Large Data Transfer Predictability and Forecasting using Application-Aware SDN

Deepak Nadig, Byrav Ramamurthy, Brian Bockelman, David Swanson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Network management for applications that rely on large-scale data transfers is challenging due to the volatility and the dynamic nature of the access traffic patterns. Predictive analytics and forecasting play an important role in providing effective resource allocation strategies for large data transfers. We propose a predictive analytics solution for large data transfers using an application-aware software defined networking (SDN) approach. We perform extensive exploratory data analysis to characterize the GridFTP connection transfers dataset and present various strategies for its use with statistical forecasting models. We develop a univariate autoregressive integrated moving average (ARIMA) based prediction framework for forecasting GridFTP connection transfers. Our prediction model tightly integrates with an application-aware SDN solution to preemptively drive network management decisions for GridFTP resource allocation at a U.S. CMS Tier-2 site. Further, our framework has a mean absolute percentage error (MAPE) ranging from 6% to 10% when applied to make rolling forecasts.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781538681343
DOIs
StatePublished - Jul 2 2018
Event12th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2018 - Indore, India
Duration: Dec 16 2018Dec 19 2018

Publication series

NameInternational Symposium on Advanced Networks and Telecommunication Systems, ANTS
Volume2018-December
ISSN (Print)2153-1684

Conference

Conference12th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2018
CountryIndia
CityIndore
Period12/16/1812/19/18

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Communication

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  • Cite this

    Nadig, D., Ramamurthy, B., Bockelman, B., & Swanson, D. (2018). Large Data Transfer Predictability and Forecasting using Application-Aware SDN. In 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2018 [8710165] (International Symposium on Advanced Networks and Telecommunication Systems, ANTS; Vol. 2018-December). IEEE Computer Society. https://doi.org/10.1109/ANTS.2018.8710165