Nati Shalom's Blog

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.

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

The Heat is on with OpenStack Icehouse

OpenStack Orchestration Moves up the Stack

OpenStack Icehouse, the Ninth OpenStack Release, was announced at at the last summit and was named for a street in Hong Kong. The Icehouse release is primarily focused on stability and maturity. The release marks another milestone in the growth of the project.  Just a few stats.

Icehouse had:

• 1,202 contributors, a 32 percent increase from the Havana release six months ago
• Approximately 350 new features added.
• 2,902 bug fixes were added in the Icehouse release cycle.

In this post  I’m going to focus on the updates in the OpenStack Heat project, which is a.k.a OpenStack Orchestration.  For a general overview on the Icehouse release I would recommend checking out the OpenStack Icehouse release notes or this article by InfoQ.

Heat Project Icehouse - Main Updates

The Heat project started as a clone of Amazon CloudFormation which is basically a declarative approach to automate the infrastructure set up on Amazon environments.  The Icehouse release marks an evolution of the project up the stack. If in previous releases Heat was mainly used to orchestrate the OpenStack infrastructure, with Icehouse it has added many more capabilities to handle orchestration of the software stack as part of the application boot process.

This shift is reflected in the new HOT template format  which allows you to describe a broader aspect of an application configuration including the new OS::Heat::ScalingPolicy. With the Icehouse release these HOT Templates have become the recommended approach. In addition, there is an ongoing effort to introduce TOSCA (Topology and Orchestration for Cloud Applications) into Heat, which is an OASIS-led standard. A new TOSCA translator project for Heat is now part of StackForge. A preview of the TOSCA simple profile can be found here supported by IBM, GigaSpaces, Huawei, Vnomic a preview version of a TOSCA-based Orchestration project for OpenStack is available with the new Cloudify 3.0 project.

Other updates include new the Trust-based security model that will allow the delegation of user roles and permissions without passing Keystone tokens or credentials. For more information on the new Heat Trust Model see the Steve Hardy post Heat auth model updates - part 1 Trust. In addition, the Icehouse release includes improvements to scalability by allowing the distribution of Heat deployments tasks between multiple Heat engines. 

To learn more about the Heat Project Icehouse updates I’d recommend looking at Steve Baker’sonline presentation

Try Out the Heat Icehouse Release on DevStack

Yoram Weinreb a Lead OpenStack Architect from GigaSpaces wrote a detailed example on how you can setup a Heat Icehouse environment on DevStack. What’s interesting with this example is the addition of Neutron orchestration as part of the simple WordPress example.

To try out the example with Heat Icehouse follow the steps from Yoram’s post.

I will be demonstrating this example at the upcoming OpenStack Design Summit in Atlanta next week in the talk - When Networks Meet Apps.