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Title: Time-Series Forecast Modeling on High-Bandwidth Network Measurements

Abstract

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 the 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.

Authors:
 [1];  [1]
  1. 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:
1377437
Grant/Contract Number:
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Grid Computing
Additional Journal Information:
Journal Volume: 14; Journal Issue: 3; Journal ID: ISSN 1570-7873
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; data modeling; time series; prediction model; network measurements; network traffic

Citation Formats

Yoo, Wucherl, and Sim, Alex. Time-Series Forecast Modeling on High-Bandwidth Network Measurements. United States: N. p., 2016. Web. doi:10.1007/s10723-016-9368-9.
Yoo, Wucherl, & Sim, Alex. Time-Series Forecast Modeling on High-Bandwidth Network Measurements. United States. doi:10.1007/s10723-016-9368-9.
Yoo, Wucherl, and Sim, Alex. Fri . "Time-Series Forecast Modeling on High-Bandwidth Network Measurements". United States. doi:10.1007/s10723-016-9368-9. https://www.osti.gov/servlets/purl/1377437.
@article{osti_1377437,
title = {Time-Series Forecast Modeling on High-Bandwidth Network Measurements},
author = {Yoo, Wucherl and Sim, Alex},
abstractNote = {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 the 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.},
doi = {10.1007/s10723-016-9368-9},
journal = {Journal of Grid Computing},
number = 3,
volume = 14,
place = {United States},
year = {Fri Jun 24 00:00:00 EDT 2016},
month = {Fri Jun 24 00:00:00 EDT 2016}
}

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