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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Building Scalable Software Defined Open Flow Networks
Committee:
Dr. Riley, Advisor
Dr. Blough, Co-Advisor
Dr. Owen, Chair
Dr. Clark
Abstract:
The objective of the proposed research is to develop techniques attacking the roots of Software Defined Networking (SDN) OpenFlow scalability problems. SDN is widely regarded as the next generation networking technique, which can create programmable, flexible, and agile networks whilst reducing costs. The core of SDN is to separate and logically centralize network control from its data plane. To achieve this separation, most SDN implementations use the de facto southbound protocol OpenFlow as the communication interface between the control and data planes. However, the scalability problem bottlenecks the deployment of SDN OpenFlow for large networks. The roots of SDN OpenFlow scalability problem are the centralized architecture of control plane and the unmatched capabilities of OpenFlow switches to deal with the massive events generated by the fine-grained granularity control mechanism. In this proposed research, the existing non-centralized control plane architectures will be compared with the centralized one in order to answer the question, "Which control plane architecture scales best?" Then, traffic engineering (TE) which is one of the key applications for the most scalable control architecture, the peer-to-peer control plane, will be investigated. The expected result of this investigation is a novel protocol which specifies how to exchange the network and application information between neighboring SDN domains to enable efficient and intelligent TE algorithms in each controller. As for weak OpenFlow switches, I work around the hardware manufacturing related limitations such as CPU and bus bandwidth by improving the utilization of the existing precise hardware resources to mitigate the scalability problem. Specifically, machine learning techniques will be exploited to make flow table management in OpenFlow switches more intelligent and efficient.
