Scale, SDN, and Network Virtualization
Posted: May 30, 2013
[This post was put together by Teemu Koponen, Andrew Lambeth, Rajiv Ramanathan, and Martin Casado]
Scale has been an active (and often contentious) topic in the discourse around SDN (and by SDN we refer to the traditional definition) long before the term was coined. Criticism of the work that lead to SDN argued that changing the model of the control plane from anything but full distribution would lead to scalability challenges. Later arguments reasoned that SDN results in *more* scalable network designs because there is no longer the need to flood the entire network state in order to create a global view at each switch. Finally, there is the common concern that calls into question the scalability of using traditional SDN (a la OpenFlow) to control physical switches due to forwarding table limits.
However, while there has been a lot of talk, there have been relatively few real-world examples to back up the rhetoric. Most arguments appeal to reason, argue (sometimes convincingly) from first principles, or point to related but ultimately different systems.
The goal of this post is to add to the discourse by presenting some scaling data, taken over a two-year period, from a production network virtualization solution that uses an SDN approach. Up front, we don’t want to overstate the significance of this post as it only covers a tiny sliver of the design space. However, it does provide insight into a real system, and that’s always an interesting centerpiece around which to hold a conversation.
Of course, under the broadest of terms, an SDN approach can have the same scaling properties as traditional networking. For example, there is no reason that controllers can’t run traditional routing protocols between them. However, a more useful line of inquiry is around the scaling properties of a system built using an SDN approach that actually benefits from the architecture, and scaling properties of an SDN system that differs from the traditional approach. We briefly touch both of these topics in the discussion below.
The system we’ll be describing underlies the network virtualization platform described here. The core system has been in development for 4-5 years, has been in production for over 2 years, and has been deployed in many different environments.
A Scale Graph
By scale, we’re simply referring to the number of elements (nodes, links, end points, etc.) that a system can handle without negatively impacting runtime (e.g. change in the topology, controller failure, software upgrade, etc.). In the context of network virtualization, the elements under control that we focus on are virtual switches, physical ports, and virtual ports. Below is a graph of the scale numbers for virtual ports and hypervisors under control that we’ve validated over the last two years for one particular use case.
The Y axis to the left is the number of logical ports (ports on logical switches), the Y axis on the right is the number of hypervisors (and therefore virtual switches) under control. We assume that the average number of logical ports per logical switch is constant (in this case 4), although varying that is clearly another interesting metric worth tracking. Of course, these results are in no way exhaustive, as they only reflect one use case that we commonly see in the field. Different configurations will likely have different numbers.
Some additional information about the graph:
- For comparison, the physical analog of this would be 100,000 servers (end hosts), 5,000 ToR switches, 25,000 VLANs and all the fabric ports that connect these ToR switches together.
- The gains in scale from Jan 2011 to Jan 2013 were all done with by improving the scaling properties of a single node. That is, rather than adding more resources by adding controller nodes, the engineering team continued to optimize the existing implementation (data structures, algorithms, language specific overhead, etc,.). However, the controllers were being run as a cluster during that time so they were still incurring the full overhead of consensus and data replication.
- The gains shown for the last two datapoints were only from distribution (adding resources), without any changes to the core scaling properties of a single node. In this case, moving from 3 to 4 and finally 5 nodes.
Raw scaling numbers are rarely interesting as they vary by use case, and the underlying server hardware running the controllers. What we do find interesting, though, is the relative increase in performance over time. In both cases, the increase in scale grows significantly as more nodes are added to the cluster, and as the implementation is tuned and improved.
It’s also interesting to note what the scaling bottlenecks are. While most of the discussion around SDN has focused on fundamental limits of the architecture, we have not found this be a significant contributor either way. That is, at this point we’ve not run into any architectural scaling limitations; rather, what we’ve seen are implementation shortcomings (e.g. inefficient code, inefficient scheduling, bugs) and difficulty in verification of very large networks. In fact, we believe there is significant architectural headroom to continue scaling at a similar pace.
SDN vs. Traditional Protocols
One benefit of SDN that we’ve not seen widely discussed is its ability to enable rapid evolution of solutions to address network scaling issues, especially in comparison to slow-moving standards bodies and multi-year ASIC design/development cycles. This has allowed us to continually modify our system to improve scale while still providing strong consistency guarantees, which are very important for our particular application space.
It’s easy to point out examples in traditional networking where this would be beneficial but isn’t practical in short time periods. For example, consider traditional link state routing. Generally, the topology is assumed to be unknown; for every link change event, the topology database is flooded throughout the network. However, in most environments, the topology is fixed or is slow changing and easily discoverable via some other mechanism. In such environments, the static topology can be distributed to all nodes, and then during link change events only link change data needs to be passed around rather than passing around megs of link state database. Changing this behavior would likely require a change to the RFC. Changes to the RFC, though, would require common agreement amongst all parties, and traditionally results in years of work by a very political standardization process.
For our system, however, as our understanding for the problem grows we’re able to evolve not only the algorithms and data structures used, but the distribution model (which is reflected by the last two points in the graph) and the amount of shared information.
Of course, the tradeoff for this flexibility is that the protocol used between the controllers is no longer standard. Indeed, the cluster looks much more like a tightly coupled distributed software system than a loose collection of nodes. This is why standard interfaces around SDN applications are so important. For network virtualization this would be the northbound side (e.g. Quantum), the southbound side (e.g. ovsdb-conf), and federation between controller clusters.
This is only a snapshot of a very complex topic. The point of this post is not to highlight the scale of a particular system—clearly a moving target—but rather to provide some insight into the scaling properties of a real application built using an SDN approach.