TY - GEN
T1 - Large Data Transfer Predictability and Forecasting using Application-Aware SDN
AU - Nadig, Deepak
AU - Ramamurthy, Byrav
AU - Bockelman, Brian
AU - Swanson, David
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85066024106&partnerID=8YFLogxK
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U2 - 10.1109/ANTS.2018.8710165
DO - 10.1109/ANTS.2018.8710165
M3 - Conference contribution
AN - SCOPUS:85066024106
T3 - International Symposium on Advanced Networks and Telecommunication Systems, ANTS
BT - 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2018
PB - IEEE Computer Society
T2 - 12th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2018
Y2 - 16 December 2018 through 19 December 2018
ER -