Title :
Assessing Forecasting Model Performance for Distributed Stream Processing Systems
Author :
Deng Huafeng ; Zhong LinHui ; Ye Maosheng
Author_Institution :
Sch. of Comput. & Inf. Eng., Jiangxi Normal Univ., Nanchang, China
Abstract :
Load forecasting plays an important role in the load balancing of the distributed stream processing systems. So, the performance and cost of three models: weighted moving average (WMA), exponential smoothing (ES) and grey model (GM(1,1)) are empirically evaluated by running three typical test cases on the load traces of the distributed stream processing systems and their results are reviewed according to three metrics: mean absolute percentage errors (MAPE), root of mean square error (RMSE), processing cost. According to the metrics of MAPE and RMSE, GM(1,1) performs best while WMA and ES perform much better than GM(1,1) according to the processing cost. However, when the load fluctuates dramatically, the prediction precision of the above models is low.
Keywords :
distributed processing; grey systems; load forecasting; mean square error methods; power engineering computing; resource allocation; assessing forecasting model; distributed stream processing system; exponential smoothing model; grey model; load balancing; load forecasting; mean absolute percentage error method; processing cost; root mean square error method; weighted moving average model; Computational modeling; Data models; Forecasting; Load forecasting; Load modeling; Predictive models; Time series analysis;
Conference_Titel :
Internet Technology and Applications, 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5142-5
Electronic_ISBN :
978-1-4244-5143-2
DOI :
10.1109/ITAPP.2010.5566395
