Generating representative Web workloads for network and server performance evaluation
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
A Prediction-Based Real-Time Scheduling Advisor
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Experiences with predicting resource performance on-line in computational grid settings
ACM SIGMETRICS Performance Evaluation Review
An engineering approach to dynamic prediction of network performance from application logs
International Journal of Network Management
Load prediction models in web-based systems
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
IEEE/ACM Transactions on Networking (TON)
Models and framework for supporting runtime decisions in Web-based systems
ACM Transactions on the Web (TWEB)
The Journal of Supercomputing
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Short-term prediction has been established in computing as a mechanism for improving services. Long-term prediction has not been pursued because attempts to use multiple steps to extend short-term predictions have been shown to become less accurate the further into the future the prediction is extended. In each case, the researchers used fine grained sampling for the analysis. This study used course sampling of ten-second intervals and then aggregated them into periods of minutes, fifteen-minutes, and hours. Each of the aggregates was used to calculate the predictions for Hourly, Daily, and Weekly cycles, determine the error rate of the prediction, and establish a confidence interval of 80%. The results then were evaluated to identify the effectiveness of long term prediction and the best cycle to predict the resource utilization most accurately.