Abstract: In resource constraints condition, it is very important to make accurate predictions of the task execution time based on time-series resource and task status generated in real-time during task execution. In order to use time-series data effectively to realize accurate prediction, a load shedding strategy is proposed to determine the time points of prediction and data processing scheme. This strategy uses dynamic time warping (DTW) distance to measure the variation of similarity between subsequences and entire sequences and determine the data used for prediction. Then we use wavelet transform to calculate the wavelet coefficients of the time-series and extract the energy value of wavelet coefficients as the features of prediction. After that, we conduct the prediction for task execution time. Experiments show that the features extracted by this method contain most information than the entire sequence and result in high accuracy in predicting the task execution time.

Key words: load shedding, wavelet transform, feature extraction, task execution time prediction