A year of RackN – 9 lessons from the front lines of evangalizing open physical ops

Let’s avoid this > “We’re heading right at the ground, sir!  Excellent, all engines full power!

another scale? oars & motors. WWF managing small scale fisheries

RackN is refining our from “start to scale” message and it’s also our 1 year anniversary so it’s natural time for reflection. While it’s been a year since our founders made RackN a full time obsession, the team has been working together for over 5 years now with the same vision: improve scale datacenter operations.

As a backdrop, IT-Ops is under tremendous pressure to increase agility and reduce spending.  Even worse, there’s a building pipeline of container driven change that we are still learning how to operate.

Over the year, we learned that:

  1. no one has time to improve ops
  2. everyone thinks their uniqueness is unique
  3. most sites have much more in common than is different
  4. the differences between sites are small
  5. small differences really do break automation
  6. once it breaks, it’s much harder to fix
  7. everyone plans to simplify once they stop changing everything
  8. the pace of change is accelerating
  9. apply, rinse, repeat with lesson #1

Where does that leave us besides stressed out?  Ops is not keeping up.  The solution is not to going faster: we have to improve first and then accelerate.

What makes general purpose datacenter automation so difficult?  The obvious answer, variation, does not sufficiently explain the problem. What we have been learning is that the real challenge is ordering of interdependencies.  This is especially true on physical systems where you have to really grok* networking.

The problem would be smaller if we were trying to build something for a bespoke site; however, I see ops snowflaking as one of the most significant barriers for new technologies. At RackN, we are determined to make physical ops repeatable and portable across sites.

What does that heterogeneous-first automation look like? First, we’ve learned that to adapt to customer datacenters. That means using the DNS, DHCP and other services that you already have in place. And dealing with heterogeneous hardware types and a mix of devops tools. It also means coping with arbitrary layer 2 and layer 3 networking topologies.

This was hard and tested both our patience and architecture pattern. It would be much easier to enforce a strict hardware guideline, but we knew that was not practical at scale. Instead, we “declared defeat” about forcing uniformity and built software that accepts variation.

So what did we do with a year?  We had to spend a lot of time listening and learning what “real operations” need.   Then we had to create software that accommodated variation without breaking downstream automation.  Now we’ve made it small enough to run on a desktop or cloud for sandboxing and a new learning cycle begins.

We’d love to have you try it out: rebar.digital.

* Grok is the correct work here.  Thinking that you “understand networking” is often more dangerous when it comes to automation.

How do platforms die? One step at a time [the Fidelity Gap]

The RackN team is working on the “Start to Scale” position for Digital Rebar that targets the IT industry-wide “fidelity gap” problem.  When we started on the Digital Rebar journey back in 2011 with Crowbar, we focused on “last mile” problems in metal and operations.  Only in the last few months did we recognize the importance of automating smaller “first mile” desktop and lab environments.

A fidelityFidelity Gap gap is created when work done on one platform, a developer laptop, does not translate faithfully to the next platform, a QA lab.   Since there are gaps at each stage of deployment, we end up with the ops staircase of despair.

These gaps hide defects until they are expensive to fix and make it hard to share improvements.  Even worse, they keep teams from collaborating.

With everyone trying out Container Orchestration platforms like Kubernetes, Docker Swarm, Mesosphere or Cloud Foundry (all of which we deploy, btw), it’s important that we can gracefully scale operational best practices.

For companies implementing containers, it’s not just about turning their apps into microservice-enabled immutable-rock stars: they also need to figure out how to implement the underlying platforms at scale.

My example of fidelity gap harm is OpenStack’s “all in one, single node” DevStack.  There is no useful single system OpenStack deployment; however, that is the primary system for developers and automated testing.  This design hides production defects and usability issues from developers.  These are issues that would be exposed quickly if the community required multi-instance development.  Even worse, it keeps developers from dealing with operational consequences of their decisions.

What are we doing about fidelity gaps?  We’ve made it possible to run and faithfully provision multi-node systems in Digital Rebar on a relatively light system (16 Gb RAM, 4 cores) using VMs or containers.  That system can then be fully automated with Ansible, Chef, Puppet and Salt.  Because of our abstractions, if deployment works in Digital Rebar then it can scale up to 100s of physical nodes.

My take away?  If you want to get to scale, start with the end in mind.

Introducing Digital Rebar. Building strong foundations for New Stack infrastructure

digital_rebarThis week, I have the privilege to showcase the emergence of RackN’s updated approach to data center infrastructure automation that is container-ready and drives “cloud-style” DevOps on physical metal.  While it works at scale, we’ve also ensured it’s light enough to run a production-fidelity deployment on a laptop.

You grow to cloud scale with a ready-state foundation that scales up at every step.  That’s exactly what we’re providing with Digital Rebar.

Over the past two years, the RackN team has been working on microservices operations orchestration in the OpenCrowbar code base.  By embracing these new tools and architecture, Digital Rebar takes that base into a new directions.  Yet, we also get to leverage a scalable heterogeneous provisioner and integrations for all major devops tools.  We began with critical data center automation already working.

Why Digital Rebar? Traditional data center ops is being disrupted by container and service architectures and legacy data centers are challenged with gracefully integrating this new way of managing containers at scale: we felt it was time to start a dialog the new foundational layer of scale ops.

Both our code and vision has substantially diverged from the groundbreaking “OpenStack Installer” MVP the RackN team members launched in 2011 from inside Dell and is still winning prizes for SUSE.

We have not regressed our leading vendor-neutral hardware discovery and configuration features; however, today, our discussions are about service wrappers, heterogeneous tooling, immutable container deployments and next generation platforms.

Over the next few days, I’ll be posting more about how Digital Rebar works (plus video demos).

As Docker rises above (and disrupts) clouds, I’m thinking about their community landscape

Watching the lovefest of DockerConf last week had me digging up my April 2014 “Can’t Contain(erize) the Hype” post.  There’s no doubt that Docker (and containers more broadly) is delivering on it’s promise.  I was impressed with the container community navigating towards an open platform in RunC and vendor adoption of the trusted container platforms.

I’m a fan of containers and their potential; yet, remotely watching the scope and exuberance of Docker partnerships seems out of proportion with the current capabilities of the technology.

The latest update to the Docker technology, v1.7, introduces a lot of important network, security and storage features.  The price of all that progress is disruption to ongoing work and integration to the ecosystem.

There’s always two sides to the rapid innovation coin: “Sweet, new features!  Meh, breaking changes to absorb.”

Docker Ecosystem Explained

Docker Ecosystem Explained

There remains a confusion between Docker the company and Docker the technology.  I like how the chart (right) maps out potential areas in the Docker ecosystem.  There’s clearly a lot of places for companies to monetize the technology; however, it’s not as clear if the company will be able to secede lucrative regions, like orchestration, to become a competitive landscape.

While Docker has clearly delivered a lot of value in just a year, they have a fair share of challenges ahead.  

If OpenStack is a leading indicator, we can expect to see vendor battlegrounds forming around networking and storage.  Docker (the company) has a chance to show leadership and build community here yet could cause harm by giving up the arbitrator role be a contender instead.

One thing that would help control the inevitable border skirmishes will be clear definitions of core, ecosystem and adjacencies.  I see Docker blurring these lines with some of their tools around orchestration, networking and storage.  I believe that was part of their now-suspended kerfuffle with CoreOS.

Thinking a step further, parts of the Docker technology (RunC) have moved over to Linux Foundation governance.  I wonder if the community will drive additional shared components into open governance.  Looking at Node.js, there’s clear precedent and I wonder if Joyent’s big Docker plans have them thinking along these lines.

StackEngine Docker on Metal via RackN Workload for OpenCrowbar

6/19: This was CROSS POSTED WITH STACKENGINE

In our quest for fast and cost effective container workloads, RackN and StackEngine have teamed up to jointly develop a bare metal StackEngine workload for the RackN Enterprise version of OpenCrowbar.  Want more background on StackEngine?  TheNewStack.io also did a recent post covering StackEngine capabilities.

While this work is early, it is complete enough for field installs.  We’d like to include potential users in our initial integration because we value your input.

Why is this important?  We believe that there are significant cost, operational and performance benefits to running containers directly on metal.  This collaboration is a tangible step towards demonstrating that value.

What did we create?  The RackN workload leverages our enterprise distribution of OpenCrowbar to create a ready state environment for StackEngine to be able to deploy and automate Docker container apps.

In this pass, that’s a pretty basic Centos 7.1 environment that’s hardware and configured.  The workload takes your StackEngine customer key as the input.  From there, it will download and install StackEngine on all the nodes in the system.  When you choose which nodes also manage the cluster, the workloads will automatically handle the cross registration.

What is our objective?  We want to provide a consistent and sharable way to run directly on metal.  That accelerates the exploration of this approach to operationalizing container infrastructure.

What is the roadmap?  We want feedback on the workload to drive the roadmap.  Our first priority is to tune to maximize performance.  Later, we expect to add additional operating systems, more complex networking and closed-loop integration with StackEngine and RackN for things like automatic resources scheduling.

How can you get involved?  If you are interested in working with a tech-preview version of the technology, you’ll need to a working OpenCrowbar Drill implementation (via Github or early access available from RackN), a StackEngine registration key and access to the RackN/StackEngine workload (email info@rackn.com or info@stackengine.com for access).

Docker-Machine Crowbar Driver Delivers Metal Containers

I’ve just completed a basic Docker Machine driver for OpenCrowbar.  This enables you to quickly spin-up (and down) remote Docker hosts on bare metal servers from their command line tool.  There are significant cost, simplicity and performance advantages for this approach if you were already planning to dedicate servers to container workloads.

Docker Machine

The basics are pretty simple: using Docker Machine CLI you can “create” and “rm” new Docker hosts on bare metal using the crowbar driver.  Since we’re talking about metal, “create” is really “assign a machine from an available pool.”

Behind the scenes Crowbar is doing a full provision cycle of the system including installing the operating system and injecting the user keys.  Crowbar’s design would allow operators to automatically inject additional steps, add monitoring agents and security, to the provisioning process without changing the driver operation.

Beyond Create, the driver supports the other Machine verbs like remove, stop, start, ssh and inspect.  In the case of remove, the Machine is cleaned up and put back in the pool for the next user [note: work remains on the full remove>recreate process].

Overall, this driver allows Docker Machine to work transparently against metal infrastructure along side whatever cloud services you also choose.

Want to try it out?

  1. You need to setup OpenCrowbar – if you follow the defaults (192.168.124.10 ip, user, password) then the Docker Machine driver defaults will also work. Also, make sure you have the Ubuntu 14.04 ISO available for the Crowbar provisioner
  2. Discover some nodes in Crowbar – you do NOT need metal servers to try this, the tests work fine with virtual machines (tools/kvm-slave &)
  3. Clone my Machine repo (Wde’re looking for feedback before a pull to Docker/Machine)
  4. Compile the code using script/build.
  5. Allocate a Docker Node using  ./docker-machine create –driver crowbar testme
  6. Go to the Crowbar UI to watch the node be provisioned and configured into the Docker-Machines pool
  7. Release the node using ./docker-machine rm testme
  8. Go to the Crowbar UI to watch the node be redeployed back to the System pool
  9. Try to contain your enthusiasm 🙂

Want More?  Linux binary & readme.

Are VMs becoming El Caminos? Containers & Metal provide new choices for DevOps

I released “VMS ARE DEAD” this post two weeks ago on DevOps.com.  My point here is that Ops Automation (aka DevOps) is FINALLY growing beyond Cloud APIs and VMs.  This creates a much richer ecosystem of deployment targets instead of having to shoehorn every workload into the same platform.

In 2010, it looked as if visualization had won. We expected all servers to virtualize workloads and the primary question was which cloud infrastructure manager would dominate. Now in 2015, the picture is not as clear. I’m seeing a trend that threatens the “virtualize all things” battle cry.

IMG_20150301_170558985Really, it’s two intersecting trends: metal is getting cheaper and easier while container orchestration is advancing on rockets. If metal can truck around the heavy stable workloads while containers zip around like sports cars, that leaves VMs as a strange hybrid in the middle.

What’s the middle? It’s the El Camino, that notorious discontinued half car, half pick-up truck.

The explosion of interest in containerized workloads (I know, they’ve been around for a long time but Docker made them sexy somehow) has been creating secondary wave of container orchestration. Five years ago, I called that Platform as a Service (PaaS) but this new generation looks more like a CI/CD pipeline plus DevOps platform than our original PaaS concepts. These emerging pipelines obfuscate the operational environment differently than virtualized infrastructure (let’s call it IaaS). The platforms do not care about servers or application tiers, their semantic is about connecting services together. It’s a different deployment paradigm that’s more about SOA than resource reservation.

On the other side, we’ve been working hard to make physical ops more automated using the same DevOps tool chains. To complicate matters, the physics of silicon has meant that we’ve gone from scale up to scale out. Modern applications are so massive that they are going to exceed any single system so economics drives us to lots and lots of small, inexpensive servers. If you factor in the operational complexity and cost of hypervisors/clouds, an small actual dedicated server is a cost-effective substitute for a comparable virtual machine.

I’ll repeat that: a small dedicated server is a cost-effective substitute for a comparable virtual machine.

I am not speaking against virtualize servers or clouds. They have a critical role in data center operations; however, I hear from operators who are rethinking the idea that all servers will be virtualized and moving towards a more heterogeneous view of their data center. Once where they have a fleet of trucks, sports cars and El Caminos.

Of course, I’d be disingenuous if I neglected to point out that trucks are used to transport cars too. At some point, everything is metal.

Want more metal friendly reading?  See Packet CEO Zac Smith’s thinking on this topic.