A Pattern Fusion Model for Multi-Step-Ahead CPU Load Prediction

TitleA Pattern Fusion Model for Multi-Step-Ahead CPU Load Prediction
Publication TypeJournal Article
Year of Publication2013
AuthorsYang, D., J. Cao, J. Fu, J. Wang, and J. Guo
JournalJournal of Systems and Software
Start Page1257

In distributed systems, resource prediction is an important but difficult topic. In many cases, multiple prediction is needed rather than only performing prediction at a single future point in time. However, traditional approaches are not sufficient for multi-step-ahead prediction. We introduce a pattern fusion model to predict multi-step-ahead CPU loads. In this model, similar patterns are first extracted from the historical data via calculating Euclidean distance and fluctuation pattern distance between historical patterns and current sequence. For a given pattern length, multiple similar patterns of this length can often be found and each of them can produce a prediction. We also propose a pattern weight strategy to merge these prediction. Finally, a machine learning algorithm is used to combine the prediction results obtained from different length pattern sets dynamically. Empirical results on four real-world production servers show that this approach achieves higher accuracy on average than existing approaches for multi-step-ahead prediction.

Refereed DesignationRefereed