The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Future Generation Computer Systems - Special issue on metacomputing
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Host load prediction using linear models
Cluster Computing
A Prediction-Based Real-Time Scheduling Advisor
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
The Statistical Properties of Hoast Load
LCR '98 Selected Papers from the 4th International Workshop on Languages, Compilers, and Run-Time Systems for Scalable Computers
Multivariate resource performance forecasting in the network weather service
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Experiences with predicting resource performance on-line in computational grid settings
ACM SIGMETRICS Performance Evaluation Review
Homeostatic and Tendency-Based CPU Load Predictions
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
CPU Load Predictions on the Computational Grid *
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Methodology for long-term prediction of time series
Neurocomputing
Adaptive Hybrid Model for Long Term Load Prediction in Computational Grid
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
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
Aligning ontology-based development with service oriented systems
Future Generation Computer Systems
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With the development of Internet-based technologies and the rapid growth of scientific computing applications, Grid computing becomes more and more attractive. Generally, the execution time of a CPU-intensive task on a certain resource is tightly related to the CPU load on this resource. In order to estimate the task execution time more accurately to achieve an effective task scheduling, it is significant to make an effective long-term load prediction in dynamic Grid environments. Nevertheless, as the prediction errors will be gradually accumulated while the best values of prediction parameters may vary vigorously, the existing prediction algorithms usually fail to achieve good prediction accuracy in the long-term prediction. To address these problems, an effective Data Aggregation based Adaptive Long term resource load Point-Prediction mechanism (DA^2LP"P"o"i"n"t) is proposed in this paper, where a data aggregation concept is introduced herein to reduce the number of prediction step. Furthermore, an interval based prediction mechanism with probability distribution representation called DA^2LP"I"n"t"e"r"v"a"l is lately proposed to improve the adaptation of prediction results. The experimental results show that the DA^2LP"P"o"i"n"t algorithm can outperform previous prediction methods in regard to mean square error (MSE). In addition, the DA^2LP"I"n"t"e"r"v"a"l algorithm can attain lesser prediction error with stronger representation capability; therefore, it is able to provide much more useful information for task scheduling in Grid environments.