Chapter 2: Use Cases

A good way to understand the value of SDN is to look at how it is used in practice. Doing so also helps explain the different perspectives on what SDN means, corresponding to what we refer to as “pure play” versus “hybrid/lite” Software-Defined Networking in the previous chapter. But before getting into how SDN is used, we start by first summarizing who is using it.

First, SDN has been embraced and widely deployed by cloud providers, with Google, Facebook, and Microsoft being the most public about adoption. While their platforms and solutions are still mostly proprietary, they have open sourced individual components in an effort to catalyze wider adoption. We discuss these individual components in later chapters.

Second, large network operators like AT&T, DT, NTT, and Comcast publicly talk about their plans to deploy SDN-based solutions—especially in their access networks—but they are proceeding cautiously, with most of their initiatives either using hybrid approaches, or in the case of pure play SDN, just starting to go into production. Of particular note is Comcast, which has deployed the open source components described in this book throughout their production network.

Finally, enterprises have begun to adopt SDN, but there are two things to note about this situation. One is that while pure play SDN is deployed in some Universities, with the goal of supporting research and innovation, adoption is slower for enterprises in general. The most likely path-to-adoption for pure play SDN by enterprises is via managed edge services offered by cloud providers. The idea is to connect on-premise clusters running edge workloads with public clouds running scalable datacenter workloads. The second is that many enterprise vendors offer SDN products, where the focus has been more on the benefits of logical control plane centralization rather than open interfaces to the data plane. Network virtualization and SD-WAN (software-defined wide area networks) have both had considerable success in the enterprise, as discussed below.

2.1 Network Virtualization

The first widely-adopted use case for SDN was to virtualize the network. Virtual networks, including both Virtual Private Networks (VPNs) and Virtual Local Area Networks (VLANs), have been a part of the Internet for years. VLANs have historically proven useful within enterprises, where they are used to isolate different organizational groups, such as departments or labs, giving each of them the appearance of having their own private LAN. However, these early forms of virtualization were quite limited in scope and lacked many of the advantages of SDN. You could think of them as virtualizing the address space of a network but not all its other properties, such as firewall policies or higher-level network services like load balancing.

The original idea behind using SDN to create virtual networks is widely credited to the team at Nicira, whose approach is described in in an NSDI paper by Teemu Koponen and colleagues. The key insight was that modern clouds required networks that could be programmatically created, managed, and torn down, without a sysadmin having to manually configure, say, VLAN tags on some number of network switches. By separating the control plane from the data plane, and logically centralizing the control plane, it became possible to expose a single API entry point for the creation, modification, and deletion of virtual networks. This meant that the same automation systems that were being used to provision compute and storage capacity in a cloud (such as OpenStack at the time) could now programmatically provision a virtual network with appropriate policies to interconnect those other resources.

Further Reading

T. Koponen et al. Network Virtualization in Multi-tenant Datacenters. NSDI, April, 2014.

The rise of network virtualization followed by several years the rise of compute virtualization, and was very much enabled by it. Compute virtualization made manual server provisioning a thing of the past, and exposed the manual and time-consuming processes of network configuration as the “long pole” in delivering a cloud service. Virtual machine migration, which enabled running VMs to move from one network location to another (taking their IP addresses with them), further exposed the limitations of manual network configuration. This need to automate network provisioning was first recognized by large cloud providers but eventually became mainstream in enterprise datacenters.

As microservices and container-based systems such as Kubernetes have gained in popularity, network virtualization has continued to evolve to meet the needs of these environments. There are a range of open source network “plugins” (Calico, Flannel, Antrea, etc.) that provide network virtualization services for Kubernetes.

Because network virtualization set out to deliver a full set of network services in a programmatic way, its impact went beyond the simplification and automation of network provisioning. As virtual networks became lightweight objects, created and destroyed as needed, with a full set of services (such as stateful firewalling, deep-packet inspection, and so on), a new approach to network security was enabled. Rather than adding security features after the network was created, security features could be created as an inherent part of the network itself. Furthermore, with no limit on how many virtual networks could be created, as approach known as microsegmentation took hold. This entails the creation of fine-grained, isolated networks (microsegments) specific to the needs of, say, a group of processes implementing a single distributed application. Microsegmentation offers clear benefits over prior approaches to network security, dramatically reducing the attack surface and the impact of attacks spreading throughout an enterprise or data center.

It’s worth noting that to create virtual networks as we have described, it is necessary to encapsulate packets from the virtual networks in a way that lets them traverse the underlying physical network. As a simple example, a virtual network can have its own private address space which is decoupled from the underlying physical address space. For this reason, virtual networks have used a range of encapsulation techniques, of which VXLAN (briefly discussed in Chapter 1) is probably the most well known. In recent years, a more flexible encapsulation called GENEVE (Generic Network Virtualization Encapsulation) has emerged.


Figure 9. An example network virtualization system

A typical network virtualization system looks something like Figure 9. The Network Virtualization Controller is an SDN controller that exposes a northbound API by which networks can be created, monitored and modified. It connects to virtual switches running on hosts–in this case, hypervisors supporting virtual machines. Virtual networks are created by programming the virtual switches to forward packets, with appropriate encapsulation, from host to host across the underlay network.

There have been reasonable debates about whether network virtualization is really SDN. Certainly it displays many of the properties we discussed in the previous chapter—the original Nicira network virtualization platform even used OpenFlow to communicate between its central controller and the data plane elements. And the centralization benefits of SDN are at the core of what made network virtualization possible, particularly as an enabler of network automation. On the other hand, network virtualization has not really enabled the disaggregation of networks envisioned by SDN: the controllers and the switches in a network virtualization system are typically quite tightly integrated using proprietary signalling methods rather than an open interface. And because the focus of network virtualization has been on connecting virtual machines and containers, it is usually implemented as an overlay among the servers on which those computing abstractions are implemented. Sitting underneath that overlay is a physical network, which network virtualization just takes as given (and that physical network need not implement SDN at all).

This observation about different aspects of SDN being implemented in switches versus end hosts is an important one that we return to in Section 3.1 (where we outline the overall SDN architecture), and again in Chapter 8 (where we describe Network Virtualization in more detail).

2.2 Switching Fabrics

The predominant use case for pure play SDN is within cloud datacenters, where for reasons of both lowering costs and improving feature velocity, cloud providers have moved away from proprietary switches (i.e., those traditionally sold by network vendors), in favor of bare-metal switches built using merchant silicon switching chips. These cloud providers then control the switching fabric that interconnects their servers entirely in software. This is the use case we explore in-depth throughout this book, so for now we give only a brief introduction.

A datacenter switching fabric is a network often designed according to a leaf-spine topology. The basic idea is illustrated by the small 4-rack example shown in Figure 10. Each rack has a Top-of-Rack (ToR) switch that interconnects the servers in that rack; these are referred to as the leaf switches of the fabric. (There are typically two such ToR switches per rack for resilience, but the figure shows only one for simplicity.) Each leaf switch then connects to a subset of available spine switches, with two requirements: (1) that there be multiple paths between any pair of racks, and (2) that each rack-to-rack path is two-hops (i.e., via a single intermediate spine switch). Note that this means in leaf-spine designs like the one shown in Figure 10, every server-to-server path is either two hops (server-leaf-server in the intra-rack case) or four hops (server-leaf-spine-leaf-server in the inter-rack case).


Figure 10. Example of a leaf-spine switching fabric common to cloud datacenters and other clusters, such as on-premises edge clouds.

The main fabric-control software sets up L2 forwarding (bridging) within a server-rack, and L3 forwarding (routing) across racks. The use of L3 down-to-the ToR switches is a well-known concept in leaf-spine fabrics, mainly due to L3 scaling better than L2. In such cases, the ToRs (leaves) route traffic by hashing IP flows to different spines using Equal-Cost Multipath (ECMP) forwarding. Because every ToR is 2-hops away from every other ToR, there are multiple such equal-cost paths. (Internally, the control software takes advantage of label switching concepts similar to that used by MPLS.) Having the fabric control software also provide L2-bridging comes from the need to support legacy workloads that often expect to communicate over an L2 network. There is much more to implementing a leaf-spine fabric, but we postpone a more complete description until Chapter 7, where we describe the specifics of the SD-Fabric implementation.

2.3 Traffic Engineering for WANs

Another cloud-inspired use case is traffic engineering applied to the wide-area links between datacenters. For example, Google has publicly described their private backbone, called B4, which is built entirely using bare-metal switches and SDN. Similarly, Microsoft has described an approach to interconnecting their data centers called SWAN. A central component of both B4 and SWAN is a Traffic Engineering (TE) control program that provisions the network according to the needs of various classes of applications.

The idea of traffic engineering for packet-switched networks is almost as old as packet switching itself, with some ideas of traffic-aware routing having been tried in the Arpanet. However, traffic engineering only really became mainstream for the Internet backbone with the advent of MPLS, which provides a set of tools to steer traffic to balance load across different paths. However, a notable shortcoming of MPLS-based TE is that path calculation, like traditional routing, is a fully distributed process. Central planning tools are common but the real-time management of MPLS paths remains fully distributed. This means that it is near impossible to achieve any sort of global optimization, as the path calculation algorithms–which kick in any time a link changes status, or as traffic loads change–are making local choices about what seems best.

Consider the example in Figure 11. Assume that all links are of unit capacity and we are trying to find paths for three unit flows of traffic. In the figure on the left, Flow A is placed first and picks one of the two shortest paths available. Flow B is placed next and takes the shortest remaining path, as the single-hop path is already filled by Flow A. When placing Flow C last, there is no choice but the long path. But a central algorithm that looked at all three flows at once and tried to place them optimally would end up with the much less wasteful set of paths shown on the right hand side of the figure. While this is a contrived example, sub-optimal outcomes as shown on the left are unavoidable when there is no central view of traffic.


Figure 11. Example of non-optimal traffic engineering (left) and optimal placement (right).

B4 and SWAN recognize this shortcoming and move the path calculation to a logically centralized SDN controller. When a link fails, for example, the controller calculates a new mapping of traffic demands onto available links, and programs the switches to forward traffic flows in such a way that no link is overloaded.

Over many years of operation, these approaches have become more sophisticated. For example, B4 evolved from treating all traffic equally to supporting a range of traffic classes with different levels of tolerance to delay and availability requirements. Examples of traffic classes included: (1) copying user data (e.g., email, documents, audio/video) to remote datacenters for availability; (2) accessing remote storage by computations that run over distributed data sources; and (3) pushing large-scale data to synchronize state across multiple datacenters. In this example, user-data represents the lowest volume on B4, is the most latency sensitive, and is of the highest priority. By breaking traffic up into these classes with different properties, and running a path calculation algorithm for each one, the team was able to considerably improve the efficiency of the network, while still meeting the requirements of the most demanding applications.

Through a combination of centralizing the decision-making process, programmatically rate-limiting traffic at the senders, and differentiating classes of traffic, Google has been able to drive their link utilizations to nearly 100%. This is two to three times better than the 30-40% average utilization that WAN links are typically provisioned for, which is necessary to allow those networks to deal with both traffic bursts and link/switch failures. Microsoft’s reported experience with SWAN was similar. These hyperscale experiences with SDN show both the value of being able to customize the network and the power of centralized control to change networking abstractions. A conversation with Amin Vahdat, Jennifer Rexford, and David Clark is especially insightful about the thought process in adopting SDN.

Further Reading

A. Vahdat, D. Clark, and J. Rexford. A Purpose-built Global Network: Google’s Move to SDN. ACM Queue, December 2015.

2.4 Software-Defined WANs

Another use-case for SDN that has taken off for enterprise users is Software-Defined Wide-Area Networks (SD-WAN). Enterprises have for many years been buying WAN services from telecommunications companies, mostly to obtain reliable and private network services to interconnect their many locations–main offices, branch offices, and corporate data centers. For most of the 21st century the most common technical approach to building these networks has been MPLS, using a technique known as MPLS-BGP VPNs (virtual private networks). The rapid rise of SD-WAN as an alternative to MPLS is another example of the power of centralized control.

Provisioning a VPN using MPLS, while less complex than most earlier options, still requires some significant local configuration of both the Customer Edge (CE) router located at each customer site, and the Provider Edge (PE) router to which that site would be connected. In addition, it would typically require the provisioning of a circuit from the customer site to the nearest point of presence for the appropriate Telco.

With SD-WAN, there was a realization that VPNs lend themselves to centralized configuration. An enterprise wants its sites—and only its authorized sites—to be interconnected, and it typically wants to apply a set of policies regarding security, traffic prioritization, access to shared services and so on. These can be input to a central controller, which can then push out all the necessary configuration to a switch located at the appropriate office. Rather than manually configuring a CE and a PE every time a new site is added, it is possible to achieve “zero-touch” provisioning: an appliance is shipped to the new site with nothing more than a certificate and an address to contact, which it then uses to contact the central controller and obtain all the configuration it needs. Changes to policy, which might affect many sites, can be input centrally and pushed out to all affected sites. An example policy would be “put YouTube traffic into the lowest priority traffic class” or “allow direct access to a given cloud service from all branch offices”. The idea is illustrated in Figure 12.


Figure 12. An SD-WAN controller receives policies centrally and pushes them out to edge switches at various sites. The switches build an overlay of tunnels over the Internet or other physical networks, and implement policies including allowing direct access to cloud services.

Note that the “private” part of the VPN is generally achieved by the creation of encrypted tunnels between locations. This is another example of a task that is painful to set up using traditional box-by-box configuration but easy to achieve when all switches are receiving their configuration from a central controller.

Many factors that are external to SDN came into play to make SD-WAN a compelling option. One of these was the ubiquity of broadband Internet access, meaning that there is no longer a reason to provision a dedicated circuit to connect a remote site, with the corresponding time and cost to install. But the privacy issue had to be solved before that could happen–as it was, using centrally managed, encrypted tunnels. Another was the increasing reliance on cloud services such as Office365 or, which have tended to replace on-premises applications in corporate data centers. It seems natural that you would choose to access those services directly from an Internet-connected branch, but traditional VPNs would backhaul traffic to a central site before sending it out to the Internet, precisely so that security could be controlled centrally. With SD-WAN, the central control over security policy is achieved, while the data plane remains fully distributed–meaning that remote sites can directly connect to the cloud services without backhaul. This is yet another example of how separating the control and data planes leads to a new network architecture.

As with some of the other use cases, SD-WAN is not necessarily doing everything that SDN promised. The control plane to data plane communication channel tends to be proprietary, and, like network virtualization, the SD-WAN solutions are overlay networks running on top of traditional networks. Nevertheless, SD-WAN has opened up a path for innovation because both the edge devices and the control planes are implemented in software, and centralization has offered new ways of tackling an old problem. Furthermore, there is plenty of competition among the players in the SD-WAN marketplace.

2.5 Access Networks

Access networks that implement the last mile connecting homes, businesses, and mobile devices to the Internet are another opportunity to apply SDN principles. Example access network technologies include Passive Optical Networks (PON), colloquially known as fiber-to-the-home, and the Radio Access Network (RAN) at the heart of the 4G/5G cellular network.

What’s interesting about these use cases is that unlike all the others—which effectively open general-purpose switches to programmable control—access networks are typically built from special-purpose hardware devices. The challenge is to transform these purpose-built devices into their merchant silicon/bare-metal counterparts, so they can be controlled by software. In the case of wired networks like PON, there are two such devices: Optical Line Terminals (OLT) and Broadband Network Gateways (BNG). In the case of the cellular network, there are also two relevant legacy components: eNodeB (the RAN base station) and the Enhanced Packet Core (EPC). A brief introduction is available online if you are not familiar with these acronyms.

Further Reading

Access Networks. Computer Networks: A Systems Approach, 2020.

Because these devices are purpose-built, not to mention closed and proprietary, they would seem to be worst-case examples for applying SDN principles. But that also means they represent an opportunity for the biggest payoff, and it is for precisely this reason that large network operators are actively pursuing software-defined PON and RAN networks. This initiative is sometimes referred to as CORD (Central Office Re-architected as a Datacenter) and has been the subject of much business analysis, including a comprehensive report by A.D. Little.

The central challenge of initiatives like CORD is to disaggregate the existing legacy devices, so as to isolate the underlying packet forwarding engine (the central element of the data plane) from the control plane. Doing so makes it possible to package the former as commodity hardware and to implement the latter in software.

Progress disaggregating PON-based access networks is quite far along, with a solution known as SEBA (SDN-Enabled Broadband Access) currently being deployed in production. Full details are beyond the scope of this book, but the general idea is to add bare-metal OLT devices to a cluster similar to the one presented in Figure 10, resulting in configuration like the one depicted in Figure 13. In other words, the cluster includes a mix of compute servers and access devices, interconnected by a switching fabric. And just as the Open Compute Project (OCP) has certified bare-metal ethernet switches, they now also certify bare-metal OLT devices. Both the fabric switches and access devices are controlled by a software-defined control plane, with the code that implements that control plane running on servers in the cluster.

Moreover, when the fabric is constructed using switches with programmable pipelines, certain functionality originally provided by the legacy hardware can be programmed into the switches that comprise the fabric. For example, BNG-equivalent functionality, which could be packaged as a Virtual Network Function (VNF) running on a general-purpose processor, is instead programmed directly into a programmable switch. This practice is sometimes called VNF off-loading because the packet processing is moved from the compute servers into the switches. This is a great example of what happens when switch data planes become programmable: developers write software that is able to take advantage of the hardware in new and unanticipated ways.


Figure 13. General hardware architecture of SEBA: SDN-Enabled Broadband Access.

Progress on Software-Defined Radio Access Networks (SD-RAN) lags software-defined broadband, with early-stage systems starting to run in trial deployments. Disaggregating the RAN is a bigger challenge, but the payoff will likely be even larger, as it leads to a 5G-empowered edge cloud. We revisit SD-RAN in Chapter 9, but for a broad introduction to how 5G is being implemented according to SDN principles, we recommend a companion book.

Further Reading

L. Peterson and O. Sunay. 5G Mobile Networks: A Systems Approach. June 2020.

The bottom line is that the effort to apply SDN principles to both fiber and mobile access networks starts with the same building block components described throughout this book. We will highlight where such software-defined access networks “plug into” the SDN software stack as we work our way through the details, with a full account presented in Chapter 9.

2.6 Network Telemetry

We conclude this overview of SDN use cases by looking at a recent example made possible by the introduction of programmable forwarding pipelines: In-Band Network Telemetry (INT). The idea of INT is to program the forwarding pipeline to collect network state as packets are being processed (i.e., “in-band”). This is in contrast to the conventional monitoring done by the control plane by reading various fixed counters (e.g., packets received/transmitted) or sampling subsets of packets (e.g., sFlow).

In the INT approach, telemetry “instructions” are encoded into packet header fields, and then processed by network switches as they flow through the forwarding pipeline. These instructions tell an INT-capable device what state to collect, and then how to write that state into the packet as it transits the network. INT traffic sources (e.g., applications, end-host networking stacks, hypervisors) can embed the instructions either in normal data packets or in special probe packets. Similarly, INT traffic sinks retrieve and report the collected results of these instructions, allowing the traffic sinks to monitor the exact data plane state that the packets observed (experienced) while being forwarded.

The idea is illustrated in Figure 14, which shows an example packet traversing a path from source switch S1 to sink switch S5 via transit switch S2. The INT metadata added by each switch along the path both indicates what data is to be collected for the packet, and records the corresponding data for each switch.


Figure 14. Illustration of Inband Network Telemetry (INT), with each packet collecting measurement data as it traverses the network.

INT is still early-stage, but it has the potential to provide qualitatively deeper insights into traffic patterns and the root causes of network failures. For example, INT can be used to measure and record queuing delay individual packets experience while traversing a sequence of switches along an end-to-end path, with a packet like the one shown in the figure reporting: “I visited Switch 1 @780ns, Switch 2 @1.3µs, Switch 5 @2.4µs.” This information can be used, for example, to detect microbursts—queuing delays measured over millisecond or even sub-millisecond time scales—as reported by Xiaoqi Chen and colleagues. It is even possible to correlate this information across packet flows that followed different routes, so as to determine which flows shared buffer capacity at each switch.

Further Reading

X. Chen, et. al. Fine-grained queue measurement in the data plane. ACM CoNEXT’19, December 2019.

Similarly, packets can report the decision making process that directed their delivery, for example, with something like: “In Switch 1, I followed rules 75 and 250; in Switch 2, I followed rules 3 and 80.” This opens the door to using INT to verify that the data plane is faithfully executing the forwarding behavior the network operator intended. We return to the potential of INT to impact how we build and operate networks in the concluding chapter of this book.

This use case illustrates once again a potential benefit of SDN: the ability to try out new ideas that would have in the past been infeasible. With traditional fixed-function ASICs doing the packet forwarding, you could never get the chance to try an idea like INT to see if the benefits justify the cost. It is this freedom to experiment and innovate that will lead to lasting benefits from SDN in the long run.