Resource Utilization Prediction: Long Term Network Web Service Traffic
Conference Name / Publication Title
Proceedings of ACM SIGITE/RIIT 2013
ISSN or ISBN
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.
Yoas, Daniel W. and Simco, Gregory, "Resource Utilization Prediction: Long Term Network Web Service
Traffic" (2013). CEC Faculty Proceedings, Presentations, Speeches and Lectures. 41.