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Title: Network bandwidth utilization forecast model on high bandwidth networks

Abstract

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

Authors:
 [1];  [2]
  1. (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1407267
DOE Contract Number:
AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: 2015 International Conference on Computing, Networking and Communications (ICNC 2015), Garden Grove, CA (United States), 16-19 Feb 2015
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Forecasting; Network; Time series analysis

Citation Formats

Yoo, Wuchert, and Sim, Alex. Network bandwidth utilization forecast model on high bandwidth networks. United States: N. p., 2015. Web. doi:10.1109/ICCNC.2015.7069393.
Yoo, Wuchert, & Sim, Alex. Network bandwidth utilization forecast model on high bandwidth networks. United States. doi:10.1109/ICCNC.2015.7069393.
Yoo, Wuchert, and Sim, Alex. Mon . "Network bandwidth utilization forecast model on high bandwidth networks". United States. doi:10.1109/ICCNC.2015.7069393. https://www.osti.gov/servlets/purl/1407267.
@article{osti_1407267,
title = {Network bandwidth utilization forecast model on high bandwidth networks},
author = {Yoo, Wuchert and Sim, Alex},
abstractNote = {With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.},
doi = {10.1109/ICCNC.2015.7069393},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Mar 30 00:00:00 EDT 2015},
month = {Mon Mar 30 00:00:00 EDT 2015}
}

Conference:
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  • With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology,more » our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.« less
  • With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less
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