Solution Demos in 16:9

I posted a few demonstrations two weeks ago. I re-recorded them in 16:9 to work better when projecting on a larger screen. Enjoy.

Accelerating Service Delivery MOV  MP4

Optimizing IT MOV MP4

Scalable Infrastructure MOV MP4

Modernize Development and Operations MOV MP4

Improving Service Design

In order to deliver services faster to their customers organizations need to ensure their development and operations teams work in unison. Over the last weeks and months I’ve been fortunate enough to spend time with several organizations in industries ranging from telecommunications, to financial services, to transportation to gain a better understanding of how they are allowing their development and operations teams to work more smoothly together and to observe where they are struggling. In this post I’ll attempt to summarize the common approaches and articulate the opportunity for improving service design.

Prior to explaining the common approaches that development and operations teams have implemented or plan to implement let’s examine two common ways development and operations work together when it comes to repeatability.

Using Repeatable Deployments
In this approach, a member of the development team (sometimes a developer, but often a system administrator within the development team itself) makes a request for a given service. This service may require approvals and fall under some type of governance, but it is automatically instantiated. Details about the service are then delivered back to the requester. The requester then controls the lifecycle of everything within the service, such as updating and configuring the application server and deploying the application itself.

Using Repeatable Builds
In this approach, a member of the development team (most often a developer) requests a given service. This service is automatically deployed and the requester is provided an endpoint in which to interact with the service and an endpoint (usually a source control system) to use for modification of the service. The requester is able to modify certain aspects of the service (most often the application code itself) and these modifications are automatically propagated to the instantiated service.

There are several patterns that I have observed with regards to the implementation of repeatability.

Repeatable Deployment
First is what I call “Repeatable Deployment”. It is probably familiar to anyone who has been in IT for the last 20 years. System administration teams deploy and configure the infrastructure components. This includes provisioning of the virtual infrastructure, such as virtual switches, storage, and machines and installing the operating system (or using a golden image). When it comes to containers, the system administration teams believe they will also provision the container and secure it in the low automation scenario.

repeatable deployment.png

Repeatable Deployments (High Level)

Once the infrastructure has been configured it is handed over to the application delivery teams. These teams deploy the necessary application servers to support the application that will run. This often includes application server clustering configuration and setting database connection pool information. These are things that developers and system administrators don’t necessarily know or care about. Finally, the application is deployed from a binary that is in a repository. This can be a vendor supplied binary or something that came from a build system that the development team created.

What is most often automated in this pattern is the deployment of the infrastructure and sometimes the deployment and configuration of the application server. The deployment of the binary is not often automated and the source to image process is altogether separated from the repeatable deployment of the infrastructure and application server.

Custom Repeatable Build
Next is what I call “Custom Repeatable Build”. This pattern is common in organizations that began automating prior to the emergence of prescriptive methods of automation or because they had other reasons like flexibility or expertise that they wanted to leverage.

custom repeatable build.png

Custom Repeatable Build (High Level)

In this pattern system administration teams are still responsible for deploying and configuring the infrastructure including storage, network, and compute as a virtual machine or container. This is often automated using popular tools. This infrastructure is then handed over as an available “unit” for the application delivery teams.

The application delivery teams in this pattern have taken ownership of the process of taking source to image and configuring the application server and delivering binary to the application server. This is done through the use of a configuration management or automation platform.

This pattern greatly decreases the time it takes to move code from development to operational. However, the knowledge required to create the source to image process and automate the deployment is high. As more application development teams are onboarded the resulting complexity also greatly increases. In one instance it took over 3 months to onboard a single application into this pattern. In a large environment with thousands of applications this would be untenable.

Prescriptive Repeatable Build
Finally, there is what I call the “Prescriptive Repeatable Build”. Similar to the other patterns the infrastructure including storage, network, and compute as a virtual machine or container are provisioned and configured by the system administration teams. Once this is complete a PaaS team provisions the PaaS using the infrastructure resources.

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Prescriptive Repeatable Build (High Level)

The PaaS exposes a self-service user interface for developers to obtain a new environment. Along with an endpoint for the instantiated service the developer is provided with an endpoint for manipulating the configuration of the service (usually the application code itself, but sometimes other aspects as well).

General Trends
Most organizations have multiple service design and delivery patterns happening at the same time. Most also want to move from repeatable deployment to prescriptive repeatable build wherever possible. This is because high automation can generally be equated with delivery of more applications in a shorter period of time. It also provides a greater degree of standardization thus reducing complexity.

Pains, Challenges, and Limitations
There are several pains, challenges, and limitations within each pattern.

The Repeatable Deployment pattern is generally the easiest to implement of the three. It generally includes automating infrastructure deployments and sometimes even automates the configuration of the application servers. Still, the disconnected nature of development from the deployment, configuration, and operation of the application itself means the repeatable deployment does not provide as much value when it comes to delivering applications faster. It also tends to lead to greater amounts of human error when deploying applications since it often relies on tribal knowledge or manual tasks between development and operations to run an application.

The Custom Repeatable Builds provide end users with a means of updating their applications automatically. This pattern also accommodates the existing source to image process, developer experience, and business requirements without requiring large amounts of change. This flexibility does come with a downside in that it takes a long time to onboard tenants. As mentioned earlier, we found that it can take months to onboard a simple application. Since large organizations typically have thousands of applications and potentially hundreds of development teams this pattern also leads to an explosion of complexity.

The Prescriptive Repeatable Builds provide the most standardization and allows developers to take source to a running application quickly. However, it often requires a significant effort to change the build process to fit into it’s opinionated deployment style. This leads to a larger risk to user acceptance depending on how application development teams behave in an organization. In using an opinionated method, however, the complexity of the end state can be reduced.

Finally, moving between each of these patterns is painful for organizations. In most cases, it is impossible to leverage existing investments from one pattern in another. This means redesigning and reimplementing service design each time.

How do Organizations Decide which Approach is Best?
Deciding which pattern is best is dependent on many factors including (but not limited to):

  • In-house skills
  • Homogeneity of application development processes
  • Business requirements driving application cycle time
  • Application architecture
  • Rigidity or flexibility of IT governance processes

The difference in pattern used on a per application basis is often the reason multiple patterns exist inside large organizations. For example, in a large organization that has grown through merger and acquisition there may be some application delivery teams that are building a Platform as a Service (PaaS) to enable prescriptive repeatable builds while others are using repeatable deployments and still others have hand crafted customized repeatable builds.

Principles for a Successful Service Design Solution
We have identified several principles that we believe a good service design solution should adhere to. This is not an exhaustive list. In no priority order they are:

  • Create Separation of Service Design and Element Authoring
    Each platform required to deliver either a repeatable deployment or repeatable build exposes its own set of elements. Infrastructure platforms might expose a Domain Specific Language (DSL) for describing compute, networking, and storage. Build systems may expose software projects and jobs. Automation and/or configuration management platforms may expose their own DSL. The list goes on. Each of the platforms has experts that author these. For example, an OpenShift PaaS platform expert will likely author a Kubernetes template. The service designer will not understand how to author this, but will need to discover the template and use it in the composition of a service. In other words: relate elements, don’t create elements.
  • Provide Support for All Patterns
    Not a single observed organization had one pattern. In fact, they all had multiple teams using multiple patterns. Solutions should take into account all the patterns observed or else they will fail to gain traction inside of organizations and provide efficiency.
  • Allow for Evolution
    Elements used in one pattern for service design should be able to be used in other service design patterns. For example, a virtual machine should be able to be a target for a repeatable deployment as well as a repeatable build. Failure to adhere to this will reduce the value of the solution as it will cause a high degree of duplication for end users.
  • Provide Insight
    We discovered that there were sections of automation in all the patterns that could be refactored into a declarative model (DSL) and away from an imperative model (workflow), but the designers of the service were either not aware of this or did not understand how to make the change. By providing insights to the service design about how to factor their service designs into the most declarative and portable model possible the solution could provide the most efficient and maintainable service design solution. Pattern recognition tools should be considered.

Improved Service Design Concept
An opportunity exists to greatly improve the service design experience. It can be possible to allow service designers to more easily design services using the widest range of items, accommodating the patterns required by the multiple teams inside most organization all while allowing the organization to evolve towards the prescriptive repeatable build pattern without losing their investments along the way. This concept allows for “services as code” and would provide a visual editor.

The concept begins with the discovery of existing elements within an organization’s platforms. For example, the discovery of a heat template from an OpenStack based IaaS platform or discovery of available repositories from a content system such as Nexus, Artifactory, or Red Hat Satellite. Discovery of these elements on a continual basis and ensuring they are placed into a source control system (or leveraging them from their existing source control systems).

discovery.png

Discovery of Element for Service Composition

Once the elements are discovered and populated the visual editor can allow for the service designer to author a new service composition. This service composition would never create elements, but would describe how the elements are related.

composition.png

Composition of Elements

While out of scope for the concept of service design the service composition could be visualized to the service consumer within any number of service catalogs that can read the service composition. Also outside of the scope of service design (although also important), brokers can utilize the service composition to instantiate a running instance of the service across the platforms.

instantiation.png

Publishing and Instantiation

Why does this matter to Red Hat?
Red Hat has a unique opportunity to provide a uniform way of designing services across all three patterns using both their products as well as other leading solutions in the market. In providing a uniform way it would increase the usability and understanding between teams within our customers and allow for an easier transition between the patterns of repeatability while still allowing users to choose what pattern is right for them. This means a reduction in friction when introducing repeatable service delivery solutions. This would directly benefit products that provide repeatable deployments such as Ansible, Satellite, and CloudForms by improving the user experience. Then, as a customer matures, the concepts discussed here would provide them with an evolutionary path to repeatable builds that would not require reengineering a solution. This would greatly benefit products such as OpenStack and OpenShift.

What’s Next?
Currently, we are working through the user experience for designing a sample application within the concept in more detail. Once we complete this we hope to build a functional prototype of the concept and continue to improve the design to obtain market validation. 

If you are a user that is interested in participating in our research or participating in co-creation please email me at jlabocki <at> redhat.com.

 

Scalable Infrastructure

In a previous post I outlined the common problems organizations face across both their traditional IT environments (sometimes called mode-1) and new emerging IT environments (sometimes called mode-2). These included:

  • Accelerating the delivery of services in traditional IT Environments to satisfy customer demands
  • Optimizing traditional IT environments to increase efficiency
  • Creating new development and operations practices for Emerging IT environment to  innovate faster
  • Delivering public-cloud like infrastructure that is scalable and programmable

I’d like to show you a quick demonstration of how Red Hat is delivering scalable infrastructure with the capabilities that enterprises demand. Red Hat Enterprise Linux OpenStack Platform delivers scale-out private cloud capabilities with a stable lifecycle and large ecosystem of supported hardware platforms. Many organizations are building their next generation cloud infrastructures on OpenStack because it provides an asynchronous architecture and is API centric allowing for greater scale and greater efficiency in platform management. OpenStack does not, however, provide functionality such as chargeback, reporting, and policy driven automation for tenant workloads and those projects that aspire to do so are generally focused solely on OpenStack. This is not realistic in an increasingly hybrid world – and enterprises that are serious about OpenStack need these capabilities. By using Red Hat CloudForms together with Red Hat Enterprise Linux OpenStack Platform it’s possible to provide capabilities such as reporting, chargeback, and auditing of tenant workloads across a geographically diverse deployment. In the demo below I demonstrate how chargeback across a multi-site OpenStack deployment works.

I hope you found this demonstration useful!

P.S. – If you are a Red Hatter or a Red Hat Partner, this demonstration is available in the Red Hat Product Demo System and is named “Red Hat Cloud Suite Reporting Demonstration”.

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Optimizing IT

In a previous post I outlined the common problems organizations face across both their traditional IT environments (sometimes called mode-1) and new emerging IT environments (sometimes called mode-2). These included:

  • Accelerating the delivery of services in traditional IT Environments to satisfy customer demands
  • Optimizing traditional IT environments to increase efficiency
  • Creating new development and operations practices for Emerging IT environment to  innovate faster
  • Delivering public-cloud like infrastructure that is scalable and programmable

I’d like to show you a quick demonstration of how Red Hat is helping optimize traditional IT environments. There are many ways in which Red Hat does this, from discovering and right sizing virtual machines to free up space in virtual datacenters, to creating a standard operating environment across heterogeneous environments to reduce complexity. In this demonstration, however, I’ll focus on how Red Hat enables organizations to migrate workloads to their ideal platform. In the demonstration video below you’ll see how using tools found in Red Hat Enterprise Virtualization and Red Hat Enterprise Linux OpenStack Platform in conjunction with automation and orchestration from Red Hat CloudForms it’s possible to migrate virtual machines in an automated fashion from VMware vSphere to either RHEV or Red Hat Enterprise Linux OpenStack Platform. Keep in mind, these tools assist with the migration process, but need to be designed for your specific environment. That said, they can greatly reduce the time and effort required to move large amounts of virtual machines once designed.

I hope you found this demonstration useful!

P.S. – If you are a Red Hatter or a Red Hat Partner, this demonstration is available in the Red Hat Product Demo System and is named “Red Hat Cloud Suite Migration Demonstration”.

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Accelerating Service Delivery Demonstration

In a previous post I outlined the common problems organizations face across both their traditional IT environments (sometimes called mode-1) and new emerging IT environments (sometimes called mode-2). These included:

  • Accelerating the delivery of services in traditional IT Environments to satisfy customer demands
  • Optimizing traditional IT environments to increase efficiency
  • Creating new development and operations practices for Emerging IT environment to  innovate faster
  • Delivering public-cloud like infrastructure that is scalable and programmable

I’d like to show you a quick demonstration of how Red Hat is helping accelerate service delivery for traditional IT environments. Developers or line of business users request stacks daily to create new services or test functionality. Each of these requests results in lots of work being done by operations and security teams. From creating virtual machines, to installing application servers, and even securing the systems – these tasks take time away from valuable resources that could be doing something else (like building out the next generation platform for development and operations). There are many solutions that exist for automating the deployment of virtual machines or the applications inside of the virtual machines, but Red Hat is uniquely positioned to automate both of these. By leveraging Red Hat CloudForms in conjunction with Red Hat Satellite it is possible to create a re-usable description for your application that can be automatically deployed via self-service with governance and controls across a hybrid cloud infrastructure. In the demonstration below we show the self-service automated deployment of a wordpress application consisting of HAProxy, 2 WordPress application servers, and a MariaDB database across both VMware vSphere and Red Hat Enterprise Virtualization.

P.S. – If you are a Red Hatter or a Red Hat Partner this demonstration is available in the Red Hat Product Demo System under the name “Red Hat Cloud Suite Deployment Demo”.

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Can Strategic Design Improve the Design and User Experience Across Open Source Communities?

If you speak to anyone involved in Information Technology there is little debate that an open source development model is the defacto development model for the next generation of technology. Cloud infrastructure with OpenStack, continuous integration with Jenkins, containers with Docker, automation with Ansible – these areas are all being transformed with technologies delivered via the open source development model. Even Microsoft has begun to embrace the open source development model (albeit sometimes only partially).

The use of an open source development model as a default is good news for everyone. Users (and organizations) are obtaining access to the greatest amount of innovation and can participate in development. At the same time, developers are able to increase their productivity and gain access to more collaborators. As organizations look to adopt technologies based on open source they often realize that it’s easier to purchase open source software rather than obtaining it directly from the community. There are many reasons including support, cost, focus on core business, and even indemnification that make it beneficial to purchase rather than consume directly from the community. Red Hat (where I work) is an example of a company that has provided software in exactly this way.

This model works well when organizations are using a single product based on a single open source community project. However, the open source development model poses challenges to creating a cohesive design and user experience across multiple products derived from open source projects. This problem ultimately affects the design and experience of the products organizations buy. In order for open source to continue it’s success in becoming the defacto standard a solution for the problem of coordinating design and user experience across multiple communities needs to solved.

The challenge of solving this problem should not be underestimated. If you think that influencing a single open source community is difficult, you can imagine how challenging it is to influence multiple communities in a coordinated manner. Developers in communities are purpose driven, wildly focused on their problem, and are focused on incremental progress. These developers justifiably shy away from grand plans, large product requirements documents, and any forceful attempts to change what they are building. After all, that’s the way monolithic, proprietary software lost to the fast moving and modular open source development model.

What is needed is a way to illustrate to development and community leaders how they can better satisfy their problem by working well with other communities and allow the community leaders to conclude on their own that they should work in the illustrated manner.

By practicing strategic design it may be possible to provide the clarity of vision and reasoning required to effectively influence multiple open source communities to work more cohesively. Strategic design is is the application of future-oriented design principles in order to increase an organization’s innovative and competitive qualities. Tim Brown, CEO of IDEO, summarizes some the purposes of design thinking (a major element of strategic design) really well in his Strategy by Design article.

At Red Hat we have recently begun testing this theory by organizing a design practice trial. The design practice trial team consisted of experts on various technologies from the field and engineering along with user experience experts.

IMG_2282

Part of the workshop trial team (During the final exercise on Day 3)

The premise for our design practice trial was simple:

•       Identify a common problem taking place across our customers using multiple open source products.
•       Analyze how the problem can be solved using the products.
•       Conceptualize an ideal user experience starting from a blank slate.
•       Share what was discovered with community leaders and product managers to assist with incremental improvement and influence direction toward the ideal user experience.

Identify
The common problem we found was organizations struggling to design re-usable services effectively.  The persona we identified for our trial was the service designer. The service designer identifies, qualifies, builds, and manages entries in a service catalog for self-service consumption by consumers. The service designer would like to easily design and publish re-usable entries in a catalog from the widest range of possible items.

Analyze
The products we used to analyze the current user experience are:

•       OpenStack to deliver Infrastructure as a Service (IaaS)
•       OpenShift to deliver Platform as a Service (PaaS)
•       ManageIQ (CloudForms) to deliver a Cloud Management Platform (CMP)
•       Pulp and Candlepin (Satellite) to deliver a Content Mirroring and Entitlement
•       Ansible (Tower) to deliver automation

Looking across our communities we find lots of “items” in each project. OpenStack provides Heat Templates, OpenShift provides Kubernetes Templates, Ansible provides play books, and on and on. In addition to this, the service designer would likely want to mix items from outside of these projects for use in a catalog entry, including public cloud and SaaS services.

What would it look like for the service designer to assemble all these items into an entry that could be ordered by a consumer that leads to a repeatable deployment? During a 3-day workshop we put ourselves in the shoes of the service designer and attempted to design an application.

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The Example Application

The team was able to design the catalog entry in about 8 hours and we feel we could have done this even faster if we weren’t so new to Ansible Tower.

Here is a quick video demonstration of deploying this application from the catalog as a finished product.

Or in MP4

I’ll spare every one the detailed results here (if you work at Red Hat send me a note and I’ll link you to our more complete analysis), but the exercise of analyzing the current solution allowed us to identify many areas for incremental improvement when using these products together to satisfy the use case of the service designer. We also identified longer term design questions that need to be resolved between the products (and ultimately, the upstream projects).

Conceptualize
What would the ideal user experience be for this? Another exercise we performed in our workshop was designing an ideal user experience starting from a blank slate. This included challenging assumptions while still defining some constraints of the concept. This proved to be challenging and eye opening for everyone involved. Starting from scratch with a design and not worrying about the underlying engineering that would be required is difficult for engineering minded individuals. We began developing what we believe the ideal user experience for service design would be. These will be worked into a workflow and low fidelity mockups to illustrate the basics of the experience.

Share
As next steps we will share our findings with community leaders and product managers in the hopes that it positively impacts the design and user experience. We will also continue meeting with customers who we believe suffer from the service design problem to continue to refine our proposed ideal design and to help them understand how our current solution works. If all goes well, we might even attempt a prototype or mock user interface to start. There are plenty of other angles we need to address, such as the viability of customer adoption and customer willingness to pay for such a concept. For a 3-day workshop (and lots of prep before), however, we feel we are off to a good start.

Will the community leaders accept our assertions that they can deliver a better experience by working together? Will any of the concepts and prototypes make it into the hands of customers? This remains to be seen. My hunch is that none of the communities will accept the exact conceptual design and user experience put forth by the Design Practice, but the conceptual design and user experience will positively influence the design and user experience within the open source communities and ultimately make it’s way to customers via Red Hat’s products and solutions. In any case, the more time we spend practicing design, the better the lives of our customers will become.

-James
@jameslabocki

 

Ansible Tower Dynamic Inventory from CloudForms

In my previous post I showed an example of how as part of the provisioning of a service CloudForms could be integrated with Ansible Tower to provide greater re-usability and portability of stacks across multiple infrastructure/cloud providers. Now I would like to show an example of how Ansible Tower’s Dynamic Inventory feature can be used in conjunction with the inventory in CloudForms to populate an inventory that can have job templates executed on them. Right now CloudForms has hosts and virtual machines in it’s inventory that would be useful to Ansible, but in the next version container support will allow CloudForms to pass Ansible an inventory of containers as hosts as well (that will be really interesting).

For those not familiar, Dynamic Inventory is a feature in Ansible Tower that allows users to maintain an inventory of hosts based on the data in an external system (LDAP, cobbler, CMDBs, EC2, etc) so they can integrate Ansible Tower into there existing environment instead of building a static inventory inside Ansible Tower itself. Since CloudForms can maintain discovery of existing workloads across many providers (vSphere, Hyper-V, RHEV, OpenStack, EC2 to name a few) it seems natural that it would be a great source of providing a dynamic inventory to Ansible for execution of job templates.

I authored the ansible_tower_cloudforms_inventory.py script to allow users to build a dynamic inventory in Ansible Tower that comes from CloudForms virtual machine inventory. This means that any time a user provisions a VM on vSphere, Hyper-V, OpenStack, RHEV, EC2, or other supported platform – CloudForms will automatically discover that VM and Ansible Tower will have it added to an inventory so it can be managed via Ansible Tower.

To use the script simply navigate to Ansible Tower’s setup page and select “Inventory Scripts”.

Screen Shot 2015-11-17 at 8.18.25 PMFrom there select the icon to add a new inventory script. You can now add the inventory script and name it and associate it with your organization.

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You should now see the added inventory script.

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Now within the inventory of your choosing add a new group named “Dynamic_CloudForms” and change the source to “Custom Script” and select your newly added script within “Custom Inventory Script”.

Screen Shot 2015-11-17 at 8.29.03 PM

Your new group should be added to your inventory

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One last thing needed by the script is the cloudforms.ini file. This file holds things like the hostname of your CloudForms instance, the username and password to use to authenticate, and other information. You’ll need to place this on your Ansible Tower server in /opt/rh/cloudforms.ini. I also found I had to install the request service on the Ansible Tower server (`yum install python-pip -y; pip install requests`).

Now you should be able to run the “start sync process” manually from the inventory screen (it’s an icon that looks like two arrows pointing opposite directions). You could also schedule this sync to run on a recurring basis.

Screen Shot 2015-11-17 at 9.44.48 PM

And Voila! Your inventory has been populated with names from CloudForms. The script will take into account some VMs may be powered down or actually be templates and add only those machines with a power_state of “on”.

It should be noted that the inventory script currently adds the VM based on it’s name in CloudForms. This is because my smart state analysis isn’t set up in my appliance and I don’t have any other fields available to me. What should be done eventually (when smart state is working) is changing the ansible_tower_cloudforms_inventory.py script to query for the ip address or hostname field of the VM. One more thing … I skipped a lot of the security checks on certificates (probably not a good thing). It shouldn’t be difficult to alter the script and python requests configuration to point to your certificates for a more secure experience.

Enjoy!

 

 

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