Introduction to signal processing
Introduction to signal processing
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Host load prediction using linear models
Cluster Computing
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Extended forecast of CPU and network load on computational Grid
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
CPU Load Predictions on the Computational Grid *
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Adaptive Filtering: Algorithms and Practical Implementation
Adaptive Filtering: Algorithms and Practical Implementation
A regression-based approach to scalability prediction
Proceedings of the 22nd annual international conference on Supercomputing
Load prediction using hybrid model for computational grid
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
A performance prediction framework for scientific applications
Future Generation Computer Systems
Towards characterizing cloud backend workloads: insights from Google compute clusters
ACM SIGMETRICS Performance Evaluation Review
Adaptive Workload Prediction of Grid Performance in Confidence Windows
IEEE Transactions on Parallel and Distributed Systems
Using Markov chain analysis to study dynamic behaviour in large-scale grid systems
AusGrid '09 Proceedings of the Seventh Australasian Symposium on Grid Computing and e-Research - Volume 99
Modeling and synthesizing task placement constraints in Google compute clusters
Proceedings of the 2nd ACM Symposium on Cloud Computing
Multi-model prediction for enhancing content locality in elastic server infrastructures
HIPC '11 Proceedings of the 2011 18th International Conference on High Performance Computing
Google hostload prediction based on Bayesian model with optimized feature combination
Journal of Parallel and Distributed Computing
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Prediction of host load in Cloud systems is critical for achieving service-level agreements. However, accurate prediction of host load in Clouds is extremely challenging because it fluctuates drastically at small timescales. We design a prediction method based on Bayes model to predict the mean load over a long-term time interval, as well as the mean load in consecutive future time intervals. We identify novel predictive features of host load that capture the expectation, predictability, trends and patterns of host load. We also determine the most effective combinations of these features for prediction. We evaluate our method using a detailed one-month trace of a Google data center with thousands of machines. Experiments show that the Bayes method achieves high accuracy with a mean squared error of 0.0014. Moreover, the Bayes method improves the load prediction accuracy by 5.6-50% compared to other state-of-the-art methods based on moving averages, auto-regression, and/or noise filters.