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
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
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
The Hadoop Distributed File System
MSST '10 Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST)
Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
CloudProphet: towards application performance prediction in cloud
Proceedings of the ACM SIGCOMM 2011 conference
Modeling and synthesizing task placement constraints in Google compute clusters
Proceedings of the 2nd ACM Symposium on Cloud Computing
Load Prediction and Hot Spot Detection Models for Autonomic Cloud Computing
UCC '11 Proceedings of the 2011 Fourth IEEE International Conference on Utility and 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
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
Characterization and Comparison of Cloud versus Grid Workloads
CLUSTER '12 Proceedings of the 2012 IEEE International Conference on Cluster Computing
Hi-index | 0.00 |
We design a novel prediction method with Bayes model to predict a load fluctuation pattern over a long-term interval, in the context of Google data centers. We exploit a set of features that capture the expectation, trend, stability and patterns of recent host loads. We also investigate the correlations among these features and explore the most effective combinations of features with various training periods. All of the prediction methods are evaluated using Google trace with 10,000+ heterogeneous hosts. Experiments show that our Bayes method improves the long-term load prediction accuracy by 5.6%-50%, compared to other state-of-the-art methods based on moving average, auto-regression, and/or noise filters. Mean squared error of pattern prediction with Bayes method can be approximately limited in [10^-^8,10^-^5]. Through a load balancing scenario, we confirm the precision of pattern prediction in finding a set of idlest/busiest hosts from among 10,000+ hosts can be improved by about 7% on average.