Category Archives: Open Trends

Supply Chain Transparency drives Open Source adoption, 6 reasons besides cost

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Author’s note: If you don’t believe that software is manufactured then go directly to your TRS80, do not collect $200.

I’m becoming increasingly impatient with people stating that “open source is about free software” because it’s blatantly untrue as a primary driver for corporate adoption.   Adopting open source often requires companies (and individuals) to trade-off one cost (license expense) for another (building expertise).  It is exactly the same balance we make between insourcing, partnering and outsourcing.

Full Speed Ahead

When I probe companies about what motivates their use of open source, they universally talk about transparency of delivery, non-single-vendor ownership of the source and their ability to influence as critical selection factors.  They are generally willing to invest more to build expertise if it translates into these benefits.  Viewed in this light, licensed software or closed services both cost more and introduce significant business risks where open alternatives exist.

This is not new: its basic manufacturing applied to IT

We had this same conversation in the 90s around manufacturing as that industry joltingly shifted from batch to just-in-time (aka Lean) manufacturing.  The key driver for that transformation was improved integration and management of supply chains.   We review witty doctoral dissertations about inventory, drum-buffer-rope flow and economic order quantity; however, trust my summary that it all comes down to companies need supply chain transparency.

As technology becomes more and more integral to delivering any type of product, companies must extend their need for supply chain transparency into their IT systems too.   That does not mean that companies expect to self-generate (insource) all of their technology.  The goal is to manage the supply chain, not to own every step.   Smart companies find a balance between control of owning their supply (making it themselves) and finding a reliable supply (multi-source is preferred).  If you cannot trust your suppliers then you must create inventory buffers and rigid contracts.  Both of these defenses limit agility and drive systemic dysfunction.  This was the lesson learned from Lean Just-In-Time manufacturing.

What does this look like for IT supply chains?

A healthy supply chain allows companies to address these issues.  They can:

  1. Change vendors / suppliers and get equivalent supply
  2. Check the status of deliveries (features)
  3. Review and impact quality
  4. Take deliverables in small frequent batches
  5. Collaborate with suppliers to manage & control the process
  6. Get visibility into the pipeline

None of these items are specific to software; instead, they are general attributes of a strong supply chain.  In a closed system, companies lose these critical supply chain values.  While tightly integrated partnerships can provide these benefits, they carry a cost premium and inherently limit vendor choice.

This sounds great!  What’s the cost?

You need to consider the level of supply chain transparency that’s right for you.  Most companies are no more likely to refine their own metal than to build from pure open source repositories.  There are transparency benefits from open source even from a single supplier.  Yet in some cases like the OpenStack community, systems are so essential that they are warrant investing as core competencies and joining the contributing community.  Even in those cases, most rely on vendors to package and extend their chosen open source software.

But that misses the point: contributing to an open source project is not required in managing your IT supply chain.  Instead, you need to build the operational infrastructure and processes that is open source ready.  They may require investing in skills and capabilities related to underlying technologies like the operating system, database or configuration management.  For cloud, it is likely to require more investment fault-tolerant architecture and API driven deployment.  Companies that are strong in these skills are better able to manage an open source IT supply chain.  In fact, they are better able to manage any IT supply chain because they have more control.

So, it’s not about cost…

When considering motivations for open source adoption, cost (or technology sizzle) should not be the primary factor.  In my experience, the most successful implementations focus first about operational readiness and project stability, and program transparency.  These questions indicate companies are thinking with an IT supply chain focus.

PS: If you found this interesting, you’ll also like my upstream imperative post.

Understanding OpenStack Designated Code Sections – Three critical questions

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A collaboration with Michael Still (TC Member from Rackspace) & Joshua McKenty and Cross posted by Rackspace.

After nearly a year of discussion, the OpenStack board launched the DefCore process with 10 principles that set us on path towards a validated interoperability standard.   We created the concept of “designated sections” to address concerns that using API tests to determine core would undermine commercial and community investment in a working, shared upstream implementation.

Designated SectionsDesignated sections provides the “you must include this” part of the core definition.  Having common code as part of core is a central part of how DefCore is driving OpenStack operability.

So, why do we need this?

From our very formation, OpenStack has valued implementation over specification; consequently, there is a fairly strong community bias to ensure contributions are upstreamed. This bias is codified into the very structure of the GNU General Public License (GPL) but intentionally missing in the Apache Public License (APL v2) that OpenStack follows.  The choice of Apache2 was important for OpenStack to attract commercial interests, who often consider GPL a “poison pill” because of the upstream requirements.

Nothing in the Apache license requires consumers of the code to share their changes; however, the OpenStack foundation does have control of how the OpenStack™ brand is used.   Thus it’s possible for someone to fork and reuse OpenStack code without permission, but they cannot called it “OpenStack” code.  This restriction only has strength if the OpenStack brand has value (protecting that value is the primary duty of the Foundation).

This intersection between License and Brand is the essence of why the Board has created the DefCore process.

Ok, how are we going to pick the designated code?

Figuring out which code should be designated is highly project specific and ultimately subjective; however, it’s also important to the community that we have a consistent and predictable strategy.  While the work falls to the project technical leads (with ratification by the Technical Committee), the DefCore and Technical committees worked together to define a set of principles to guide the selection.

This Technical Committee resolution formally approves the general selection principles for “designated sections” of code, as part of the DefCore effort.  We’ve taken the liberty to create a graphical representation (above) that visualizes this table using white for designated and black for non-designated sections.  We’ve also included the DefCore principle of having an official “reference implementation.”

Here is the text from the resolution presented as a table:

Should be DESIGNATED: Should NOT be DESIGNATED:
  • code provides the project external REST API, or
  • code is shared and provides common functionality for all options, or
  • code implements logic that is critical for cross-platform operation
  • code interfaces to vendor-specific functions, or
  • project design explicitly intended this section to be replaceable, or
  • code extends the project external REST API in a new or different way, or
  • code is being deprecated

The resolution includes the expectation that “code that is not clearly designated is assumed to be designated unless determined otherwise. The default assumption will be to consider code designated.”

This definition is a starting point.  Our next step is to apply these rules to projects and make sure that they provide meaningful results.

Wow, isn’t that a lot of code?

Not really.  Its important to remember that designated sections alone do not define core: the must-pass tests are also a critical component.   Consequently, designated code in projects that do not have must-pass tests is not actually required for OpenStack licensed implementation.

OpenCrowbar Design Principles: Attribute Injection [Series 6 of 6]

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This is part 5 of 6 in a series discussing the principles behind the “ready state” and other concepts implemented in OpenCrowbar.  The content is reposted from the OpenCrowbar docs repo.

Attribute Injection

Attribute Injection is an essential aspect of the “FuncOps” story because it helps clean boundaries needed to implement consistent scripting behavior between divergent sites.

attribute_injectionIt also allows Crowbar to abstract and isolate provisioning layers. This operational approach means that deployments are composed of layered services (see emergent services) instead of locked “golden” images. The layers can be maintained independently and allow users to compose specific configurations a la cart. This approach works if the layers have clean functional boundaries (FuncOps) that can be scoped and managed atomically.

To explain how Attribute Injection accomplishes this, we need to explore why search became an anti-pattern in Crowbar v1. Originally, being able to use server based search functions in operational scripting was a critical feature. It allowed individual nodes to act as part of a system by searching for global information needed to make local decisions. This greatly added Crowbar’s mission of system level configuration; however, it also created significant hidden interdependencies between scripts. As Crowbar v1 grew in complexity, searches became more and more difficult to maintain because they were difficult to correctly scope, hard to centrally manage and prone to timing issues.

Crowbar was not unique in dealing with this problem – the Attribute Injection pattern has become a preferred alternative to search in integrated community cookbooks.

Attribute Injection in OpenCrowbar works by establishing specific inputs and outputs for all state actions (NodeRole runs). By declaring the exact inputs needed and outputs provided, Crowbar can better manage each annealing operation. This control includes deployment scoping boundaries, time sequence of information plus override and substitution of inputs based on execution paths.

This concept is not unique to Crowbar. It has become best practice for operational scripts. Crowbar simply extends to paradigm to the system level and orchestration level.

Attribute Injection enabled operations to be:

  • Atomic – only the information needed for the operation is provided so risk of “bleed over” between scripts is minimized. This is also a functional programming preference.
  • Isolated Idempotent – risk of accidentally picking up changed information from previous runs is reduced by controlling the inputs. That makes it more likely that scripts can be idempotent.
  • Cleanly Scoped – information passed into operations can be limited based on system deployment boundaries instead of search parameters. This allows the orchestration to manage when and how information is added into configurations.
  • Easy to troubleshoot – since the information is limited and controlled, it is easier to recreate runs for troubleshooting. This is a substantial value for diagnostics.

OpenCrowbar Design Principles: Emergent services [Series 5 of 6]

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This is part 5 of 6 in a series discussing the principles behind the “ready state” and other concepts implemented in OpenCrowbar.  The content is reposted from the OpenCrowbar docs repo.

Emergent services

We see data center operations as a duel between conflicting priorities. On one hand, the environment is constantly changing and systems must adapt quickly to these changes. On the other hand, users of the infrastructure expect it to provide stable and consistent services for consumption. We’ve described that as “always ready, never finished.”

Our solution to this duality to expect that the infrastructure Crowbar builds is decomposed into well-defined service layers that can be (re)assembled dynamically. Rather than require any component of the system to be in a ready state, Crowbar design principles assume that we can automate the construction of every level of the infrastructure from bios to network and application. Consequently, we can hold off (re)making decisions at the bottom levels until we’ve figured out that we’re doing at the top.

Effectively, we allow the overall infrastructure services configuration to evolve or emerge based on the desired end use. These concepts are built on computer science principles that we have appropriated for Ops use; since we also subscribe to Opscode “infrastructure as code”, we believe that these terms are fitting in a DevOps environment. In the next pages, we’ll explore the principles behind this approach including concepts around simulated annealing, late binding, attribute injection and emergent design.

Emergent (aka iterative or evolutionary) design challenges the traditional assumption that all factors must be known before starting

  • Dependency graph – multidimensional relationship
  • High degree of reuse via abstraction and isolation of service boundaries.
  • Increasing complexity of deployments means more dependencies
  • Increasing revision rates of dependencies but with higher stability of APIs

OpenCrowbar Design Principles: Simulated Annealing [Series 4 of 6]

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This is part 4 of 6 in a series discussing the principles behind the “ready state” and other concepts implemented in OpenCrowbar.  The content is reposted from the OpenCrowbar docs repo.

Simulated Annealing

simulated_annealingSimulated Annealing is a modeling strategy from Computer Science for seeking optimum or stable outcomes through iterative analysis. The physical analogy is the process of strengthening steel by repeatedly heating, quenching and hammering. In both computer science and metallurgy, the process involves evaluating state, taking action, factoring in new data and then repeating. Each annealing cycle improves the system even though we may not know the final target state.

Annealing is well suited for problems where there is no mathematical solution, there’s an irregular feedback loop or the datasets change over time. We have all three challenges in continuous operations environments. While it’s clear that a deployment can modeled as directed graph (a mathematical solution) at a specific point in time, the reality is that there are too many unknowns to have a reliable graph. The problem is compounded because of unpredictable variance in hardware (NIC enumeration, drive sizes, BIOS revisions, network topology, etc) that’s even more challenging if we factor in adapting to failures. An operating infrastructure is a moving target that is hard to model predictively.

Crowbar implements the simulated annealing algorithm by decomposing the operations infrastructure into atomic units, node-roles, that perform the smallest until of work definable. Some or all of these node-roles are changed whenever the infrastructure changes. Crowbar anneals the environment by exercising the node-roles in a consistent way until system re-stabilizes.

One way to visualize Crowbar annealing is to imagine children who have to cross a field but don’t have a teacher to coordinate. Once student takes a step forward and looks around then another sees the first and takes two steps. Each child advances based on what their peers are doing. None wants to get too far ahead or be left behind. The line progresses irregularly but consistently based on the natural relationships within the group.

To understand the Crowbar Annealer, we have to break it into three distinct components: deployment timeline, annealing and node-role state. The deployment timeline represents externally (user, hardware, etc) initiated changes that propose a new target state. Once that new target is committed, Crowbar anneals by iterating through all the node-roles in a reasonable order. As the Annealer runs the node-roles they update their own state. The aggregate state of all the node-roles determines the state of the deployment.

A deployment is a combination of user and system defined state. Crowbar’s job is to get deployments stable and then maintain over time.

OpenCrowbar Design Principles: The Ops Challenge [Series 2 of 6]

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This is part 2 of 6 in a series discussing the principles behind the “ready state” and other concepts implemented in OpenCrowbar.  The content is reposted from the OpenCrowbar docs repo.

The operations challenge

A deployment framework is key to solving the problems of deploying, configuring, and scaling open source clusters for cloud computing.

2012-09-21_13-51-00_331Deploying an open source cloud can be a complex undertaking. Manual processes, can take days or even weeks working to get a cloud fully operational. Even then, a cloud is never static, in the real world cloud solutions are constantly on an upgrade or improvement path. There is continuous need to deploy new servers, add management capabilities, and track the upstream releases, while keeping the cloud running, and providing reliable services to end users. Service continuity requirements dictate a need for automation and orchestration. There is no other way to reduce the cost while improving the uptime reliability of a cloud.

These were among the challenges that drove the development of the OpenCrowbar software framework from it’s roots as an OpenStack installer into a much broader orchestration tool. Because of this evolution, OpenCrowbar has a number of architectural features to address these challenges:

  • Abstraction Around OrchestrationOpenCrowbar is designed to simplify the operations of large scale cloud infrastructure by providing a higher level abstraction on top of existing configuration management and orchestration tools based on a layered deployment model.
  • Web ArchitectureOpenCrowbar is implemented as a web application server, with a full user interface and a predictable and consistent REST API.
  • Platform Agnostic ImplementationOpenCrowbar is designed to be platform and operating system agnostic. It supports discovery and provisioning from a bare metal state, including hardware configuration, updating and configuring BIOS and BMC boards, and operating system installation. Multiple operating systems and heterogeneous operating systems are supported. OpenCrowbar enables use of time-honored tools, industry standard tools, and any form of scriptable facility to perform its state transition operations.
  • Modular ArchitectureOpenCrowbar is designed around modular plug-ins called Barclamps. Barclamps allow for extensibility and customization while encapsulating layers of deployment in manageable units.
  • State Transition Management EngineThe core of OpenCrowbar is based on a state machine (we call it the Annealer) that tracks nodes, roles, and their relationships in groups called deployments. The state machine is responsible for analyzing dependencies and scheduling state transition operations (transitions).
  • Data modelOpenCrowbar uses a dedicated database to track system state and data. As discovery and deployment progresses, system data is collected and made available to other components in the system. Individual components can access and update this data, reducing dependencies through a combination of deferred binding and runtime attribute injection.
  • Network AbstractionOpenCrowbar is designed to support a flexible network abstraction, where physical interfaces, BMC’s, VLANS, binding, teaming, and other low level features are mapped to logical conduits, which can be referenced by other components. Networking configurations can be created dynamically to adapt to changing infrastructure.

Continue Reading > post 3

OpenCrowbar Design Principles: Reintroduction [Series 1 of 6]

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While “ready state” as a concept has been getting a lot of positive response, I forget that much of the innovation and learning behind that concept never surfaced as posts here.  The Anvil (2.0) release included the OpenCrowbar team cataloging our principles in docs.  Now it’s time to repost the team’s work into a short series over the next three days.

In architecting the Crowbar operational model, we’ve consistently twisted adapted traditional computer science concepts like late binding, simulated annealing, emergent behavior, attribute injection and functional programming to create a repeatable platform for sharing open operations practice (post 2).

Functional DevOps aka “FuncOps”

Ok, maybe that’s not going to be the 70’s era hype bubble name, but… the operational model behind Crowbar is entering its third generation and its important to understand the state isolation and integration principles behind that model is closer to functional than declarative programming.

Parliament is Crowbar’s official FuncOps sound track

The model is critical because it shapes how Crowbar approaches the infrastructure at a fundamental level so it makes it easier to interact with the platform if you see how we are approaching operations. Crowbar’s goal is to create emergent services.

We’ll expore those topics in this series to explain Crowbar’s core architectural principles.  Before we get into that, I’d like to review some history.

The Crowbar Objective

Crowbar delivers repeatable best practice deployments. Crowbar is not just about installation: we define success as a sustainable operations model where we continuously improve how people use their infrastructure. The complexity and pace of technology change is accelerating so we must have an approach that embraces continuous delivery.

Crowbar’s objective is to help operators become more efficient, stable and resilient over time.

Background

When Greg Althaus (github @GAlhtaus) and Rob “zehicle” Hirschfeld (github @CloudEdge) started the project, we had some very specific targets in mind. We’d been working towards using organic emergent swarming (think ants) to model continuous application deployment. We had also been struggling with the most routine foundational tasks (bios, raid, o/s install, networking, ops infrastructure) when bringing up early scale cloud & data applications. Another key contributor, Victor Lowther (github @VictorLowther) has critical experience in Linux operations, networking and dependency resolution that lead to made significant contributions around the Annealing and networking model. These backgrounds heavily influenced how we approached Crowbar.

First, we started with best of field DevOps infrastructure: Opscode Chef. There was already a remarkable open source community around this tool and an enthusiastic following for cloud and scale operators . Using Chef to do the majority of the installation left the Crowbar team to focus on

crowbar_engineKey Features

  • Heterogeneous Operating Systems – chose which operating system you want to install on the target servers.
  • CMDB Flexibility (see picture) – don’t be locked in to a devops toolset. Attribute injection allows clean abstraction boundaries so you can use multiple tools (Chef and Puppet, playing together).
  • Ops Annealer –the orchestration at Crowbar’s heart combines the best of directed graphs with late binding and parallel execution. We believe annealing is the key ingredient for repeatable and OpenOps shared code upgrades
  • Upstream Friendly – infrastructure as code works best as a community practice and Crowbar use upstream code
  • without injecting “crowbarisms” that were previously required. So you can share your learning with the broader DevOps community even if they don’t use Crowbar.
  • Node Discovery (or not) – Crowbar maintains the same proven discovery image based approach that we used before, but we’ve streamlined and expanded it. You can use Crowbar’s API outside of the PXE discovery system to accommodate Docker containers, existing systems and VMs.
  • Hardware Configuration – Crowbar maintains the same optional hardware neutral approach to RAID and BIOS configuration. Configuring hardware with repeatability is difficult and requires much iterative testing. While our approach is open and generic, the team at Dell works hard to validate a on specific set of gear: it’s impossible to make statements beyond that test matrix.
  • Network Abstraction – Crowbar dramatically extended our DevOps network abstraction. We’ve learned that a networking is the key to success for deployment and upgrade so we’ve made Crowbar networking flexible and concise. Crowbar networking works with attribute injection so that you can avoid hardwiring networking into DevOps scripts.
  • Out of band control – when the Annealer hands off work, Crowbar gives the worker implementation flexibility to do it on the node (using SSH) or remotely (using an API). Making agents optional means allows operators and developers make the best choices for the actions that they need to take.
  • Technical Debt Paydown – We’ve also updated the Crowbar infrastructure to use the latest libraries like Ruby 2, Rails 4, Chef 11. Even more importantly, we’re dramatically simplified the code structure including in repo documentation and a Docker based developer environment that makes building a working Crowbar environment fast and repeatable.

OpenCrowbar (CB2) vs Crowbar (CB1)?

Why change to OpenCrowbar? This new generation of Crowbar is structurally different from Crowbar 1 and we’ve investing substantially in refactoring the tooling, paying down technical debt and cleanup up documentation. Since Crowbar 1 is still being actively developed, splitting the repositories allow both versions to progress with less confusion. The majority of the principles and deployment code is very similar, I think of Crowbar as a single community.

Continue Reading > post 2

Just for fun, putting themes to OpenStack Conferences

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I’ve been to every OpenStack summit and, in retrospect, each one has a different theme.  I see these as community themes beyond the releases train that cover how the OpenStack ecosystem has changed.

The themes are, of course, highly subjective and intented to spark reflection and discussion.

City Release Theme My Commentary
ATL Ice House Its my sandbox! The new marketplace is great and there are also a lot of vendors who want to differentiate their offering and are not sure where to play.
HK Havana Project land grab It felt like a PTL gold rush as lots of new projects where tossed into the ecosystem mix.  I’m wary of perceived “anointed” projects that define “the way” to do things.
PDX Grizzly Shiny new things We went from having a defined core set of projects to a much richer and varied platforms, environments and solutions.
SD Folsom Breaking up is hard to do Nova began to fragment (cinder & quantum neutron)
SF Essex New kids are here Move over Rackspace.  Lots of new operating systems, providers, consulting and hosting companies participating.  Stackalytics makes it into a real commit race.
BOS Diablo Race to be the first Everyone was trying to show that OpenStack could be used for real work.  Lots of startups launched.
SJC Cactus Oh, you like us! We need some process This is real so everyone was exporing OpenStack.  We clearly needed to figure out how to work together.  This is where we migrated to git.
SA Bexar We’re going to take over the world We handed out rose-colored classes that mostly turned out pretty accurate; however. many some top names from that time are not in the community now (Citrix, NASA, Accenture, and others).
ATX Austin We choose “none of the above” There was a building sense of potential energy while companies figured out that 1) there was a gap and 2) they wanted to fill it together.

How DefCore is going to change your world: three advisory cases

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The first release of the DefCore Core Capabilities Matrix (DCCM) was revealed at the Atlanta summit.  At the Summit, Joshua and I had a session which examined what this means for the various members of the OpenStack community.   This rather lengthy post reviews the same advisory material.

DefCore sets base requirements by defining 1) capabilities, 2) code and 3) must-pass tests for all OpenStack products. This definition uses community resources and involvement to drive interoperability by creating the minimum standards for products labeled “OpenStack.”

As a refresher, there are three uses of the OpenStack mark:

  • Community: The non-commercial use of the word OpenStack by the OpenStack community to describe themselves and their activities. (like community tweets, meetups and blog posts)
  • Code: The non-commercial use of the word OpenStack to refer to components of the OpenStack framework integrated release (as in OpenStack Compute Project Nova)
  • Commerce: The commercial use of the word OpenStack to refer to products and services as governed by the OpenStack trademark policy. This is where DefCore is focused.

In the DefCore/Commerce use, properly licensed vendors have three basic obligations to meet:is_it_openstack_graphic

  1. Pass the required Refstack tests for the capabilities matrix in the version of OpenStack that they use. Vendors are expected (not required) to share their results.
  2. Run and include the “designated sections” of code for the OpenStack components that you include.
  3. Other basic obligations in their license agreement like being a currently paid up corporate sponsor or foundation member, etc.

If they meet these conditions, vendors can use the OpenStack mark in their product names and descriptions.

Enough preamble!  Let’s see the three Advisory Cases

MANDATORY DISCLAIMER: These conditions apply to fictional public, private and client use cases.  Any resemblence to actual companies is a function of the need to describe real use-cases.  These cases are advisory for illustration use only and are not to be considered definitive guidenance because DefCore is still evolving.

Public Cloud: Service Provider “BananaCloud”

A popular public cloud operator, BananaCloud has been offering OpenStack-based IaaS since the Diablo release. However, they don’t use the Keystone component. Since they also offer traditional colocation and managed services, they have an existing identity management system that they use. They made a similar choice for Horizon in favor of their own cloud portal.

banana

  1. They use Nova a custom scheduler and pass all the Nova tests. This is the simplest case since they use code and pass the tests.
  2. In the Havana DCCM, the Keystone capabilities are a must pass test; however, there are no designated sections of code for Keystone. So BananaCloud must implement a Keystone-compatible API on their IaaS environment (an effort they had underway already) that will pass Refstack, and they’re good to go.
  3. There are no must pass tests for Horizon so they have no requirements to include those features or code. They can still be OpenStack without Horizon.
  4. There are no must pass tests for Trove so they have no brand requirements to include those features or code so it’s not a brand issue; however, by using Trove and promoting its use, they increase the likelihood of its capabilities becoming must pass features.

BananaCloud also offers some advanced OpenStack capabilities, including Marconi and Trove. Since there are no must pass capabilities from these components in the Havana DCCM, it has no impact on their offering additional services. DefCore defines the minimum requirements and encourages vendors to share their full test results of additional capabilities because that is how OpenStack identifies new must pass candidates.

Note: The DefCore DCCM is advisory for the Havana release, so if BananaCloud is late getting their Keystone-compatibility work done there won’t be any commercial impact. But it will be a binding part of the trademark license agreement by the Juno release, which is only 6 months away.

Private Cloud: SpRocket Small-Business OpenStack Software

SpRocket is a new OpenStack software vendor, specializing in selling a Windows-powered version of OpenStack with tight integration to Sharepoint and AzurePack. In their feature set, they only need part of Nova and provide an alternative object storage to Swift that implements a version of the Swift API. They do use Heat as part of their implementation to set up applications back ended by Sharepoint and AzurePack.

  1. sprocketFor Nova, they already use the code and have already implemented all required capabilities except for the key-store. To comply with the DefCore requirement, they must enable the key-store capability.
  2. While their implementation of Swift passes the tests, We are still working to resolve the final disposition of Swift so there are several possible outcomes:
    1. If Swift is 0% designated then they are OK (that’s illustrated here)
    2. If Swift is 100% designated then they cannot claim to be OpenStack.
    3. If Swift is partially designated then they have to adapt their deploy to include the required code.
  3. Their use of Heat is encouraged since it is an integrated project; however, there are no required capabilities and does not influence their ability to use the mark.
  4. They use the trunk version of Windows HyperV drivers which are not designated and have no specific tests.

Ecosystem Client: “Mist” OpenStack-consuming Client Library

Mist is a client library for load+kt programmers working on applications using the OpenStack APIs. While it’s an open source project, there are many commercial applications that use the library for their applications. Unlike a “pure” OpenStack program, it also supports other Cloud APIs.

Since the Mist library does not ship or implement the OpenStack code base, the DefCore process does not apply to their effort; however, there are several important intersections with Mist and OpenStack and Core.

  • First, it is very important for the DefCore process that Mist map their use of the OpenStack APIs to the capabilities matrix. They are asked to help with this process because they are the best group to answer the “works with clients” criteria.
  • Second, if there are APIs used by Mist that are not currently tested then the OpenStack community should work with the Mist community to close those test gaps.
  • Third, if Mist relies on an API that is not must-pass they are encouraged to help identify those capabilities as core candidates in the community.