models significantly brought down the individual forecast error below 25% and under estimations are reduced by a maximum factor of 55.2%. The superiority of the proposed scheme is validated using a comprehensive statistical evaluation based on Wilcoxon signed rank test and Friedman test with Finner post-hoc analysis. We observed that error preventive weighted exponential smoothing model produced best forecasts.

Keywords

Cloud computing Workload estimation Time series Auto regression Exponential smoothing 

Notes

Acknowledgements

The authors would like to thank the Ministry of Electronics & Information Technology (MeitY), Government of India for financial support to carry out this work.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ApplicationsNational Institute of Technology, KurukshetraKurukshetraIndia

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