Filter Based Resource Demand Estimation for On-demand Provision
Huang Xian · Acta Automatica Sinica · 2014
As the development of demand resource provision, resource demands of software is becoming one of the most important attributes of resource management. Measurement and estimation are widely used in fetching the demands.However, it is hard to measure the short job s resource demands by current measurement tools, and the regression methods suffer from the well-studied problem of multicollinearity. Therefore, the estimated results are not confident. In order to improve the estimation precision, we propose a Kalman filter based approach, which can predict the unobservable attribute by observable attributes, and filter the noise existing in the measurement. At last, we test our approach with a benchmark and compare the relative errors, which can demonstrate that with the reasonable parameters, our approach can get close to the real demands quickly, and get the estimated value with the mean error less than 8 %.