Reactive, Proactive, Predictive: SDN Models
#SDN #openflow A session at #interop sheds some light on SDN operational models
One of the downsides of speaking at conferences is that your session inevitably conflicts with another session that you'd really like to attend. Interop NY was no exception, except I was lucky enough to catch the tail end of a session I was interested in after finishing my own.
I jumped into OpenFlow and Software Defined Networks: What Are They and Why Do You Care? just as discussion about an SDN implementation at CERN labs was going on, and was quite happy to sit through the presentation.
CERN labs has implemented an SDN, focusing on the use of OpenFlow to manage the network. They partner with HP for the control plane, and use a mix of OpenFlow-enabled switches for their very large switching fabric.
All that's interesting, but what was really interesting (to me anyway) was the answer to my question with respect to the rate of change and how it's handled. We know, after all, that there are currently limitations on the number of inserts per second into OpenFlow-enabled switches and CERN's environment is generally considered pretty volatile.
The response became a discussion of SDN models for handling change. The speaker presented three approaches that essentially describe SDN models for OpenFlow-based networks:
Reactive models are those we generally associate with SDN and OpenFlow. Reactive models are constantly adjusting and are in flux as changes are made immediately as a reaction to current network conditions. This is the base volatility management model in which there is a high rate of change in the location of end-points (usually virtual machines) and OpenFlow is used to continually update the location and path through the network to each end-point. The speaker noted that this model is not scalable for any organization and certainly not CERN.
Proactive models anticipate issues in the network and attempt to address them before they become a real problem (which would require reaction). Proactive models can be based on details such as increasing utilization in specific parts of the network, indicating potential forthcoming bottlenecks. Making changes to the routing of data through the network before utilization becomes too high can mitigate potential performance problems. CERN takes advantage of sFlow and Netflow to gather this data.
A predictive approach uses historical data regarding the performance of the network to adjust routes and flows periodically. This approach is less disruptive as it occurs with less frequency that a reactive model but still allows for trends in flow and data volume to inform appropriate routes.
CERN uses a combination of proactive and predictive methods for managing its network and indicated satisfaction with current outcomes.
I walked out with two takeaways. First was validation that a reactive, real-time network operational model based on OpenFlow was inadequate for managing high rates of change. Second was the use of OpenFlow as more of an operational management toolset than an automated, real-time self-routing network system is certainly a realistic option to address the operational complexity introduced by virtualization, cloud and even very large traditional networks.
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