Latest revision as of 00:56, 29 January 2021

Abstract

Previous research on SDN traffic engineering mostly focuses on static traffic, whereas dynamic traffic, though more practical, has drawn much less attention. Especially, online SDN multicast that supports IETF dynamic group membership (i.e., any user can join or leave at any time) has not been explored. Different from traditional shortest-path trees (SPT) and graph theoretical Steiner trees (ST), which concentrate on routing one tree at any instant, online SDN multicast traffic engineering is more challenging because it needs to support dynamic group membership and optimize a sequence of correlated trees without the knowledge of future join and leave, whereas the scalability of SDN due to limited TCAM is also crucial. In this paper, therefore, we formulate a new optimization problem, named Online Branch-aware Steiner Tree (OBST), to jointly consider the bandwidth consumption, SDN multicast scalability, and rerouting overhead. We prove that OBST is NP-hard and does not have a $|D_{max}|^{1-\epsilon}$-competitive algorithm for any $\epsilon >0$, where $|D_{max}|$ is the largest group size at any time. We design a $|D_{max}|$-competitive algorithm equipped with the notion of the budget, the deposit, and Reference Tree to achieve the tightest bound. The simulations and implementation on real SDNs with YouTube traffic manifest that the total cost can be reduced by at least 25% compared with SPT and ST, and the computation time is small for massive SDN.

Comment: Full version (accepted by INFOCOM 2018)

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1801.00110

• http://arxiv.org/pdf/1801.00110

• http://dx.doi.org/10.1109/infocom.2018.8486290

• http://xplorestaging.ieee.org/ielx7/8464035/8485803/08486290.pdf?arnumber=8486290,

http://dx.doi.org/10.1109/infocom.2018.8486290

• https://dblp.uni-trier.de/db/journals/corr/corr1801.html#abs-1801-00110,

https://arxiv.org/abs/1507.08728,

https://ieeexplore.ieee.org/document/8486290,

https://ui.adsabs.harvard.edu/abs/2018arXiv180100110C/abstract,

https://arxiv.org/pdf/1507.08728v3,

https://academic.microsoft.com/#/detail/2964215898