TY - GEN
T1 - Comprehensive Prediction Models of Control Traffic for SDN Controllers
AU - Yu, Bong Yeol
AU - Yang, Gyeongsik
AU - Yoo, Chuck
N1 - Funding Information:
∗This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (2015-0-00288, Research of Network Virtualization Platform and Service for SDN 2.0 Realization). This research was also supported by the MSIT(Ministry of Science and ICT), Korea, under the SW Starlab support program(2015-0-00280) supervised by the IITP.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - In SDN, as the control channel becomes a performance bottleneck, modeling the control channel traffic is important. Such a model is useful in predicting the control channel traffic for network provisioning. However, previously proposed models are quite limited in that they assume only the forwarding function of a specific controller for their models. To overcome the limitations, first, this paper analyzes the control traffic by seven functions (including forwarding function) of a controller. Then, we build a seven-function model to predict control channel usage and evaluate the prediction accuracy that achieves as high as 94%. Note that previous models did not have any quantitative evaluation. Our model is built with the Open Network Operating System (ONOS) controller and extended to Floodlight and POX controllers. We show that the extended model also achieves similar prediction accuracy (95%). Furthermore, we compare three controllers in terms of control channel usage through our model.
AB - In SDN, as the control channel becomes a performance bottleneck, modeling the control channel traffic is important. Such a model is useful in predicting the control channel traffic for network provisioning. However, previously proposed models are quite limited in that they assume only the forwarding function of a specific controller for their models. To overcome the limitations, first, this paper analyzes the control traffic by seven functions (including forwarding function) of a controller. Then, we build a seven-function model to predict control channel usage and evaluate the prediction accuracy that achieves as high as 94%. Note that previous models did not have any quantitative evaluation. Our model is built with the Open Network Operating System (ONOS) controller and extended to Floodlight and POX controllers. We show that the extended model also achieves similar prediction accuracy (95%). Furthermore, we compare three controllers in terms of control channel usage through our model.
KW - Control channel
KW - Control traffic
KW - OpenFlow
KW - SDN controller
KW - Scalability
KW - Software-defined networking
UR - http://www.scopus.com/inward/record.url?scp=85054386182&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054386182&partnerID=8YFLogxK
U2 - 10.1109/NETSOFT.2018.8460111
DO - 10.1109/NETSOFT.2018.8460111
M3 - Conference contribution
AN - SCOPUS:85054386182
SN - 9781538646335
T3 - 2018 4th IEEE Conference on Network Softwarization and Workshops, NetSoft 2018
SP - 415
EP - 423
BT - 2018 4th IEEE Conference on Network Softwarization and Workshops, NetSoft 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE Conference on Network Softwarization and Workshops, NetSoft 2018
Y2 - 25 June 2018 through 29 June 2018
ER -