The 10 new rules of open source infrastructure

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The 10 new rules of Open Source infrastructure

Recently, I gave a keynote at the Cloud Native / OpenStack Days in Tokyo titled “the ten new rules of open source infrastructure”. It was well received and folks pointed out on Twitter that they would like to see more detail around those ten rules. Others seemed to benefit from clarifying commentary. I’ve attempted to summarize the points I’ve made during the talk here, and happy to have a conversation or add more rules based on your observations in this space over the last ten years. I strongly believe there are some lasting concepts and axioms that are true in infrastructure IT, and documenting some of them is important to guide decisions that go into the next generation thinking as we evolve in this space.

1 Consume unmodified upstream.

The time for
vendors to proclaim that they are able to somehow make open source
projects “enterprise-ready” by releasing a “hardened” version of
infrastructure software based on those upstream projects is over.
OpenStack has been stable for multiple releases now and capable of
addressing even the most advanced use cases and workloads without any
vendor interference at all. See CERN. See AT&T.

I believe this
is the most important rule of them all because it is self-limiting to
not follow it: why would you restrict the number of people able to work
on, support and innovate with your platform in production by introducing
downstream patches? The whole point of open infrastructure is to be
able to engage with the larger community for support and to create a
common basis for hiring, training and innovating on your next-generation
infrastructure platform.

2 Infrastructure-is-a-Product.

When
implementing the next generation infrastructure, it is important to
remember that you are essentially entering a market with competing
alternatives for your constituency. In almost all cases that is a public
cloud alternative, but even legacy stacks based on older technologies
such as VMware can pose a significant risk, especially if your workload
adoption is slow. Outreach, engagement with developers, actively working
on migrating workloads on to the new platform is critical to reaching a
lower cost per computing unit then alternative platforms. The sooner
that point is reached, the better.

Infrastructure should also not
be treated as something “hand-crafted”. Large scale implementations are
without exception based on standardization of components and simplicity
in architecture and parameter tuning. Limiting the ability to transfer
knowledge of existing clusters to new teams, losing the ability to train
new staff, or recover from the “expert departure” in your team is key
and the only way to achieve this is by avoiding customized reference
architectures introducing technical debt into your infrastructure.

3 Automate for Day 1826.

Almost
all teams have not automated to the degree they ought to, and most of
them realize this at some level but fail to do something about it. Part
of the reason why certain tools have become popular with operators is
that they address the first 80% of all automation use cases quite nicely
but do so at the cost of being able to reasonably address the rest. The
result is that lifecycle management events such as upgrades, canarying,
expansion and so on remain complicated and fail to reduce the amount of
energy that event consumes. A simple test is this: if you still have to
SSH into a server to perform some task you simply have not automated
enough. Any and all events concerning that machine should be addressable
via API and through comprehensive automation and orchestration setup.

When
choosing your orchestration automation, assume that the technology
stack will change over the course of your hardware amortization period
(typically five years). Your VMware of today might be an OpenStack of
tomorrow, might turn into a Kubernetes cluster on top, right next to it
on bare metal, or even be replaced by it. You just don’t know. Once the
“Kubernetes of serverless” crystallizes, you will have to automate the
deployment and integration of that technology. Maintaining the same
operational paradigm across those events is more important than ever as
upstream innovation cycles shorten and the number of releases per year
increase.

4 Run at capacity on-prem. Use public cloud as overflow.

If
the goal is providing the best economics in the data center, running
your on-premise infrastructure as close to capacity as possible is a
natural consequence. Hardware should be chosen to provide the best value
for the money invested in it, which may not always lead to the lowest
cost in that investment, but will lead to the best economics overall, especially if the goal is to achieve comparable cost structures as public alternatives.

That
said, don’t mistake this rule as a call to avoid public cloud. On the
contrary, I recommend working with a minimum of two public cloud
providers in addition to having a solid on-premise strategy that
fulfills the economic parameters of our organization. Having two public
cloud partners allows for healthy competition and enforces cloud-neutral
automation in your operations, a key attribute of a successful
multi-cloud strategy. While having certified administrators for public
clouds is good, cloud API agnostic automation is better.

5 Upgrade, don’t backport.

At Ubuntu,
we have always endorsed this paradigm, and it continues to be true for
open infrastructure. As upstream project support cycles shorten (think
of the number of supported releases and maintenance windows for
OpenStack and Kubernetes, for example), getting into the habit of
upgrading rather than introducing technical debt that is exacerbating
the costs of literally every lifecycle management event that
comes after its introduction is one of the most important rules to
follow. With the right type of automation process in place, upgrading
should be predictable, and a solvable problem in a reasonable amount of
time. Running your infrastructure as a product, and consuming unmodified
upstream code are both contributory factors in that predictability, and
without them, this is an almost unmanageable task at any scale.

6 Workload placement matters.

In
a way, it’s understandable. The whole point of implementing a private
cloud is so that you don’t have to worry about this. However, when you do care,
it is typically almost impossible to establish any kind of debugging
baseline. Clouds are by nature dynamic, so debugging what happened when
some service-level violation occurred needs to take the changing nature
of the infrastructure into account. All clouds of reasonable volume have
this problem, and most operations teams ignore the necessity of
maintaining the correlation between what happens at the bare metal level
all the way to what happens at the virtual and container level. The
smaller the unit of consumable compute is (think large VMs vs small VMs
vs fat containers, vs … you get the idea), the more dynamic the
environment typically is, and establishing causation between symptoms
and root cause gets exponentially harder the more layers you introduce.
Think about workload placement as you onboard tenants and establish the
necessary telemetry to capture those events in their context. This will
lead to predictive analysis, which ultimately may allow you to introduce
AI into your operations (and the larger/more complex your cloud
infrastructure is, the more urgent that will be).

7 Plan for transition

I
made this point earlier, but it is unrealistic to expect a specific set
of hardware to be tied to a specific infrastructure throughout the
entirety of its lifespan. This is superbly exemplified in edge use
cases. Telco edge specifically will have to address changing workload
requirements over the next three years as some workloads will transition
into containerized versions of them, others may remain as a VM, and
some remain on bare metal. Thus, the nature of the edge and its
management requirements will evolve over the next three years.
Consequently, it’s not the containerized “end state” that is worth
designing for, but the state of transition. Again, the full
automation and identical operational paradigm across bare-metal
provisioning, virtual machine management, OpenStack and Kubernetes will
play a crucial role in achieving this design goal.

8 Security should not be special

Security
patches are patches; security operations is simply – operations. Most
cloud projects are devised between developers and operations and
infrequently do they involve the often separate “security team”. What
happens is that after much debate, discussion and creation of a roll-out
plan, security teams are confronted with “done deals” to which they
mostly react by throwing water on all of those plans. Security is a
mindset and an original posture that should be exhibited from the start
and continued to be focused on as part of the requirements. Security
isn’t special, it’s just as important and critical as any other
non-functional requirement that has to be met by the cluster in order to
meet the stakeholder’s expectations. So involve security early, often
and stay close, lest you’re willing to start over deep into the process.

9 Embrace shiny objects

The
whole point of open infrastructure is to foster innovation and to give
companies a competitive edge through the acceleration of their
next-generation application rollout. Why stand in the way? If your
developers want containers, why not? If your developers want serverless,
why not? Being part of the solution rather than deriding new technology
stacks as “shiny objects” only highlights a lack of confidence in the
existing operational paradigm and automation. Sure, it’s fun to engage
in some commentary over a beer after work – and that is exactly where it
should stay.

10 Be edgy, go Micro!

Shameless plug: I work for Canonical and I care very much about two innovative projects I would like to highlight.

Microstack
is a project for OpenStack Edge use cases and currently in beta. It
installs a full OpenStack cluster on a single node and will support
limited clustering once it goes GA. I’m super excited about it because
it elegantly solves the majority of the requirements of small
form-factor OpenStack in a clear and concise format, using the Snap
application container format. It’s innovative, small, and deserves your
attention.

Microk8s
is the same for Kubernetes. A single snap package to install on any of
the compatible Linux distributions, with a feature-rich add-on system
that lets you provision service meshes such as Istio and Linkerd,
Knative, Kubeflow and more. Check it out on microk8s.io.

Because
both are distributed as snaps, they can be used in an IoT/Devices
context as well as in a cloud or data center context. They can be
installed with a single command:

$ snap install microstack --beta --classic
$ snap install microk8s --classic

Conclusion

And
that’s it – observations over the last ten years in the cloud industry,
crystallized as ten new rules. Despite the intro image above, I don’t
intend those rules to be taken as ‘commandments’ and I’m certainly no
Moses in this regard. I am simply summarizing my observations that I’ve
made over the course of the last 10 years as an OpenStack architect and
now product manager in this space.

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