July 28, 2017

Protecting your border or offering a service to others?

The value of automation in the DataCenter

Everyone is aware of the value of the automation.
Many companies and individual engineers implemented various ways to save time, from shell scripts to complex programs and to fully automated IaaS solutions.

It helps reducing the so called "Shadow IT", a phenomenon that happens when developers can't get a fast enough response from the IT of the company and rush to the public cloud to get what they need. Doing that they complete and release their project soon, but sometimes troubles start with the production phase of the deployment (unexpected additional budget for the IT, new technologies that they are not ready to manage, etc.).

shadow IT happens when corporate IT is not fast enough
shadow IT happens when corporate IT is not fast enough

For sure, some departments are organized in silos (a team responsible for servers, one for storage, one for networking, one for virtual machines, of course one for security...) and the provisioning of even simple requests takes too long.

process inefficiency due to silos and wait time
process inefficiency due to silos and wait time

Pressure on the infrastructure managers

So there is inefficiency in the company, that affects the business outcome of every project.
Longer time to market for strategic initiatives, higher costs for infrastructure and people.
Finger pointing starts, to identify who is responsible for the bottleneck.

The efficiency of teams and individuals is questioned, and responsibility is cascaded through the organization from project managers to developers, to the server team, to the storage team and generally the network is at the end of the chain... so that they have no one else to blame.

Those on the top (they consider themselves on top of the value chain) believe - or try to demonstrate - that their work is slowed down by the inefficiency of the teams they depend on. They try to suggest solutions like: "you said that your infrastructure is programmable, now give me your API and I will create everything I need on demand".

Of course this approach could bring some value (not much, as we'll see in the rest of the post) but it mines the relevance of the specialists teams that are supposed to manage the infrastructure according to best practices, to apply architectural blueprints that have been optimized for the company's specific business, to know the technology in deeper detail.
So they can't accept to be bypassed by a bunch of developers that want to corrupt the system playing with precious assets with their dirty hands.

The definitive question is: who owns the automation?
Should it be left to people that know what they need (e.g. Developers)?
Should it be owned by people that know how technology works, and at the end of the day are responsible for the SLA including performances, security and reliability that could be affected by a configuration made by others (i.e. IT Administrators)?

In my opinion, and based on the experience shared with many customers, the second answer is the correct one.
By definition the developer is not an expert on security: if he can easily program a switch via its REST API to get a network segment, it’s not the same when traffic needs to be secured and inspected.

The IT admin patrols the infrastructure
The IT Admin patrolling the infrastructure

Offering a self service catalog (or API)

A first, immediate solution could be the introduction of an easy automation tool like Cisco UCS Director, that manages almost every element in a multi vendor Data Center infrastructure: from servers to networks to storage to virtualization in a single dashboard. But what is more interesting is that every atomic action you do in the GUI is also reflected in a task in the automation library, that allows you to create custom workflows lining all the tasks for a process that you want to automate.
A common example of automation workflow is the creation of a 4-hypervisors server farm.
A single workflow starts from the SAN storage creating a volume and 4 LUN, where the hypervisor will be installed to enable remote boot for the servers. Then a network is created (or the existing management network will be used) and 4 Service Profiles (the definition of a server in Cisco UCS) are created from a template, with individual ip address, mac address and wwn for each network interface. Then, zoning and masking are executed to map every new server to a specific LUN and the service profiles are associated to 4 available servers (either blades or rack mount servers). The hypervisors are installed using the PXE boot, writing the bytes in the remote storage, configured and customized, and finally added to a (new) cluster in the hypervisor manager (e.g. vCenter).

All this process takes less then one hour: you could launch it and go to lunch, when you're back you'll find the cluster up and running. Compare it to a manual provisioning of the same server farm, eventually performed by a number of different teams (see the picture above): it would take days, sometimes weeks. 
Other use cases are simpler: maybe just creating a 3 tier application with VM and dedicated networks.

Once the automation workflow has been built and validated, it can be used by the IT admin or by the Operations everyday, to save time and ensure consistent outcome (no manual errors). But it can also be offered as a service to all the departments that depend on the IT for their projects. 

You can build a service catalog with enterprise features: multitenancy, role based access control, reporting, chargeback, approvals, etc. But you can also offer (secured) access to the API to launch the workflow, offering a degree of autonomy to your consumers. Eventually, using a resource quota: you don’t want everyone to be able to create dozens of VMs every hour if the capacity of the system can't sustain it. 

They will appreciate the efficiency improvement, for sure.

What's in it for me?

If you allow your internal clients to self serve, you will: 

  • get less requests for trivial tasks, that consume time and give no satisfaction (let them play with it),
  • be the hero of the productivity increase (no requests pending in your queue)
  • dedicate your time and skill to designing the architectural blueprint that will be offered as a service to your clients (so that everybody plays according to your rules)
  • use policy based provisioning, so that you define the rules just once and map them to tenants and environments: every deployment will inherit them
  • maintain control on resource consumption and system capacity, hence on costs and budget
  • increase your relevance: they will come to you to discuss their needs, propose new services, collaborate in governance

Example: network provisioning

The discussion above is valid for the entire infrastructure in the Data Center.
Now I tell you the story of a customer that implemented it specifically for the networking.

They were influenced by the trend about SDN and initially they were caught in the marketing trap "SDN means software implemented networking, hence overlay". Then they realized the advantage provided by ACI and selected it as the SDN platform ("software defined networking", thanks to the software controller and the ACI policy model).

Developers and the Architecture department asked to access the API exposed to self provision what they needed for new projects, but this was seen as an invasion of the property (see the picture with the dirty hands).

It would have worked, but it implied a transfer of knowledge and delegation of responsibility on a critical asset. At the end of the day, if developers and software designers had knowledge in networking, specialists would not exist.

So the network admins built a number of workflows in UCS Director, using the hundreds of tasks offered by the automation library, to implement some use cases ranging from basic tasks (allow this VM to be reached from the DMZ) to more complex scenarios (create a new environment for a multi tier application including load balancer and firewall configuration, plus access from the monitoring tools, with a single request).

3 tier application blueprint
Blueprint designed in collaboration with Security and Software Architects

Graphical editor for the workflows, with the tasks library
Graphical Editor for the workflow

These workflows are offered in a web portal (a service catalog is offered by UCSD out of the box) and through the REST API exposed by UCSD. Sample calls were provided to consumers as python clients, powershell clients and Postman collections, so that the higher level orchestration tool maintained by the Architecture dept was able to invoke the workflows immediately, inserting them in the business process automation that was already in place.

Example of python client running a UCSD workflow
Example of python client running a UCSD workflow

All the executions of the workflows - launched through the self service catalog or through the REST API - are tracked in the system and the administrator can inspect the requests and their outcome:

The IT admin can audit the requests for the automation workflows
The Service Requests are audited and can be inspected and rolled back

 Any run of the workflow can be inspected in full detail, look at the tabs in the window:

The IT admin can inspect any run of the workflows
The Admin has full control (see the tabs in the window)


Cisco UCS Director
Cisco ACI 
ACI for Simple Minds
ACI for (Smarter) Simple Minds
Invoking UCS Director Workflows via the Northbound API 

June 15, 2017

The best open source solution for microservices networking


There a big (justified) hype around containers and microservices.
Indeed, many people speak about the subject but few have implemented a real project.
There is also a lot of excellent resources on the web, so there is no need for my additional contribution.
I just want to offer my few readers another proof that a great solution exists for containers networking, and it works well.
You will find evidences in this post and pointers to resources and tutorials.
I will explain it in very basic terms, as I did for Cisco ACI and SDN, because I’m not talking to network specialists (you know I’m not either) but to software developers and designers. 
Most of the content here is reused from my sessions at Codemotion 2017 in Rome and Amsterdam (you can see the recording on youtube). 

Containers Networking

When the world moved from bare metal servers to virtual machines, virtual networks were also created and added great value (plus some need for management).

physical hosts connected to a network
Initially networking was simple

Of course virtual networks make the life of developers and servers managers easier, but they also add complexity for network managers: now there are two distinct networks that need to be managed and integrated.
You cannot simply believe that an overlay network runs on a physical dumb pipe with infinite bandwidth, zero latency and no need for end to end troubleshooting.

Virtual Machines connected to an overlay network
Virtual Machines connected to an overlay network

With the advent of containers, their virtual networking layered on top of the VM virtual network (the majority of containers run inside VM for a number of reasons), though there are good examples of container runtime on physical hosts.
So now you have 3 network layers stacked on top of each other, and a need to manage the network end to end that makes your work even more complex.

Containers inside VM: many layers of overlay networks
Containers inside VM: many layers of overlay networks

This increased abstraction creates some issues when you try to leverage the value of resources in the physical environment:
- connectivity: it's difficult to insert network services, like load balancers and firewalls, in the data path of microservices (regardless the virtual or physical nature of the appliances).
- performances: every overlay tier brings its own encapsulation (e.g. vxlan). Encapsulation over encapsulation over encapsulation starts penalizing the performances... just a little  ;-)
- hardware integration: some advanced features of your network (performances optimization, security) cannot be leveraged
Do not despair: we will see that a solution exists for this mess.

Microservices Networking

This short paragraph describes the existing implementation of the networking layer inside the containers runtime.
Generally it is based on a pluggable architecture, so that you can use a plugin that is delegated by the container engine to manage the container's traffic. You can choose among a number of good solutions from the open source community, including the default implementation from Docker.

Minimally the networking layer provides:
- IP Connectivity in Container’s Network Namespace
- IPAM, and Network Device Creation (eth0)
- Route Advertisement or Host NAT for external connectivity

containers networking
The networking for containers

There are two main architectures that allow to plug an external implementation for networking: CNM and CNI. Let's have a look at them.

The Container Network Model (CNM)    

Proposed by Docker to provide networking abstractions/API for container networking. 
It is based on the concept of a Sandbox that contains configuration of a container's network stack (Linux network namespace).
An endpoint is container's interface into a network (a couple of virtual ethernet interfaces).  
A network is collection of arbitrary endpoints that can communicate. 
A container can belong to multiple endpoints (and therefore multiple networks).
CNM allows for co-existence of multiple drivers, with a network managed by one driver   
Provides Driver APIs for IPAM and for Endpoint creation/deletion.  
- IPAM Driver APIs: Create/Delete Pool, Allocate/Free IP Address Network   
- Driver APIs: Network Create/Delete, Endpoint Create/Delete/Join/Leave  

Used by docker engine, docker swarm, and docker compose.  
Also works with other schedulers that runs standard containers e.g. Nomad or Mesos.

Container Network Model

Container Network Model


The Container Network Interface (CNI)


Proposed by CoreOS as part of appc specification, used also by Kubernetes. 
Common interface between container run time and network plugin.
Gives driver freedom to manipulate network namespace.
Network described by JSON configuration.

Plugins support two commands: 
- Add Container to Network 
- Remove Container from Network     

Container Network Interface

Container Network Interface

Many good implementations of the models above are available on the web.
You can pick one to complement the default implementation with a more sophisticated solution and benefit from better features.  

It looks so easy on my laptop. Why is it complex?

When a developer sets up the environment on its laptop, everything is simple.
You test your code and the infrastructure just works (you can also enjoy managing... the infrastructure as code).
No issues with performances, security, bandwidth, logs, conflicts on resources (ip address, vlan, names…).
But when you move to an integration test environment, or to a production environment, it’s no longer that easy.
IT administrators and the operations team are well aware of the need for stability, security, multi tenancy and other enterprise grade features.
So not all solutions are equal, especially for networking. Let's discuss their impact on Sally and Mike:

Sally (software developer) - she expects:
Develop and test fast 
Agility and Elasticity 
Does not care about other users

Mike (IT Manager) - he cares for: 
Manage infrastructure 
Stability and Security 
Isolation and Compliance 

These conflicting goals and priorities challenge the collaboration and the possibility to easily adopt a DevOps approach.
A possible solution is a Policy-based Container Networking.
Policy based management is simpler thanks to Declarative Tags (used instead of complex commands syntax), and it is faster because you manage Groups of resources instead of single objects (think of the cattle vs pets example).

What is Contiv

Contiv unifies containers, VMs, and bare metal servers with a single networking fabric, allowing container networks to be addressable from VM and bare-metal network endpoints.  Contiv combines strong network performance, support for industry-leading hardware, and an application-oriented policy that can move across networks together with the application.

Contiv's goal is to manage the "operational intent" of your deployment in a declarative way, as you generally do for the "application intent" of your microservices. This allows for a true infrastructure as code management and easy implementation of DevOps practices.


Contiv provides an IP address per container and eliminates the need for host-based port NAT. It works with different kinds of networks like pure layer 3 networks, overlay networks, and layer 2 networks, and provides the same virtual network view to containers regardless of the underlying technology. 

Contiv works with all major schedulers like Kubernetes, Docker Swarm, Mesos and Nomad. These schedulers provide compute resources to your containers and Contiv provides networking to them. Contiv supports both CNM (Docker networking Architecture) and CNI (CoreOS and Kubernetes networking architecture). 
Contiv has L2, L3 (BGP), Overlay (VXLAN) and ACI modes. It has built in east-west service load balancing. Contiv also provides traffic isolation through control and data traffic. 
It manages global resources: IPAM, VLAN/VXLAN pools.

Contiv Architecture

Contiv is made of a master node and an agent that runs on every host of your server farm:

Contiv and its clustered architecture
Contiv's support for clustered deployments

The master node(s) offer tools to manipulate Contiv objects. It is called Netmaster and implements CRUD (create, read, update, delete) operations using a REST interface. It is expected to be used by infra/ops teams and offers RBAC (role based access control).

The host agent (Netplugin) implements cluster-wide network and policy enforcement. It is stateless: very useful in case of a node failure/restart and upgrade.

A command line utility (that is a client of the master's REST API) is provided: it's named netctl.

Contiv and its cluster wide architecture
Contiv's architecture


Learning Contiv is very easy: from the Contiv website there is a great tutorial that you can download and run locally.
For your convenience, I executed it on my computer and copied some screenshots here, with my comments to explain it step by step.

First, let's look at normal docker networks (without Contiv) and how you create a new container and connect it to the default network:

Networks in Docker
Networks in Docker

You can inspect the virtual bridge (in the linux server) that is managed by Docker: look at the IPAM section of the configuration and its Subnet, then at the vanilla-c container and its ip address.

How Docker sees its networks
How Docker sees its networks

You can also look at the network config from within the container:

the network config from within the container

Now we want to create a new network with Contiv, using its netctl command line interface:

Contiv's netctl command line interface
Contiv's netctl command line interface

Here you can see how Docker lists and uses a Contiv network:

how Docker lists and uses a Contiv network

Look at the IPAM section, the name of the Driver, the name of the network and of the tenant:

We now connect a new container to the contiv-net network as it is seen by Docker: the command is identical when you use a network created by Contiv.

Multi tenancy

You can create a new Tenant using the netctl tenant create command:

Creating tenants in Contiv
Creating tenants in Contiv

A Tenant will have its own networks, that can overlap other tenants' network names and even their subnets: in the example below, the two networks are completely isolated and the default tenant and the blue tenant ignore each other - even though the two networks have the same name and use the same subnet
Everything works as if the other network did not exist (look at the "-t blue" argument in the commands).

Two different networks, with identical name and subnet, can exist in different tenants
Two different networks, with identical name and subnet

Let’s attach a new container to the contiv-net network in the blue tenant (the tenant name is explicitly used in the command, to specify the tenant's network):

All the containers connected to this network will communicate. The network extends all across the cluster and benefits of all the features of the Contiv runtime (see the website for a complete description).

The policy model: working with Groups

Contiv provides a way to apply isolation policies among containers groups (regardless of the tenants, eventually within the tenants).  To do that we create a simple policy called db-policy, then we associate the policy to a group (db-group, that will contain all the containers that need to be treated the same) and add some rules to the policy to define which ports are allowed.

Creating a policy in Contiv
Creating a policy in Contiv

(click on the images to zoom in)

Adding rules to a policy
Adding rules to a policy

Finally, we associate the policy with a group (a group is an arbitrary collection of containers, e.g. a tier for a microservice) and then run some containers that belong to db-group:

Creating a group in Contiv, so that many containers get the same policies
Creating a group

The policy named db-policy (defining, in this case, what ports are open and closed) is now applied to all the 3 containers: managing many end points as a single object makes it easy and fast, just think about auto-scaling (especially when integrated with Swarm, Kubernetes, etc.).

The tutorial shows many other interesting features in Contiv, but I don't want to make this post too long  :-)

Features that make Contiv the best solution for microservices networking

  • Support for grouping applications or applications' components.
  • Easy scale-out: instances of containerized applications are grouped together and managed consistently.
  • Policies are specified on a micro-service tier, rather than on individual container workloads.
  • Efficient forwarding between microservice tiers.
  • Contiv allows for a fixed VIP (DNS published) for a micro-service
  • Containers within the micro-services can come and go fast, as resource managers auto-scale them, but policies are already there... waiting for them.
  • Containers' IP addresses are immediately mapped to the service IP for east-west traffic.
  • Contiv eliminates the single point of forwarding (proxy) between micro-service tiers.
  • Application visibility is provided at the services level (across the cluster).
  • Performances are great (see references below).
  • It mirrors the policy model that made Cisco ACI an easy and efficient solution for SDN, regardless the availability of an ACI fabric (Contiv also works with other hw and even with all-virtual networks).

I really invite you to have a look and test it yourself using the tutorial

It's easy and not invasive at all, seeing is believing.

January 22, 2017

Hybrid Cloud and your applications lifecycle: 7 lessons learned

Hybrid Cloud is a must nowadays, I will not spend a word to convince you (you’d not be reading this post if you didn’t believe it). This is the story of a real project.

This post provides more context about the story I summarized at Just 1 step to deploy your applications in the cloud(s).
The structure of the post is:
  • Motivation
  • Use Cases
  • Time
  • Software Stack
  • Benefit of the architecture we implemented
  • Lessons Learned (the most important part)

Motivation for hybrid cloud, and most of the work in my customers' projects, include the following areas:
- Cost control (there is a strong debate: some swear it’s cheaper, others have discovered hidden costs: e.g. network traffic in production, after they made a business case just on the cost of VM provisioning).
- Governance model (IT must find a way to maintain control over resources usage, design patterns, compliance and security when application developers chose private cloud or public cloud).
- Mature technical solution: architecture and technology (there are many good products and system integrators in the market)

But, once you have made a decision, what will you run in the hybrid cloud?
Will your applications be spread across the boundary of your datacenter (one tier inside, other tiers outside)?
Or can we say that it is rather a multi-cloud deployment, where you have a number of resource pools that you can use as a target for deployments?

This project was made by a large corporation, to test how a hybrid cloud can be built and operated and to verify the impact on their current organization.
It is not a full production environment, it’s a pilot project that demonstrated on a small scale how easily you can build a software defined fully automated data center, including both resource pools from your local data center(s) and from public cloud providers.

The solution is expected to be cost effective, of course, but the greater benefits come from business agility and consistent governance.

Use Cases:

The evaluation was focused on 3 main use cases, all requiring that end users order the deployment of a complex software stack from a service catalog: the target for the deployment can be either the private cloud or the public cloud, or a combination of the two. These are the areas where the implementation demonstrated the value of the multi-cloud solution:
  • Business Intelligence (self-deployment of R Studio and additional tools)
  • ETL (self-deployment of a common software for ETL that data scientists would use in autonomy)
  • High Performance Computing (HPC) on OpenStack, with the integration of a DevOps pipeline.

Subject matter experts were provided from different lines of business in the company to support the implementation activities and evaluate the result. 
The use cases represent some frequent activities that the company needs in their usual business, especially in R&D. Improving efficiency and quality in the associated processes will have an impact on the overall business outcome. Applications were selected for the self service catalog that are deployed frequently (every week) and whose installation process takes time (some man days, accounting for both infrastructure and software setup), delaying business objectives.


All the activities in the project were delivered in time (six weeks), including the setup of the hardware and software systems for the hybrid cloud, the implementation of the 3 main use cases and some additional use cases, the functional tests and the stress tests. This is a demonstration that a proper selection of the technology and a good organization of the project allow for immediate return.
Challenges like setup of the remote access to the lab for remote experts, constraints in the networking and security configuration in the lab, some missing information about the process to install the applications (essential to build the model for the automation) slowed down the implementation. See Lessons learned.

Software Stack:

This is a complete end to end solution: its adoption will happen with a phased approach, starting from the components that grant an easy and immediate impact on the most critical business requirements and adopting some non-functional components later to complete the architecture. The extension from private cloud (based on any combination of VMware, other hypervisors and OpenStack) to a hybrid cloud (integrating AWS, Azure and more) was very quick (it is just a matter of configuration and definition of the governance model). Checkmarks in the picture show what we realized in the short timeframe of the project. The rest is part of a phased plan. The blue boxes show the components provided by Cisco.

a full solution for the hybrid cloud

The fundamental component in this architecture is Cisco CloudCenter (CCC), that has 2 main roles: 
- providing an orchestration solution that offers users the possibility to self-deploy complex software stacks from blueprints offered in a catalog, 
- brokering cloud resources from both private and public clouds (in the project we integrated VMware, OpenStack and AWS, but more clouds are supported).
CloudCenter manages the lifecycle of software applications in the cloud (at a level of abstraction where the underlying physical infrastructure does not matter).
The OpenStack use cases for HPC are supported by a Cisco Validated Design named UCSO: it includes a reference architecture for running the Red Hat OSP8 distribution on a certified hardware platform made of Cisco UCS servers and Nexus 9000 switches. The setup process and the operations are defined by the official deployment guide and Cisco's technical support assumes responsibility on the entire stack, including the Red Hat software.
The management of the entire DC infrastructure from a single orchestration platform was made possible by Cisco UCSD (UCS Director): a single dashboard and workflow engine to manage servers, network and storage, both physical and virtual. The status, the performances and the remaining capacity of all the systems were monitored with Cisco UCSPM (UCS Performance Manager).

Benefits of the architecture we implemented

The implementation of the multi-cloud solution demonstrated the major benefits that a hybrid cloud delivers.
  • A consistent architecture based on software (and eventually hardware) components that integrate easily and satisfy all the business and technical requirements.
  • All components in the architecture are loosely coupled and their integration is based on standard protocols and documented open API. As a consequence, every component can be replaced by an alternative solution (from a different vendor, from the open source, from a custom build) with no fear of vendor lock in.
  • The adoption of a hybrid cloud solution can happen gradually, starting with a core implementation with the most critical components (e.g. CCC, ACI and UCSO), adding more features as a second step (infrastructure automation and monitoring) with UCSD and UCSPM, eventually a unified service catalog and ITSM portal later.

Lessons Learned:

  1. use cases
  2. network topology
  3. security and trust
  4. reusable work (repositories and services)
  5. engage SME and business owners
  6. document
  7. refine (iterations, devops)

Use Cases 
The selection of the use cases is important. You need a quick return to demonstrate the value of the hybrid cloud: the adoption of the hybrid model should address immediate business needs, that the end users can appreciate, rather than be driven just by an industry trend. 
IT projects should not start because a new technology is very smart, but because the outcome makes the business easier and more productive.
Always engage your end users in the planning phase and avoid academic use cases that have a limited appeal on the decision makers. In this project we were lucky because the preparation was done by the steering committee very well in advance.
Once the models for the automation were ready, we could test any combination of the deployment for the application tiers: everything in the private cloud, everything in the public cloud, or the front end deployed on one side and the back end on the other side. The benchmarking capabilities of the product (CCC) allowed to compare the price/performances ratio of the different options based on vSphere, Openstack and AWS - specifically for each application, with tailored reporting.
Network Topology
A hybrid environment connects - by definition - areas that were designed separately (your datacenter and the public cloud). They have security policies and configurations that are not meant to work together, and this makes it difficult. Before you start the setup, dedicate the right time to collect all the requirements and to design the connectivity properly. 
We had some issues with the network proxies and the firewalls because of the protocols and ports that we needed to open to allow a proper integration of the Cloud Management Platform (running on premise) with the orchestration engine (with one instance running in each cloud region used in the project, to leverage the local API exposed by the cloud provider and to manage the lifecycle of the applications in the cloud). 

communication among the components of Cisco CloudCenter

Another important requirement is to have a unique repository for all the artifacts, the blueprints and the installation packages for the applications: it should be reachable from all the target clouds that you plan to use, regardless its location (it can be either in the private or in the public cloud, but all the servers you deploy will access it to stand up a new instance of the application). 
The same applies to any public repository that is used in the setup of the applications (both commercial software and open source components, e.g. packages installed using yum).
See also CCC Components Overview for more detail.

Security and Trust
It's important that a good level of trust is established between the architects building the hybrid cloud and the operations team, especially the security guys. Special rules and new policies need to be setup to allow the new platform to work, it's impossible to keep the same old governance model that addresses a single end user identity. 
Sometimes I feel like I'm living - again - the same conflict that I had with Database Administrators, when I tried to configure JDBC database connection pools in the first Java application servers in the 80's. The system should be trusted, and a delegation of the decisions (authentication, authorization and audit) accepted.

Reusable work (repositories and services)
When you model a software application to automate its deployment, you should identify any building block that can potentially be reused in a different model. If you create a reusable (parametric) deliverable and save it individually in a common repository, next time you'll have the work ready to be reused.
This applies to architectural building blocks like database servers, web servers, load balancers, firewalls, distributed caches, etc. 
If they have been created as separate services, instead of just being a part of a monolithic model, they will appear in your designer's palette everytime you model a similar application and you can drag and drop them in the topology. We did that in the project and we saved a lot of time in the implementation. 

Engage SME and business owners
It is important that subject matter experts (SME) collaborate at the definition of blueprints and the build of the automation model. Even though documentation exists for the deployment of the application, you should work together. 
The user knows all the requirements, he knows how to verify and troubleshoot, he has encountered all the setup issues already.
I've learned that the best way to document the setup process for an application, so that you can use it as a reference for the automation, is to ask the SME to install it in front of you in a clean environment where the application was never run, and record a video of the process. It's faster than writing documentation, more complete and reliable. We did that using the desktop sharing feature in Webex and we recorded the sessions.  

While you do the work, keep track of all the steps. Take (maybe informal) notes, but mostly take a lot of screen shots to document what you did. You can keep them on a wiki or on a shared folder, they will help a lot when you have time to create the formal documentation of the project. If you need to troubleshoot, eventually involving other people, this information will be unvaluable.
Of course, versioning and taking snapshots of all deliverables also helps in case you need to go back for whatever reason. 

Refine (iterations, devops)
Create the implementation for a minimum viable product (MVP) as soon as you can. Get the product (i.e. the entire self service catalog, or just the implementation of a single application blueprint) to early customers as soon as possible, to get their feedback before you go too deep in the implementation.
Applied to a hybrid cloud scenario, this will help to evaluate:
- quality of the service you are building, including documentation
- how much the users need it and use it in the real world
- performances of the distributed environment and any bottleneck (network, computing, configuration)
- security implications 
You will have all the time to make it perfect, through iterations that improve the implementation, collect feedback, allow for tuning the design and the configuration. No need to work in a hurry and make mistakes, while you keep your users waiting for the final "perfect" product but they don't see any progress.