Nati Shalom's Blog

The Disruption Cycle: A Dime a Dozen

 

Before the dust even settled around the whopping Dell/EMC deal, another bombshell was dropped with the Red Hat acquisition of Ansible over the weekend.  While I was still processing my thoughts on what I think the Dell deal means for the enterprise IT industry - I had to make an abrupt about-face and think about this poster-child of the community and bottom-up startup approach of Ansible means for the IT industry as a whole, and how this may be an even more substantial win. 

The EMC/Dell deal seems to be centered more on the merits of a financial transaction, i.e. grabbing billions of dollars from the existing customer base, refunding debts, latching onto VMware as the only publicly traded company and so forth.  The following diagram summarizes this quite nicely:

Dell Acquisition of EMC (and associated Federation assets) – blue text is new [Source: Wikibon, 2015]

What seems to be clear is that while the EMC/Dell deal is good for its shareholders its equally bad news for their customers as noted by the David Vellante report on Wikibon as both companies will have to squeeze their customer base to justify the ROI for the deal.

“Dell is promising within 18-24 months it will de-leverage (i.e. pay down its debt). How do companies manage cash cows? Answer – they leverage their lockin: 1) Longer product cycles; 2) Increase maintenance/support costs; 3) Cut salaries; 4) Narrow the R&D focus; 5) Charge a premium – We expect this to be the likely Dell playbook for VMAX, VNX, Data Domain, Compellent, etc”

It's hard not to notice that there's nothing in this deal that tells a clear story on how the new company will transform into the new cloud/web-scale business model.

So if anything I think that the Dell/EMC deal marks an end of an era more than a new beginning as Cade Metz puts this nicely on wired.com

“HP. Cisco. Dell. EMC. IBM. Oracle. Think of them as the walking dead. Oh, sure, they'll shuffle along for some time. They'll sell some stuff. They'll make some money. They'll command some headlines. They may even do some new things. But as tech giants, they're dead...When someone asked what we should call that IBM-HP-EMC-Dell-Cisco merger, his response was wonderfully descriptive. He suggested we call the company Fucked By The Cloud.”

It's also interesting to read the reaction to this post on twitter :100:

The case for Red Hat/Ansible is the exact antipode of the EMC/Dell deal. The financial factor didn't really play any real factor at all, what really mattered is the developer mindshare, adoption rate and popularity of Ansible in the DevOps community.

The Ansible stats tell the story - plain & simple - high adoption rate, low revenue, and I would add, in essence a high adoption rate at the expense of revenue.

• Founded in 2013

• 40 people

• $6M investment round

• High & rapid adoption

• Low sales - Estimated to be ~$1M

• Acquired at $150M

The ROI on this deal is a no brainer for Ansible.  The rationale for the Red Hat acquisition isn't so much on the direct revenue from Ansible as a product, but rather the potential monetization from its  impressive adoption.  This is interesting as it clearly put the benchmark for a successful startup these days back on eyeballs and popularity, and less on having a sustainable business model. But this seems to be the new game, and technology companies are all scrambling to get in line with this new model.

What's also interesting is that we’re starting to see new companies emerging to disrupt not just the dinosaur of the world, but also the early cloud companies such as Puppet and Chef that are now being disrupted...and there's a long list of such companies. Salesforce is a good example for one that is also being threatened by this rapid disruption.

We can see a clear vicious cycle that applies to all companies old and new. New generation companies rise on the wave of adoption by providing free and open source tooling, as they start to monetize and build a sustainable business model they start to lose popularity, and a call for a new disruptive tool is heard.

That explains the timing behind this acquisition, Ansible had reached a ceiling when it comes to adoption, and failed to build a sustainable business model out of their product. Containers/Docker have disrupted the entire configuration management market, and to a large degree render it obsolete (the adoption curve shows a clear declining trend on all the other configuration management tools).

With this in mind Ansible is clearly at its peak right now, so their best option was to be acquired by a bigger company like Red Hat. Red Hat is probably the only company with a sustainable business model around open source, so for them the direct revenue from Ansible isn't that interesting but more of the combined revenue that could be generated by bundling Ansible into a bigger DevOps tool set. I'm sure that the fact that Ansible’s founders are also formerly Red Hat played a not small factor in the overall decision.

Final words

What have we learned from all this?

• Cisco, EMC, Dell, HP, IBM are “walking dead”

• Adoption/popularity is the new king

• The only constant is change!

Disrupt yourself before your competitors do --> Embrace change

Steve Jobs’ main mantra was "disrupt yourself before your competitors do" and indeed what's clear in both deals is that change is inevitable, and it can and will happen to big companies just as it can happen to a smaller company. Therefore, the assurance that we used to think we had by relying on big companies isn't a safe bet anymore, similarly the risk of using new technologies from startups isn't as great as it used to be.

In this world those who would survive are only those who are set to adopt new technologies fast. There’s a direct correlation between the speed of adoption and the speed of innovation. The challenge is actually even bigger for companies who grow fast. Chef and Puppet are a good example in this regard. They created a new disruption, grew fairly fast, but stagnated for a long time, and failed to respond to the new disruption in their domain. 

This is where I see the biggest shift in the value chain specifically in the new data center. The value is less on how we build the tools, but more on how we put them together, and how we can adopt new tools or replace existing ones fast in way that will allow us to always be ready for the next disruption. This explains the rise in orchestration tools, as I pointed out at the last OpenStack Summit, and in my previous post orchestration tool roundup. that plays an important role as a "gluing" mechanism in the area of DevOps and automation.

Making Hybrid Cloud Simple and Real with vCloud Air, TOSCA and Cloudify

 

 

While hybrid cloud has been a mainstay discussion in the cloud world for quite some time now (note this post of mine on hybrid cloud from 2011, when CloudStack was still Cloud.com), the reality is that setting up hybrid cloud has proven a fairly complex process. Therefore we've seen only a few real implementations of true hybrid cloud in the wild.

In this post, I  wanted to describe the joint work that we've invested together with the VMware cloud team to change this reality and finally make the hybrid cloud story simple and possible.

vCloud Air makes hybrid cloud truly simple and real

While most public clouds started as independent public cloud services, and only later started to add private cloud connectivity; the exact opposite is true for vCloud Air, which was designed primarily as an native extension of VMware private cloud. In this context, users are able to use the same tools currently in use to manage their local VMware environment in order to manage their public cloud resources, as well. This makes the hybrid cloud story significantly simpler, as there is no longer the need to worry about the connectivity between the two sides of the data center, and at the same time we get the cost benefit of using resources on-demand on a pay per use basis.

The ability to make hybrid cloud so simple fit well with the Cloudify pure-play orchestration vision, and made the integration with the two platforms a perfect fit.

In this post I wanted to spend some time on what that means for VMware and OpenStack users.

Putting vCloud Air, TOSCA and Cloudify  together

 

vCloud Air users needed a simple way to deploy and manage their applications on vCloud Air using DevOps tools similar to Amazon Cloud Formation.

As the landscape is still being shaped, and there’s no clear winner yet, it is very important for these users to have tools that will allow them to keep their options open.

TOSCA seemed to be a great fit in such a context, as it provides a standard templating language that doesn’t tie users into a particular platform.

On top of this, TOSCA orchestration also aims to go beyond the installation part of the application and covers all the aspects of the application lifecycle through the addition of workflows and policies that can be used to handle continuous deployment, self-healing, and auto-scaling processes.

On a similar note, many Cloudify users comes from Enterprise and Telco markets. Such users are already heavily invested in VMware. These customers are actively looking for a more agile and cost effective way to run their data center.  The ability to bring capacity on demand seamlessly into private cloud makes the entire private/hybrid cloud story truly attainable.

By putting vCloud Air, TOSCA and Cloudify together we’re getting the cost benefit of using on demand resources for our private data center in a way that doesn’t tie us to a specific platform and at the same time provide a more complete application lifecycle and management solution that enables full automation of our deployment and DevOps processes.

Extending the solution to the rest of the VMware stack

VMware provides three kinds of platforms today, the vCenter/vSphere environment that serves many of the enterprises data centers today, and VMWare Integrated OpenStack, which provides API compatibility with OpenStack for vSphere/vCenter environments.

Cloudify comes with native TOSCA support through plugins that allows you to use TOSCA blueprints as a templating language across both vCloud Air, vSphere and OpenStack that provides a consistent way to manage applications across all three environments.  On top of this, it also makes it simple to build mixed resources of all three environments all with the same blueprint.

Having pure-play orchestration and management that is backed by an industry standard, TOSCA, enables the decoupling of the way to manage applications from the underlying infrastructure. While there is still the need to rely on specific APIs and features of the specific infrastructure, they are now at a point where the cost of the switch isn’t as significant compared with the alternative of binding themselves directly into the specific underlying infrastructure.

The benefit for OpenStack users

Many OpenStack users are Enterprises and Telco customers who are known to be heavy users of VMware platforms. This integration makes the ability to mix and match existing VMware environments together with OpenStack and vCloud Air, providing greater flexibility to decide which applications and workloads fit best in which environment while using common and consistent management for their applications across environments.

What should we expect next?

Through our partnership with VMware we’re planning to make this integration simpler for vCloud Air users by allowing them to use TOSCA directly through the vCloud Air public cloud service. In addition, the plan is to make the hybrid cloud story between vCloud Air, vSphere and OpenStack as seamless and simple as possible so we will be adding more examples and tighter integration for all three platforms.

How should I get started?

• TOSCA Orchestration for VMware - Hybrid Cloud Made Simple
• vSphere Plugin Docs
• VMware Hybrid Cloud with Cloudify

PaaS vs Docker - why is it such a heated debate?

Docker started as just a software container on top of a Linux operating system which seemed like a simple optimization for a fat hypervisor.

Its disruptive force however comes from the fact that it does force us to rethink many of the layers of the cloud stack. Starting from the way we handle configuration management, through the way we handle networking and build systems, and even microservices. Not all of this is directly related to Docker per se, but that’s the difference between thinking of Docker as a container, and Docker as a change agent or a movement. 

Aside from Docker’s traditional analogy to the shipping world and how containerization changed the landscape of the maritime world, to me a similar analogy to this is moving from bricks and mortar to glass and metal buildings. You could think of glass and metal as just another form of constructing the same buildings and houses we’ve always had, however the introduction of glass and metal design changed the entire landscape and standard of our former city lives. The fact that with this new approach we can now build entire buildings in a fraction of the time, and rising to a height that is exponentially higher than with the former construction methods, is much more than optimization; it has led to a complete disruption and renewed way of thinking about architecture and design principles.

In this way, we can choose to think of Docker as a simple optimization for building PaaS - instead of just using buildpack for shipping our software into a PaaS – since we can now use a Docker images instead right?

The problem with that thinking lies in its root - thinking of Docker and containers as yet another software packaging tool is analogous to thinking that glass and metal are just another form of bricks.

Anyway the thing that brought me to write about this again is an interesting Twitter thread that developed on the subject over the previous weekend, which was triggered by one of my earlier posts on the subject - Why do I need PaaS if I use Docker?

A basic summary of what was discussed is whether Docker can be considered a platform without having supporting services, whether orchestration is the missing link, whether Docker is a viable VM replacement, and if PaaS is just a buzzword or actually constitutes anything on top of IaaS with orchestration implementations on various layers, and what really is the difference between abstraction and automation?

So, why is this *such* a heated debate?

To understand why is this such a heated debate we need to understand the various players in this discussion, as their perspective is very much influenced by whether you’re a cloud provider, a PaaS provider, an orchestration provider or a container provider.

Mapping the different players and their approach to this trend.

The cloud providers perspective:

The most interesting perspective IMO is that of the cloud providers. Cloud providers like Amazon and Google already provide a PaaS: Elastic Beanstalk and GAE, yet in their recent announcements they announced a new offering for orchestration and containers as a service that is not tied to their PaaS offering. Judging by the market reaction it looks like many of the users have been quite enthusiastic and in favor of this new offering.

What can we learn from this?

Cloud providers looks at PaaS as yet another tool to drive workloads into their cloud. In the case of AWS they don’t even charge extra for their PaaS other than the cost of the infrastructure instances that they use. Quoting from the AWS pricing page:

“There is no additional charge for Elastic Beanstalk – you only pay for the underlying AWS resources (e.g. Amazon EC2, Amazon S3) that your application consumes.”

That puts them in a much more pragmatic and unbiased position to basically offer containerization as part of their PaaS offering, or as  a new service as long as it meets their users demand and thus drives more utilization onto their infrastructure.

The fact that they decided to offer orchestration as an independent service and not an extension to their PaaS offering is IMO the strongest validation that this is probably the approach that best meets the user’s needs.

PaaS providers

PaaS providers on the other hand are making a good amount of money selling PaaS platforms and services. It is, therefore, clear that when they approach this question that they are biased, by definition. They view Docker as a threat and as a result are trying to minimize its real value, and position it as a natural evolution of their existing platform. Their strategy is to declare support for Docker as an underlying container. They would also offer the option to use containers as the packaging format for applications similar to buildpack. OpenShift from Red Hat have taken even another step in this direction and are planning to switch their underlying orchestration engine to Kubernetes.

To me, all this is a fine progression but the main question that still remains open is whether PaaS is indeed the right tool for handling more complex application workloads?

This opened up another interesting question - is there enough value left for PaaS if we can use containers with orchestration (the most obvious being Docker orchestration) as an automation and management tool?

That question sparked an interesting debate which I found to be fairly surprising as it seemed to reflect what I view as a bit of a narrow PaaS-centric view by many of the PaaS providers who fail to realize that there is more than one approach to managing applications rather than just putting a container abstraction on top of my apps.

What is the difference between PaaS (abstraction) and orchestration (automation)?

Both PaaS and orchestration aim to solve the complexity challenge of deploying and managing apps. Having said that there there is a fundamental difference between the two approaches, let me explain.

PaaS:

Takes an abstraction approach, with abstraction we’re basically hiding complexity by exposing a simpler interface. That approach also comes with an opinionated architecture i.e. in order to provide a simple interface, applications need to be built and written in a certain way that fits the assumptions behind the design of that platform.

In the case of PaaS you don’t have much control over many of the operational aspects associated with managing your application, for example the way it handles scaling, high availability, performance, monitoring, logging, updates. There is also a much stronger dependency on the platform provider in the choice of language and stack. Of course, some of these are open source and provide a range of plug-ins that allow some degree of extensibility, but at the end of the day you still have to make sure that this fits with the core design of the platform.

The main advantage with this approach is that as long as the app fits into the PaaS design principles, you do get a simple way to deploy applications without worrying about the operational aspects. It’s also much simpler to guarantee the behavior of your applications once they have been deployed.

Automation/Orchestration:

With automation we’re basically taking the same steps that we would have performed manually, and scripting them. By scripting them, we’re achieving a similar outcome, i.e. we can run a complex processes such as application deployment in one command, however the fact that the end result may be similar doesn’t make the two approaches the same as is often argued, let me explain. Kind of like the end doesn’t justify the means, but more to the effect of - the end doesn’t necessarily account for the means.

With automation we run a script, and as such we can actually read the script, and quite often understand the underlying steps that will be executed when we run it.  As those steps will often follow the same steps that we would do ourselves, it’s also easier to follow up on these steps and retrace them for troubleshooting purposes. All this is fine, but that’s not the main difference. A script is something that can be shared, cloned, modified or rewritten completely so the degree of control and flexibility is significantly higher than with that of a PaaS/abstraction approach.

That flexibility also comes with a cost. With automation / scripting it’s much harder to guarantee portability and the behavior of an application, as it often relies on many external dependencies that can break at any given point in time. So in the end, we may still end up with too much complexity. (Unfortunately, the usual tradeoff for flexibility is complexity when it comes to technology).

So to put it in @nukemberg’s  terms : The difference between PaaS and Automation/Orchestration can be summed up as Magic vs Black Magic.

Adding containers to the rescue

This is where the combination of containers and automation becomes handy. Containers allow us to strike a better balance when it comes to the degree of control vs. simplicity. Containers allow us to reduce the complexity, which is a result of the number of moving parts and dependencies. The right balance in this way is to use automation mostly to handle the dependencies between services and tiers of applications, handle policies such as scaling and failover, and less so how to install software and configure it on RHL, Ubuntu, Windows or whatever environment.

Putting PaaS, orchestration/automation and containers together

Both PaaS and orchestration/automation and containers shouldn’t be viewed as alternative to one another but rather as complementary stacks, in a very similar way to the way Google and Amazon have approached the same challenge.  The diagram below is taken from my previous post on Docker vs. PaaS that outlines how each of the layers are ordered in this new stack.

It is important to note that the PaaS box in the diagram refers to the more traditional PaaS implementations i.e. Elastic Beanstalk, Heroku, GAE and such. Both Pivotal/CloudFoundry and Red Hat/OpenShift are building new PaaS versions that will expand into orchestration/containers to support more advanced orchestration and container support.

The diagram above illustrates how I believe the new application deployment platform stack would shape up when we add orchestration/automation and containers to the mix. This layered approach enables users to choose which layer of the stack they want to use based on their specific use case. E.g. they can choose a PaaS for use cases where they just want to deploy simple apps and not worry about how their apps are managed, or use automation/orchestration if they want to have more tight control over the operational aspects of their apps. Whether this new stack will be packaged under the same platform is less important for the sake of this discussion.

Why is this still disruptive?

So if we can put PaaS, automation/orchestration and containers together why is this still disruptive?

I think that the answer to that is based on the fact that with the combination of containers, this now allows us to remove a fairly big chunk of the complexity for automating our application deployments. By doing this, the difference in terms of complexity between the use of PaaS/abstraction or the automation/orchestration approach to deploy even a simple application has narrowed significantly.

I, therefore, think that given those two options, most users would prefer to use an approach that is simple enough but does leave them with a higher degree of control. Because of all this, I expect that we will see a much wider and broader adoption of containers/orchestration to manage apps, rather than PaaS.

Do I Need PaaS if I Use Docker?

In my recent post on OpenStack  “Do I Need OpenStack if I Use Docker?” I covered the confusion that has emerged around one area of disruption that Docker has created. In this post, I’d like to segue into another similar topic that has emerged around the same concept where I sense similar disruption and confusion: the use of PaaS in its traditional form vs using a straight Docker approach.

Background

PaaS in its simplest form is a platform that enables developers to develop and deploy web applications into production fast by abstracting many details of the underlying infrastructure, whereas Docker provides a generic container for packaging software stacks in a portable way.

A Traditional PaaS Stack

Many of the traditional PaaS implementations use Docker or LXC as the underlying container to enable multi-tenant applications on a shared infrastructure. Usually PaaS relies on web containers that are specific to a certain language (Tomcat, Jetty/Java, modephp/PHP, Rails/Ruby, Django/Python,NodeJS/JavaScript…..) to host the user’s code, and Docker or LXC containers as lightweight VMs that hold these web containers, and provides the required isolation level between the various web containers. This architecture lends itself nicely into simple web applications which are often built out of a web front-end, and a database as a backend persistent model. As far as I know, Docker actually initially emerged from one of the PaaS’ provided by DotCloud, and evolved into its current incarnation.

The introduction of Docker as a simple abstraction to containers, has turned Docker into a portable software container that isn’t tied to a specific language or framework. It can not only be used as a web container, but also as a more general purpose software container for all sorts of applications such as microservices, big data analytics, stream processing, as well as legacy applications.

Some PaaS implementations, such as CloudFoundry, have already started to offer support for running Docker containers as one of the software components that are supported by the platform.

Having said that, PaaS provides a fairly opinionated model for the way we should model and package our applications. Often times, our packaging is too limiting as we start to grow to more scalable models, such as microservices.

This causes many users today to question whether there is enough added value in PaaS in order to justify these limitations, when users can simply run their software directly on Docker.

Using Docker as an Alternative to PaaS

Using Docker with a combination of orchestrations engines such as Kubernetes (in the case of GCE), Heat in the case of OpenStack, or Cloudify (for multiple clouds such as OpenStack, VMware, AWS, as well as bare metal)

can provide a relatively simple way to package, deploy and manage applications in an automated fashion.

Unlike the traditional PaaS stack - it doesn’t force a specific opinionated architecture for deploying apps although it does lend itself very nicely into both web applications and microservices architecture.

My Take

The main promise of PaaS is in the speed of development and simplicity. Traditional PaaS implementations achieve that goal through extreme abstraction of the underlying infrastructure, as well as by promoting an opinionated architecture, which provides a fairly optimized and consistent deployment experience for applications that fit into that model. The combination of the two ultimately does help achieve this promise, but for a fairly limited set of applications.

Using Docker coupled with orchestration as an alternative provides a more general purpose platform for achieving a similar goal. Unlike the traditional PaaS approach, this use of Docker with an orchestration engine does expose the user to more knobs and infrastructure details, which makes the user experience for developing and deploying simple web apps more complex than with the PaaS alternative, however it does provide the added benefit of flexibility - like with the onset of OpenStack, and open source cloud in general..

The right way to think of PaaS and Docker

In my opinion, the right way to think of the two is in a layered approach in which both the PaaS containers and Docker fit together as outlined in the diagram below.

As seen in the diagram above,  PaaS becomes one use case for deploying simple web apps. For more complex application deployments, it makes more sense to use Docker directly. In this context orchestration becomes a central piece for managing both use cases. Orchestration also provides a layer of abstraction between the application components and the underlying infrastructure.

The important realization is that in this context the role of PaaS as we know it becomes more of a niche role, yet still remains a fairly common and important use case. It is also important to note that the two models are not mutually exclusive. An application that runs on a PaaS container could interact with services that are managed directly through a Docker container, just as you would use other cloud services such as database as a service, load balancer as a service or any other on-demand resource.

Another important note is that the combination of Docker and orchestration opens a new path to manage dynamic workloads not just on an IaaS environment, but also directly on bare metal. I’m starting to see more use cases where this option becomes more popular, specifically for managing dev and testing environments where the use of a full blown cloud infrastructure may just  be overkill.

Final Note

When we started the journey into the cloud world a few years ago, the underlying cloud infrastructure was very complicated. As a result, the only way possible to simplify the application deployment experience was to take extreme measures in the level of abstraction, as well as by forcing an opinionated architecture to ensure that the application fits into typical cloud architecture. As the underlying infrastructure and tooling evolves,  those extreme measures are no longer needed for all use cases. What we’re now seeing is basically a shift of of pieces in the PaaS architecture that was previously embedded within the "PaaS Box" such as containers and orchestration that have becomes more generic, along with independent services that have their own right to exist.

The good news in all this, is that as users we are finally beginning to receive the simplicity of PaaS, that was previously limited only to greenfield web applications, for use with a  much broader spectrum of applications and architecture styles.

So to answer the question from the opening note, do i need PaaS if i use Docker? The answer is, yes. It makes sense to have the two combined, but the role of PaaS as we know it has narrowed down significantly.

The VMware OpenStack Cloud Conundrum

OpenStack is an open source cloud infrastructure that is considered by many as a cost-effective alternative to VMware.

In reality, the transition from VMware to OpenStack isn’t that trivial, which leads most enterprises to take a hybrid cloud approach. This means they run their OpenStack infrastructure alongside their existing VMware infrastructure with an aim to gradually transition workloads into their new OpenStack environment.

Recently, VMware announced their own VMware Integrated OpenStack, which is a VMware-supported OpenStack distribution providing tighter integration between existing VMware environments and OpenStack.

In this post I will discuss three of the options for putting OpenStack and VMware together and weigh what I believe are the pros and cons of each approach.

1. Using OpenStack with VMware Hypervisor plug-in

OpenStack Nova comes with a pluggable architecture for integrating various hypervisors. It supports both KVM, VMware as well as Hyper-V.

The first option for integrating VMware into OpenStack is through the ESXDriver for Nova.

In this option the Nova scheduler can spawn VMware ESX VM’s through an ESX enabled node (as opposed to KVM which runs directly from the Nova compute node).

Pros:

In this approach, we can potentially re-use our VMware images assets and easily import them into our OpenStack environment.

Cons:

1. Limited use of VMware features:  ESXDriver cannot use many of the vSphere platform advanced capabilities, namely vMotion, high availability, and Dynamic Resource Scheduler (DRS).

2. Limited Portability: There are some features of VMware, such as vMotion, that many enterprises rely on today and would need to be turned off as we move to OpenStack. As some of the images were built with the assumption that this unique VMware features exists, it wouldn’t be possible to transition them into an OpenStack environment and expect them to work.

In summary, while this option makes sense, the cost and technical limitations make it less popular. In fact, a recent OpenStack survey report indicates a fairly small percentage of users who actually use this feature.

Using VMware vSphere with OpenStack

In this approach we utilize the compute resources using vSphere. This will allow us to take advantage of all the VMware features that come with vSphere and overcome the limitations mentioned above.

In this case, the entire vCenter ESX cluster appears as a one big hypervisor. The actual allocation of the ESX hosts is done through VMware vCenter and is not exposed to the Nova Controller, as outlined in the diagram below:

Similarly, we will plug-in the VMware Storage and Network services to allow for even more complete integration across the compute, network and storage stack as outlined in the diagram below:

You can learn more about how this integration works in the OpenStack reference guide.

Pros:

The main advantage of this approach is that it enables users of VMware to benefit from both worlds; on one hand, they can use OpenStack as an open API and on the other hand they can utilize their existing VMware infrastructure.

Cons:

The main disadvantage of this approach is that it creates a completely different OpenStack implementation, which has some serious differences in its implementation and behavior from the original open source version of OpenStack - specifically in the way the compute nodes are managed.

The Elephant in the Room:

One of the main motivations to transition to OpenStack in the first place is to cut costs.

In both options we rely on the VMware stack, and therefore the actual savings are still unknown.

3. Using a Common Management and Orchestration as an abstraction to both VMware and OpenStack

In the third option we will not use any of the OpenStack VMware plug-ins, but instead we will use an orchestration layer as higher level abstraction between OpenStack and our VMware environment.

The orchestration layer provides a common management and deployment infrastructure. In this approach we will not be trying to force the VMware infrastructure to fit into the OpenStack API, but instead we will just map the different calls to either OpenStack or VMware into the appropriate type. In this way, the application is kept aware of whether it is running on OpenStack or VMware. However, since the calls to each of the infrastructure components are now centralized into one driver per environment, it is managed once for all the applications. Additionally, there is a default implementation for the built-in types, so in most cases the user will need to deal with the details of implementations of each element type only for specific customization.

Pros:

1. We are able to utilize all of the features and capabilities of each infrastructure with no limits.
2. We reduce dependency risk - With this abstraction we’re less vulnerable to a specific infrastructure and we keep our options open to move or add new environments as needed.
3. Support for vSphere and vCloud -Since we are not limited to the use of a specific API, we can integrate with both vSphere and vCloud API.

Cons

1. We are shifting the dependency on the management and orchestration layer.
2. We may lose some of the management and orchestration capabilities that are specific to each of the environments.
3. Additional customization effort: Since we’re not relying on a common API, we may need to customize the built-in types per environment to fit our specific needs. Having said that, it is important to note that this customization is only done once for all of our applications. Additionally, it is expected that over time the default types will cover most of the use cases. Therefore, the need for customization will be minimized.

My Take

Given that the cloud infrastructure world keeps on changing and evolving very rapidly (not just between OpenStack and VMware), any tight integration approach will have a higher chance of breaking compatibility or being limited to the least common denominator at some point. We also need to be aware that even though in the first two options we remain compatible with the OpenStack API, we still end up with different OpenStack implementations from a behavior perspective.

On top of that, we need to be prepared for new disruptions. This is actually taking place right now, for example, with Docker orchestration that continues to disrupt and challenge the way we handle our compute and network infrastructure.

With this in mind it would be too risky not to keep our options open.

Having an abstraction layer at a higher level of the stack gives us the benefit of being less vulnerable to changes at the lower level infrastructure. It also provides us the flexibility to adopt new infrastructure changes in the future.

Having said all this, the question “Are we minimizing or just shifting the risk?” still lingers.

This is where TOSCA comes in handy. TOSCA provides a standard way to describe our application blueprint. This significantly reduces our dependency on a specific implementation of the orchestration and management layer and this was one of the main reasons that led us to choose TOSCA when we designed the third generation of our orchestration with Cloudify.

References

• OpenStack VMware integration

• http://www.slideshare.net/danwent/hk-2013-v-mware-session?related=1

• OpenStack Hypervisor support Matrix

• OPENSTACK COMPUTE FOR VSPHERE ADMINS (highly recommended and the source for many of the diagrams in this article)

• Cloudify - Tosca Based Orchestration

Do I need OpenStack if I use Docker?

 

Docker has broken a record in the speed in which it moved from being a disruptive technology to a commodity. The speed of adoption and popularity of Docker brings with it lots of confusion.

In this post I wanted to focus on a trend of commentary that has been gaining popularity, that I’ve started to hear more often recently from users who just started using Docker.  Whether it makes sense to use OpenStack if they’ve already chosen to use Docker.

Before I give my take on this, I wanted to start with a short background for the rationale behind this question.

Background

In its simplest form, Docker provides a container for managing software workloads on shared infrastructure, all while keeping them isolated from one another. Virtual machines such as KVM do a similar job by creating a complete operating system stack of all the OS devices (through a Hypervisor). However, unlike the virtual machine approach, Docker relies on a built-in feature of the Linux operating system named LXC (Linux Containers). LXC utilizes the built-in operating system features of process isolation for memory, and to a lesser degree, CPU and networking resources. Docker images do not require a complete boot of a new operating system, and as a result, provide a much lighter alternative for packaging and running applications on shared compute resources. In addition, it allows direct access to the device drivers which makes I/O operations faster than with a hypervisor approach. The latter makes it possible to use Docker directly on bare metal which, often times causes people to ask whether the use of a cloud such as OpenStack is really necessary if they’re already using Docker.

This performance difference between Docker and a hypervisor such as KVM is backed by a recent benchmark done by Boden Russell and presented during the recent DockerCon event.

The benchmark is fairly detailed, and as expected, it shows a significant difference between the time it takes to boot a KVM hypervisor to a Docker container. It also indicates a fairly big difference in the memory and CPU utilization between the two, as can be seen in the diagram below.

This difference in performance can map to a difference in density and overall utilization difference between the two in similar proportion. This difference can easily map into a big difference in cost that is directly affected by the number of resources needed to run a given workload.

My Take:

• This question has nothing specific to do with OpenStack and can be applied similarly to any other cloud infrastructure. The reason it is often brought up in the context of OpenStack, in my opinion, is due to the fact that OpenStack is fairly popular in private cloud environments which is the only environment in which we can even consider a pure Docker alternative.

• It's about the hypervisor stupid:

Many of the performance benchmarks compare Docker versus KVM, and have little to do with OpenStack. In fact, this specific benchmark of both KVM images and Docker containers was running through OpenStack which shows that the two technologies works nicely together. In that context, most of the utilization  arguments become irrelevant when I choose to run OpenStack on top of a Docker based Nova stack as illustrated in the diagram below taken from the OpenStack documentation.

• Cloud infrastructure provides a complete data center management solution in which containers or hypervisors, for that matter, are only part of a much bigger system.  Cloud infrastructure such as OpenStack includes multi-tenant security and isolation, management and monitoring, storage and networking and more. All of those services are needed for any cloud / data center management and have little dependency on whether Docker or KVM are being used.

• Docker isn't (yet) a full-featured VM and has some serious limitations like security, Windows support (as indicated in the following email thread in the Red Hat mailing list), and therefore cannot be considered a complete alternative to KVM just yet. While there’s ongoing work to bridge those gaps, it is safe to assume that adding the missing functionality may come at an additional  performance cost.

• There’s a big difference between raw hypervisor performance/containerization and application performance, as indicated in the following graphs from the benchmark results. A possible explanation for that is that applications often use the caching technique to reduce the I/O overhead.

• If we package Docker containers within a KVM image the difference can become negligible. This architecture often uses hypervisors for managing the cloud compute resources, and an orchestration layer such as Heat, Cloudify, or Kubernetes on top to manage containers within the hypervisor resources.

Conclusion

This brings me to the conclusion that the right way to look at OpenStack, KVM and Docker, is as a complementary stack in which OpenStack plays the role of the overall data center management. KVM as the multi-tenant compute resource management, and Docker containers as the application deployment package. 

In this context a common model would be to use Docker for the following roles:

• Docker provides deterministic software packaging and fits nicely with the immutable infrastructure model.

• Docker for excellent great containerization of microservices pods

• Using Docker on top of OpenStack as well as a bare metal environment

Having said all of the above, I do see cases mostly for well defined workloads, where the use of cloud infrastructure isn't mandatory. For example, if I were to consider the automation of a small shop development and testing environment for DevOps purposes, I would consider using Docker directly on a bare metal environment.

Orchestration can be a great abstraction tool between the two environments

One of the benefits of using an orchestration framework with Docker, is that it can allow us to switch between OpenStack or bare metal environments at any given point in time. In this way, we can choose either options just by pointing our orchestration engine to the target environment of choice. OpenStack Orchestration (Heat) declared support for orchestrating Docker starting from the Icehouse release. Cloudify is an open source TOSCA based orchestration that works on OpenStack and other clouds such VMware, AWS and bare metal, and recently included Docker orchestration .  Google Kubernetes is associated mostly with GCE but could be customized to work with other cloud or environments.

References:

• Using Docker as and OpenStack compute resource (Nova)

• Orchestrating Docker containers through OpenStack Heat

• Orchestrating Docker containers on OpenStack, VMware, AWS, bare metal and Other clouds through TOSCA/Cloudify

• Docker limitations as indicated in Red Hat mailing list

• The boring truth: full virtualization and containerization both have their place

• Using Docker as immutable infrastructure

• Docker drops LXC as default execution environment

Our Open Source Cloud (Re) Architecture with OpenStack + TOSCA

Cloudify OpenStack Native approach - The Story behind the Scenes

In the previous post I outlined what I mean by native-approach to the OpenStack cloud. In a nutshell, a native approach represents out of the box tighter integration with OpenStack, all without being limited only to OpenStack.

In this post I want to share some of the stories behind the design of Cloudify 3.0 and the approach that we have taken to make Cloudify *native* to OpenStack.  I would first start by outlining our motivation behind this approach.

OpenStack-only or Not-only-OpenStack?

It is our core belief that for the foreseeable future supporting hybrid cloud is going to be important for many of the OpenStack users for the following reasons:

1. Transitioning to OpenStack is a long journey - Many enterprises have VMware-based stacks as their core infrastructure. Where many of these organizations have often started their journey toward OpenStack by creating a dev environment, and then gradually moving more workloads into their OpenStack environment as they get more comfortable with it. This process usually spans over months and possibly even years. It became apparent to us, that during this time these customers would still be using VSphere and VCloud in parallel to OpenStack. For such users, having a common platform to manage their app deployment across two environments can smoothen the transition and also reduce the risk of getting stacked if one of the environments doesn’t meet their needs.
2. The public cloud market is going to be dominated by non-OpenStack clouds - while OpenStack is on a trajectory to dominate the private cloud market, the public cloud market looks quite different with AWS as a strong and long-standing leader and other major clouds emerging such as GCE and Azure - hoping to close in. Many organizations wouldn’t want to host their entire data center on a private cloud, and would therefore need to take a hybrid cloud approach. In order to support hybrid cloud, we had to include an abstraction layer that will allow us to integrate with clouds other than OpenStack.

At the same time we wanted to have a more intimate integration with OpenStack in order to leverage the fact that it is open source and allows for a deeper integration with its core services. Through that we were able to enable smooth and simple integration of Cloudify into OpenStack.

Extending or Rewriting?

Cloudify 2.x already took a step toward deeper integration with OpenStack and we were one of the first to announce our support for OpenStack. Recently, we also added support for OpenStack Neutron.

We realized that if we want to make Cloudify fit natively into the OpenStack infrastructure it is not enough to have integration with the OpenStack components.  We also need to ensure that the design behind Cloudify will be consistent with the way other projects and services of OpenStack are implemented. This led us to the conclusion that we wouldn’t be able to be a first class citizen with OpenStack unless we underwent a major redesign of Cloudify.

Going Python?

Our first step in this direction was to move from Java to Python. Moving to Python wasn’t as painful as you would think for us, because many of our developers already knew the language. This decision quickly proved itself, as it became much simpler not just to develop the new version of Cloudify faster, but it also enabled us to join other OpenStack projects as well as reuse some of the same components and frameworks that are already used by OpenStack.

Integrating with Core OpenStack Services

Many of the products that declare support for OpenStack mostly integrate with the compute API (Nova), and quite often come with their own security, messaging, configuration, and other such considerations. We felt that this is not enough, and doesn’t fit in with the way other services are built within OpenStack. Hence, we decided to deepen our integration with other services such as Keystone, Neutron, Heat and later with Ceilometer, Mistral and others.

Going TOSCA?

Cloudify 2.x was designed around a proprietary Groovy-based domain specific language (DSL) for defining application topology and configuration. It became apparent that the DSL had become a central piece of Cloudify architecture. As a result, maintaining a proprietary DSL would only serve as a barrier going forward. TOSCA (Topology Orchestration Specification for Cloud Applications led by the Oasis Foundation) defines a standard templating language. TOSCA was similar and richer in concept to our previous 2.x specification. Because of this, using TOSCA seemed like a natural evolution.

At the time when we decided to use TOSCA, the specification was provided in a fairly complex XML. Our first step for adopting TOSCA was to come with a simplified YAML based version which better fits into the spirit of OpenStack. After the OpenStack summit in Hong Kong, when we first introduced the idea, it became apparent that members from the Oasis group were already thinking along this line.We decided to join forces and join the Oasis organization. We started working with the team to incorporate TOSCA as an official part of OpenStack. During the Atlanta summit, it was decided to integrate the TOSCA project into the Heat project and now there is an official definition of the TOSCA 2.0 specification based on YAML. Our goal is to use the TOSCA 2.0 specification as the official templating language for Cloudify instead of our current TOSCA like DSL.

Integrating with OpenStack Heat

OpenStack is growing, and with this the services that are included as part of the core OpenStack project continue to grow up the stack. One of these services is OpenStack Heat, which started as the equivalent of Amazon Cloud Formation, and is becoming more of a general purpose infrastructure orchestration these days.

We realized that since Heat is closely integrated with OpenStack it will follow the OpenStack release cycles and API, and therefore would provide a useful tool for setting up the OpenStack infrastructure. The approach that we have taken with Heat is an “overlay approach”. This means that users can use Heat as they do today to set up their infrastructure. Then, Cloudify integrates with Heat in a way that will allow it to discover any resource that was provisioned through Heat, and ultimately add the monitoring, logging and software stack on top of that environment. 

Integrating with Neutron - Network Orchestration

Networking becomes a core service in any cloud deployment. Networking refers to an element such as security-group, private IP, floating IP as well as routers, DNS, vLans, load balancers, and such.

Cloudify 2.7 included basic integration with Neutron that allows users to attach floating IPs and set the availability zone configuration.

With Cloduify 3.0, we included support for all the networking elements and can now create vLans, security groups and more, as part of the application deployment.

Design for Scale

Application orchestration often requires intimate and continuous integration with the application so that it can detect failure in sub-seconds and take corrective actions as needed. This often imposes a more complex scalability challenge.

Cloudify 2.x uses a hierarchy of managers, where each manager controls 100+ nodes as a scaling architecture. One of the things that we have experienced over the last year is that there is a growing class of services such as  Big Data and NFV. With these services, a single service can be comprised out of more than 100s and potentially even 1000s of instances. This is why the manager of managers approach didn’t fit well to these use cases.

To enable scaling of 1000s of nodes per manager, we decided to use a message broker approach based on AMQP as well as to separate the provisioning, logging and real-time monitoring tasks into separate services that can scale independently. This allows us to also control the level of intimacy between the Cloudify manager and the application services. As an example, we can tune Cloudify to handle the provisioning and logging only, and use the real-time information that has already been gathered by the infrastructure. This is especially important in the context of OpenStack, as we expect that a large part of the metrics can be gathered through the infrastructure itself.

Integrating your DevOps toolchain of choice

One thing that has become clear through the various OpenStack statistics based on their annual survey, is that many of the OpenStack users are using a toolchain that is comprised of tools available through OpenStack infrastructure, as well as tools that are not specific to OpenStack such as Docker, Chef, Puppet, Ansible, JClouds and others.

Integrating all these tools as part of a deployment system becomes a fairly complex task that can takes weeks and even months.

We realized that if we want to provide a full lifecycle management of the application, it wouldn’t be right to force a particular stack or toolchain on a user, but rather provide an open pluggable architecture that could enable you to easily integrate your choice of tools into the same deployment.

With Cloudify 2.x we included support for Chef, Puppet and also had a cloud driver that allows us to integrate with various cloud infrastructures, as well as with tools like JClouds.

With Cloudify 3.x we created a more generic plugin architecture that allows users to plug in almost any element of their application, starting from the cloud plugin to configuration management, orchestration, monitoring etc.

To make this simple we included a Bash plugin that provides a simple framework for integration with external tools, just by pointing the plugin to the relevant Bash script.

Final Notes

In this post I thought sharing the story behind the scenes that we went through to make Cloudify OpenStack native, and the rationale and decision process behind them, would be relevant to other users that are considering their OpenStack strategy.  Especially for those still deciding how deep they should integrate with OpenStack.

The good news is that, it is quite possible to integrate natively with OpenStack without limiting yourself only to OpenStack. Having said that, the initial investment required to get there is quite big. I hope that this lesson from our own experience will save others time.

Going Hybrid Cloud with OpenStack

OpenStack Silicon Valley is just a few short weeks away and we are looking forward to talking OpenStack with the local community.

A main focus for Team Cloudify at the event will be around the topic of portability AKA hybrid cloud, not only between environments, but also between OpenStack and non-OpenStack environments. In July we announced the release of Cloudify 3, a complete rewrite and redesign of Cloudify.  This change provides an intelligent orchestration and management platform that is even more compatible with the open source world of OpenStack. The platform is designed to integrate with any tool chain and provide interoperability across stacks and environments.

Why the focus on portability? It is our core belief that for the foreseeable future, supporting hybrid clouds is going to be important for many OpenStack users. Here are a couple of reasons why:

1. Transitioning to OpenStack is a long journey - Many enterprises are based on VMware as their core infrastructure. Often, those organizations begin their journey to OpenStack by creating a dev environment and gradually moving more of their workload onto OpenStack as they become more accustomed to the new environment. This process typically spans the course of months and sometimes years. It is clear that during this period of transition, customers will continue to use vSphere and vCloud in parallel with OpenStack. For those users, having a common platform with which to manage their app deployment across those two environments can ease the transition and reduce the risk of hitting a roadblock if one of the environments does not meet their needs.

1. The public cloud market is going to be dominated by non-OpenStack clouds - While OpenStack is on a trajectory to dominate the private cloud market, the public cloud market is led by AWS with other major clouds emerging, such as GCE and Azure. Many organizations would not want to host their entire data center on a private cloud and would, therefore, need to take a hybrid cloud approach. In order to support hybrid cloud environments, an abstraction layer is needed to enable integration with clouds other than OpenStack.

While we strongly believe in the necessity of supporting hybrid deployments, the integration with OpenStack was a major consideration when redesigning the Cloudify platform. By switching to Python code, developing compatibility with Heat and supporting the TOSCA specification, Cloudify is now more than ever more deeply integrated with OpenStack’s core services.

This deeper integration with OpenStack technology makes Cloudify even more equipped to handle the task of multi-cloud deployment and management between OpenStack and not-only-OpenStack environments.

Open Cloud Deconstructed. What does it mean to be OpenStack Native?

OpenStack is just not like any other cloud.

OpenStack is an open source cloud and that makes it quite unique in comparison to many of the closed source public clouds generally available today, the most common being AWS, Azure GCE, and others.

The fact that it is open source, allows us to integrate with the lower level of the stack and know exactly how each element of the infrastructure is designed and implemented.

As with any open source project we have the option to contribute and influence how the OpenStack cloud ultimately shapes out.

Given the above background, when we're designing an integration with OpenStack, we should approach it differently than we approach other clouds. A more native integration with OpenStack involves two main angles:

• Integration with OpenStack core and infrastructure services, which usually includes this full OpenStack component list - as well as those that are continually being developed and added with every release.

• Follow the OpenStack design principles - OpenStack is written in  Python and uses a certain stack for messaging, database and such . To be OpenStack native you should align yourself with the OpenStack design principles as closely as possible.

Current options to be a part of the OpenStack ecosystem

In addition to the general guidelines outlined above, there are couple of additional options to be part of OpenStack. These options mostly fit in with those looking to take an OpenStack *only* approach, and require heavy involvement in the OpenStack community project.

• OpenStack Core - be a part of the OpenStack distribution

• OpenStack StackForge is an incubation framework that allows you to add new projects into the OpenStack ecosystem. StackForge uses the same set of tools and frameworks that are used to develop the core OpenStack services, and as such it allows smoother transition from an incubation project into the core services. That makes this option more relevant for projects that are likely to be included as an OpenStack core service once they've successfully passed the incubation stages.

• OpenStack Marketplace - The marketplace is a web directory that lists the various ecosystem players and their OpenStack solutions. Currently the marketplace is focused on OpenStack distros, training, consulting and drivers, and doesn’t cover solutions on top of OpenStack.

What’s missing?

While there is a fairly clear process, and tool chain for handling core and incubated services on OpenStack, these options fit mostly with services that are primarily designed to only run on OpenStack. What’s missing is a clear certification process that will allow users to find solutions in the ecosystem, outside of OpenStack, and find out quickly what their level of integration and support for OpenStack is. This should include information such as version support, and platform the solution was certified on, which of the OpenStack services are integrated, how much of the stack of the solution is compatible with that of OpenStack’s, and other such important information. A good and simple starting point could be the OpenStack marketplace, however this is just the first step. What’s needed is a set of frameworks similar to StackForge but much more permissive that will provide a means for validating the compatibility with OpenStack.

Why should you care?

As with any popular framework there are many tools that claim support for OpenStack, however in many cases the integration mostly includes basic integration with the OpenStack compute API (Nova). A good example for this is Cloud Foundry, or other tools such as Chef, Puppet, Docker, just to name a few. While these tools do have support for OpenStack, the level of integration can vary quite dramatically. Quite often, each of these  solutions comes with a parallel stack for handling security, monitoring and other issues, which isn’t compatible with the OpenStack infrastructure. As an end user, it is important that to be able to find out quickly which of these products comes with basic support for OpenStack, and which one takes a more native approach to OpenStack. There are many implications to the level of complexity and also to the level vulnerability of those services based on the support level.

OpenStack should attract more ecosystem projects rather than competing with them

In my opinion, there has been too much focus developing more alternative projects for the OpenStack project in comparison to the focus that has been placed on projects that exist within the broader ecosystem. The main reason for this, is the lack of native support for OpenStack. A good example of this, is project Solum and its ecosystem equivalents Cloud Foundry and OpenShift. 

I think that it’s time that OpenStack realign its focus from growing all around the stack, to supporting and attracting more ecosystem projects, that focus on adding native support to OpenStack. In order to do this, there needs to be more emphasis on creating certification, compatibility and testing projects. Additionally, the marketplace initiative needs to receive more attention and investment in order to make it more than a web-based directory service, similar to the AWS marketplace.

I would like to propose a discussion on that subject during the next summit in Paris. Do you think this is a good or bad idea? I’d appreciate it if you could share your thoughts.

OpenStack by the Numbers

One thing that I like about working with an open source community like OpenStack is that you get direct access not just to the source code itself, but to its users and ecosystem as well.  This allows you to be less exposed to marketing spins, and actually analyze our actions based on real numbers.

The OpenStack Foundation conducts a very insightful survey twice a year which helps to measure how users are using the technology. This is an important and useful feedback loop that goes to the PTL of the various projects, and can help product companies to analyze their go to market plans and such. You can find the source

I found the original version a bit too verbose, so I took the liberty to put together an executive summary of the main points from the survey, mostly targeted at decision makers. I hope that you’ll find it useful, any feedback would be highly appreciated.

Main Reasons for Choosing OpenStack

The main reason cited for choosing OpenStack is Open Technology and Avoiding Lock-in.

Interestingly enough Cost Saving comes only 4th!

 

Size of Deployments

The large majority of the current deployments are under 100 nodes.

Around 30% of the deployments run relatively large scale deployments between 500-1000.

QA/DEV	Production

Production seems to follow a similar trend but lower in numbers to QA, which is reasonable as I believe that some of the QA deployments represent a transition stage to production.

 

User Type

The majority of the users seem to be service providers and cloud operators.

There is also a good number of ISVs and ecosystem partners, which is a strong indication that OpenStack is indeed an ecosystem.

There's a very strong presence of OpenStack in the Telco industry. Finance seem to be lagging behind the adoption curve with only three financial organizations declaring their use of OpenStack.

The majority of the deployments run on private or hosted private clouds. About 20% run on public or hybrid clouds. OpenStack also seems to attract mostly small to mid-size companies.

 

Private/Public Clouds	Size of Organizations

 

Deployment Tools

The survey includes two categories for deployment tools, one for those that are used for deploying the OpenStack infrastructure, and the other that are used for application configuration (covered in the application survey).

According to the two surveys users tend to use different sets of tools for the infrastructure deployment and for application configuration management. Puppet seems to be the most popular choice for OpenStack users in both categories. 

One of the interesting results is to see that Bare Metal and Docker are starting to appear in more deployments. This is fairly impressive given that the two are not yet officially supported by OpenStack.  VMware is around 10%-13% of KVM of Virtualization similar to XEN.

 

OpenStack Deployment Tools

 

Applications on top of OpenStack

As noted above, Puppet is popular for application configuration, as well as infrastructure deployment. Heat and Docker take the 2nd and 3rd place respectively,  putting Chef behind in the 4th place.

Given the popularity of Heat during the last summit I wouldn’t be surprised if Heat and Docker will surpass the use of Puppet in this category.

 

 

Popular Frameworks

Interestingly enough, JClouds seems to be gaining popularity amongst OpenStack users. This may be an indication that ~ ¼ of the users are interested in frameworks that are not tied only OpenStack. Not surprisingly, Python is far more popular than any other language, however Java seems to have a good and dominant presence more than other frameworks. One possible explanation is that the type of users that are attracted to OpenStack are more of the traditional enterprise organizations, and less of the new web-facing companies.

Operating Systems

Ubuntu and CentOS are the most popular choice. Windows adoption amongst OpenStack users is between 10-20%.

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Application Survey	Production Survey

 

Compatibility / Portability

EC2 is a relatively popular choice which indicates that there is an increasing desire among the OpenStack community to maintain portability with EC2. This also correlates with the popularity of portability frameworks such as JClouds.

 

QA/DevTest	Production Survey

 

References

• OpenStack user survey presentation
• OpenStack user survey online session from OSS Atlanta 2014