Network bandwidth utilization forecast model on high bandwidth networks
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
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.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- DOE Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1407267
- 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
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