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A Pattern Fusion Model for Multi-Step-Ahead CPU Load Prediction
|Title||A Pattern Fusion Model for Multi-Step-Ahead CPU Load Prediction|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Yang, D., J. Cao, J. Fu, J. Wang, and J. Guo|
|Journal||Journal of Systems and Software|
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.