Network automation does not an automated network make. Today’s network engineers are frequently guilty of two indulgences. First, random acts of automation hacking. Second, pursuing aspirational visions of networking grandeur — complete with their literary adornments like “self-driving” and “intent-driven” — without a plan or a healthy automation practice to take them there.
Can a Middle Way be found, enabling engineers to set achievable goals, while attaining the broader vision of automated networks as code? Taking some inspiration from our software engineering brethren doing DevOps, I believe so.
How Not to Automate
There’s a phrase going around in our business: “To err is human; to propagate errors massively at scale is automation.”
Automation is a great way for any organization to speed up bad practices and #fail bigger. Unfortunately, when your business is network ops, the desire to be a cool “Ops” kid with some “Dev” chops — as opposed to just a CLI jockey — will quickly lead you down the automation road. That road might not lead you to those aspirational goals, although it certainly could expand your blast radius for failures.
Before we further contemplate self-driving, intent-driven networking, and every other phrase that’s all the rage today (although I’m just as guilty of such contemplation as anyone else at Juniper), we should take the time to define what we mean by “proper” in the phrase, “building an automated network properly.”
If you haven’t guessed already, it’s not about writing Python scripts. Programming is all well and good, but twenty minutes of design often really does save about two weeks of coding. To start hacking at a problem right away is probably the wrong approach. We need to step back from our goals, think about what gives them meaning, apply those goals to the broader picture, and plan accordingly.
To see what is possible with automation, we should look at successful patterns of automation outside of networking and the reasons behind them, so we may avoid the known bad habits and anti-patterns, and sidestep avoidable pitfalls. For well-tested patterns of automation, we needn’t look any further than the wealth of knowledge and experience in the arena of DevOps.
It matters what we call things. For better or worse, a name focuses the mind. The overall IT strategy to improve the speed, resilience, quality, and intelligence of our applications is not called automation or orchestration. While ITIL volumes make their steady march into museums, the new strategy to enable business speed and smarts is incontrovertible, and it’s called DevOps.
Initially, that term may invoke a blank page or even a transformation conundrum. But you can learn what it means to practice successful DevOps culture, processes, design, tooling and coding. DevOps can define your approach to the network, and is why we ought not promote network automation (which could focus the mind on the wrong objectives) and instead talk about DevNetOps as the application of patterns of DevOps applied to networking.
Networks as Code
The idea of infrastructure-as-code (IaC) has been around for a while, but surprisingly has seldom been applied to networking. Juniper Networks (where I hang my hat) and other networking vendors like Apstra have made some efforts over the years to move folks in this direction, but there is still a lot of work to do. For example, Juniper has had virtual form factors of most series of hardware systems, projects like Junosphere for network modeling in the cloud (many of us now use Ravello), and impressive presentations on IaC and professional services consulting. Juniper’s senior marketing director Mike Bushong (formerly with Plexxi) wrote about the network as code back in 2014.
IaC is generally well applied to cloud infrastructure, but it’s way harder to apply to bare metal. For evidence of this, just look at Triple-O, Kubernetes on Kubernetes, or Kubernetes on OpenStack on Kubernetes! That bottom, metal layer is quite the predicament.
To me, this means in networking, we should be easily applying IaC to software defined networking (SDN). But can we apply it to our network devices and manage the physical network? I asked Siri, and she said it was a trick question. As an armchair architect myself, I don’t have all the answers. But as I see it, here are some under-considered aspects for designing networks as code with DevNetOps:
In tech, everyone loves shiny objects, so let’s start there. Few network operators — even those who have learned some programming — are knowledgeable about the ecosystem of DevOps tooling, and few consider applying those same tools to networking. Learning Python and Ansible is just scratching the surface. There is a vast swath of DevOps tools for CI/CD, site reliability engineering (SRE), and at-scale cloud-native ops.
2. Chain the Tool Chain: a Pipeline as Code
When we approach the network as code, we need to consider network elements and their configurations as building blocks created in a code-development pipeline of dev/test/staging/production phases. Stringing together this pipeline shouldn’t be a manual process; it should be mostly coded to be automatic.
As with software engineering, there are hardware and foundational software elements with network engineering, such as operating systems that the operator will not create themselves, but rather just configure and extend. These configurations and extensions, with their underlying dependencies, can be built together, versioned, tested, and delivered. Thinking about the network as an exercise in development, automation should start in the development infrastructure itself.
3. Immutable Infrastructure
Virtualization and especially containers have made the concept of baking images very accessible, and immutable infrastructure popular. While there is still much work to do with network software disaggregation, containerization, and decoupling of services, there are many benefits of adopting immutable infrastructure that are equally applicable to networking. Today’s network devices are poster children for config drift, but to call them “snowflakes” would be an insult to actual snowflakes.
Applying principles of immutable infrastructure, I imagine a network where each device PXE-boots into a minimal OS and runs signed micro-service building blocks. Each device has declarative configs, decoupled secret management and rotation, and logging and other monitoring data with good overall audit ability and traceability — all of which is geared to take the network off the box ASAP.
Interestingly, practices such as SSH’ing into boxes would be rendered impossible, and practices that “savvy” network automators do today like running Ansible playbooks against an inventory of devices would be banished.
Upgrades to network software and even firmware/microcode on devices could be managed automatically, by means of canary tests and rolling upgrade patterns. To do this on a per-box or per-port basis, or at finer levels of flows or traffic-processing components, we need to be able to orchestrate traffic balancing and draining.
If that sounds complex, we can make things simpler. We could treat devices and their traffic like cattle instead of pets, and rely on their resilience. Killing and resurrecting a component would restart it with a new version. While this is suitable for some software applications, treating traffic as disposable is not yet desirable for all network applications and SLAs. Still, it would go a long way toward properly designing for resilience.
One implication of all this is the presence of redundancy in the network paths. As with any networking component, that’s very important for resilience. Drawing inspiration from scale-out architectures in DevOps and the microservices application model, redundancy and scale would go hand-in-hand by means of instance replication. So redundancy would neither be 1:1 nor active-passive. We should always be skeptical of architectures that include those anti-patterns.
Good design would tolerate a veritable networking chaos monkey. Burning down network software would circuit-break to limit failures. Killing links and even boxes, we would quickly re-converge as we often do today, but dead boxes, dead SDN functions or dead virtual network functions would act like phoenix servers, rising back up or staying down in case of repeated failures or detected hardware failures.
The pattern for preventing black-swan event failures is to practice forcing these failures, and thus practice automating around them, so that the connectivity or other network service and its performance is tolerant and acceptably resilient on its own SLA measuring stick, whatever the meta-service in question may be.
In each one of these above topics lies much more complexity than I will dive head-first into here. By introducing them here, my aim has been to demonstrate there are interesting patterns we may draw from, and some operators are doing so already. If you’ve ever heard the old Zen Buddhist koan of the sound of one hand clapping, that’s the sound you’re likely to hear from your own forehead, once the obviousness of applying DevOps to DevNetOps hits you squarely in the face.
Just as the hardest part of adopting DevOps is often cited to be breaking off one manageable goal at a time and focusing on that, I think we’ll find the same is true of DevNetOps. Before we even get there in networking, I think we need to scope the transformation properly of applying DevNetOps to the challenges and design of networking, especially with issues of basic physical connectivity and transport.
While “network automation” leads the mind to jump to things like applying configuration management tooling and programming today’s manual tasks, DevNetOps should remind us that there is a larger scope than mere automation coding. That scope includes culture, processes, design, and tools. Collectively, they may all lead us to a happier place.