Bandwidth variability prediction with rolling interval least squares (RILS)
Proceedings of the 50th Annual Southeast Regional Conference
Host load prediction in a Google compute cloud with a Bayesian model
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Provisioning Virtual Resources Adaptively in Elastic Compute Cloud Platforms
International Journal of Web Services Research
A robust optimization for proactive energy management in virtualized data centers
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Google hostload prediction based on Bayesian model with optimized feature combination
Journal of Parallel and Distributed Computing
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Predicting grid performance is a complex task because heterogeneous resource nodes are involved in a distributed environment. Long execution workload on a grid is even harder to predict due to heavy load fluctuations. In this paper, we use Kalman filter to minimize the prediction errors. We apply Savitzky-Golay filter to train a sequence of confidence windows. The purpose is to smooth the prediction process from being disturbed by load fluctuations. We present a new adaptive hybrid method (AHModel) for load prediction guided by trained confidence windows. We test the effectiveness of this new prediction scheme with real-life workload traces on the AuverGrid and Grid5000 in France. Both theoretical and experimental results are reported in this paper. As the lookahead span increases from 10 to 50 steps (5 minutes per step), the AHModel predicts the grid workload with a mean-square error (MSE) of 0.04-0.73 percent, compared with 2.54-30.2 percent in using the static point value autoregression (AR) prediction method. The significant gain in prediction accuracy makes the new model very attractive to predict Grid performance. The model was proved especially effective to predict large workload that demands very long execution time, such as exceeding 4 hours on the Grid5000 over 5,000 processors. With minor changes of some system parameters, the AHModel can apply to other computational grids as well. At the end, we discuss extended research issues and tool development for Grid performance prediction.