Ph.D. Proposal Oral Exam - Hemin Yang

Event Details
  • Date/Time:
    • Monday April 23, 2018
      1:00 pm - 3:00 pm
  • Location: Room 3100, Klaus
  • Phone:
  • URL:
  • Email:
  • Fee(s):
  • Extras:
No contact information submitted.

Summary Sentence: Building Scalable Software Defined Open Flow Networks

Full Summary: No summary paragraph submitted.

Title:  Building Scalable Software Defined Open Flow Networks


Dr. Riley, Advisor

Dr. Blough, Co-Advisor

Dr. Owen, Chair

Dr. Clark


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.

Additional Information

In Campus Calendar

ECE Ph.D. Proposal Oral Exams

Invited Audience
Phd proposal, graduate students
  • Created By: Daniela Staiculescu
  • Workflow Status: Published
  • Created On: Apr 12, 2018 - 5:27pm
  • Last Updated: Apr 12, 2018 - 5:27pm