TY - GEN
T1 - Control Channel Isolation in SDN Virtualization
T2 - 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2023
AU - Yoo, Yeonho
AU - Yang, Gyeongsik
AU - Shin, Changyong
AU - Lee, Jeunghwan
AU - Yoo, Chuck
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Performance isolation is an essential property that network virtualization must provide for clouds. This study addresses the performance isolation of the control plane in virtualized software-defined networking (SDN), which we call control channel isolation. First, we report that the control channel isolation is seriously broken in the existing network hypervisor in that the end-to-end control latency grows by up to 15 x as the number of virtual switches increases. This jeopardizes the key network operations, such as routing, in datacenters. To address this issue, we take a machine learning approach that learns from the past control traffic as time-series data. We propose a new network hypervisor, Meteor, that designs an LSTM autoencoder to predict the control traffic per virtual switch. Our evaluation results show that Meteor improves the processing latency per control message by up to 12.7x. Furthermore, Meteor reduces the end-to-end control latency by up to 73.7%, which makes it comparable to the non-virtualized SDN.
AB - Performance isolation is an essential property that network virtualization must provide for clouds. This study addresses the performance isolation of the control plane in virtualized software-defined networking (SDN), which we call control channel isolation. First, we report that the control channel isolation is seriously broken in the existing network hypervisor in that the end-to-end control latency grows by up to 15 x as the number of virtual switches increases. This jeopardizes the key network operations, such as routing, in datacenters. To address this issue, we take a machine learning approach that learns from the past control traffic as time-series data. We propose a new network hypervisor, Meteor, that designs an LSTM autoencoder to predict the control traffic per virtual switch. Our evaluation results show that Meteor improves the processing latency per control message by up to 12.7x. Furthermore, Meteor reduces the end-to-end control latency by up to 73.7%, which makes it comparable to the non-virtualized SDN.
KW - Control channel
KW - Isolation
KW - LSTM autoencoder
KW - Machine learning
KW - Network virtualization
KW - SDN
UR - https://www.scopus.com/pages/publications/85166323359
U2 - 10.1109/CCGrid57682.2023.00034
DO - 10.1109/CCGrid57682.2023.00034
M3 - Conference contribution
AN - SCOPUS:85166323359
T3 - Proceedings - 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2023
SP - 273
EP - 285
BT - Proceedings - 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2023
A2 - Simmhan, Yogesh
A2 - Altintas, Ilkay
A2 - Varbanescu, Ana-Lucia
A2 - Balaji, Pavan
A2 - Prasad, Abhinandan S.
A2 - Carnevale, Lorenzo
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 May 2023 through 4 May 2023
ER -