Predict task running time in grid environments based on CPU load predictions
Future Generation Computer Systems
Load prediction using hybrid model for computational grid
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
Predicting Running Time of Grid Tasks based on CPU Load Predictions
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
Optimization of procedures for discovery and information of idle resources in distributed systems
ACM SIGOPS Operating Systems Review
An enhanced load balancing mechanism based on deadline control on GridSim
Future Generation Computer Systems
Future Generation Computer Systems
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
Google hostload prediction based on Bayesian model with optimized feature combination
Journal of Parallel and Distributed Computing
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The ability to accurately predict future resource capabilities is of great importance for applications and scheduling algorithms which need to determine how to use time-shared resources in a dynamic grid environment. In this paper we present and evaluate a new and innovative method to predict the one-stepahead CPU load in a grid. Our prediction strategy forecasts the future CPU load based on the tendency in several past steps and in previous similar patterns, and uses a polynomial fitting method. Our experimental results demonstrate that this new prediction strategy achieves average prediction errors that are between 37% and 86% lower than those incurred by the previously best tendency-based method.