S-fabric: towards scalable and incremental SDN deployment in data centers
2017 | conference paper. A publication of Göttingen
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S-fabric: towards scalable and incremental SDN deployment in data centers
Shen, H. ; Wang, H. ; Wieder, P. & Yahyapour, R. (2017)
Proceedings of the 2017 International Conference on Telecommunications and Communication Engineering pp. 25-29. ICTCE '17, Osaka, Japan.
New York, USA: Association for Computing Machinery. DOI: https://doi.org/10.1145/3145777.3145790
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Details
- Authors
- Shen, Haoyun ; Wang, Hao ; Wieder, Philipp ; Yahyapour, Ramin
- Abstract
- Scalable and robust SDN requires the controller to be distributed. In many SDN designs, the distributed controllers are acting as replicas by forming clusters. For large-scale data centers across multiple geographically distributed locations, the controllers have to maintain a synchronized global view. These restrict themselves on single point of failure, low scalability, more communication effort, bad isolation, and rigid deployment. In this paper, we propose S-Fabric, a novel data center network design, which provides a sliced control plane and a policy-based user-defined data plane. By slicing the network through flows, and assigning a non-replica controller to each slice, S-Fabric achieves flexibility and elasticity, while ensuring isolation and separation. We leverage (but not limited to) a two-tiered spine and leaf architecture, and define forwarding rules for spine, leaf and edge switch respectively. By simplifying the flow table, S-Fabric keeps the number of forwarding rules on spine switches equal to the number of used leaf/edge ports inside a data center. By matching subnets in slices to VLANs on the edge switches, S-Fabric brings backwards compatibility to traditional data centers. S-Fabric enables an incremental deployment of SDN in traditional data centers, since it requires no SDN capability on the spine/core switches.
- Issue Date
- 2017
- Publisher
- Association for Computing Machinery
- Organization
- Gesellschaft für wissenschaftliche Datenverarbeitung
- Conference
- ICTCE '17
- ISBN
- 978-1-4503-5315-1
- Conference Place
- Osaka, Japan
- Event start
- 2017-10-22
- Event end
- 2017-10-24
- Language
- English