This is a simple configuration example to show you the basics of integrating Ansible, Amazon Web Services CloudFormation, and F5’s AS3 declarative interface to create an ‘infrastructure-as-code’ BIG-IP implementation. It’s designed to give you the fundamental building blocks, and plenty of opportunity to extend and enhance things from here.

The architecture looks like this:

There is an Ansilbe host, simply a Linux server with Ansible and the AWS CLI tool installed , with a copy of the F5 Cloud Formation repository cloned onto it, and then a short and simple Ansible playbook which we can run. In an S3 bucket (because why not?) we have an AS3 declaration, again using a very simple configuration.

When we run the playbook, Ansible is going to use the F5 Cloud Formation Template (CFT) and data from the playbook to deploy and configure a BIG-IP, including AWS security group objects, etc. Part of the playbook data specifies a URL where the AS3 declaration is available and the post-install processes on the BIG-IP will uses this to pull down and apply the AS3 declaration.

The result is a running, BIG-IP passing traffic to some webservers, configured entirely by two small text files, with no scripting or deep BIG-IP knowledge required.

Although this configuration isn’t production-ready – it should give you the building blocks to create the infrastructure-as-code configuration.

Prerequisites

  • A working knowledge of AWS and a bit of F5.
  • An AWS account
  • An IAM User that has API access and the right permissions
  • A VPC
  • A subnet
  • A Webserver (or two)
  • An Internet gateway
  • An S3 bucket with public access enabled
  • An AWS Key pair
  • An evaluation BIG-IP registration key if you don’t use the Pay-As-You-Go (PAYG) option - either contact your friendly local spiderman F5 account team or request some here
  • Some sort of Linux instance with internet access (I used an Ubuntu EC2 instance – but whatever you are familiar with will probably work – although you will need to substitute the package installer commands of your OS where appropriate)

Getting Started

Enable AWS API Access

If you’ve use AWS primarily through the web console then first create an AIM user for API access and get the secret and key

https://docs.aws.amazon.com/IAM/latest/UserGuide/id_users_create.html#id_users_create_cliwpsapi

Make sure the AWS user has permissions (these are probably overkill but will work fine for a demo).

It’s best to create a group to include these policies and then assign the user to the group (or use roles, but this article isn’t about AWS API best practices.)

Configure the Ansible Host

Install and configure the AWS CLI:

$ sudo apt-get install awscli

Install Ansible and the Boto (AWS Python SDK) components:

$ sudo apt-get install ansible

$ sudo apt-get install python-pip

$ pip install boto3

$ pip install botocore

$ pip install boto

Use the API user’s access key ID and secret access key, along with the region your VPC is in, to configure the AWS cli:

$aws configure

AWS Access Key ID [None]: XXXXXXXXXXXXXXXXXXXX

AWS Secret Access Key [None]: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

Default region name [None]: us-west-1

Default output format [None]:

Check it works:

$ aws ec2 describe-instances

<some text describing your EC2 instances>

Clone the F5 AWS Cloudformation GitHub repository:

$ git clone https://github.com/F5Networks/f5-aws-cloudformation

Copy the template we are going to use to a ~/files directory – this isn’t really a necessary step for a production setup, and you could use environment variables etc. to get around long paths but it makes the playbook muc easier to read for this demo.

$mkidr ~/files

$cp /home/ubuntu/f5-aws-cloudformation/supported/standalone/1nic/existing-stack/byol/f5-existing-stack-byol-1nic-bigip.template ~/files/.

Set up a directory for your playbooks:

$mkdir -p ~/ansible/playbooks

Configuring an AS3 Declaration

Gather the IP address[es] of your target servers, and the address you intend to use for application traffic

Then use an example declaration  from the AS3 documentation – the declaration below is about as simple as it gets:

{
   "class": "AS3",
   "action": "deploy",
   "persist": true,
   "declaration": {
      "class": "ADC",
      "schemaVersion": "3.0.0",
      "id": "example-declaration-01",
      "label": "Sample 1",
      "remark": "Simple HTTP application with RR pool",
      "Sample_01": {
         "class": "Tenant",
         "A1": {
            "class": "Application",
            "template": "http",
            "serviceMain": {
               "class": "Service_HTTP",
               "virtualAddresses": [
                  ""
               ],
               "pool": "web_pool"
            },
            "web_pool": {
               "class": "Pool",
               "monitors": [
                  "http"
               ],
               "members": [{
                  "servicePort": 80,
                  "serverAddresses": [
                     "",
                     ""
                  ]
               }]
            }
         }
      }
   }
}

 

Create this as a text file and then create an S3 bucket to put it in, make sure there is public read access to the file, and get the URL for the file

Check that it’s accessible from an internet connected machine

$ curl <your URL>

<your AS3 declaration>

Configuring Playbook Variables

Using the method of your choice, gather the following information

  • VPC ID
  • Subnet ID
  • IP addresses to use for the BIG-IP
  • Source IP restrictions - at least for management access you should lock this down as much as possible.
  • The URL of your AS3 declaration
  • Your AWS SSH key pair name

 

Create the playbook in ~/ansible/playbooks, in my example I called it “bigip_as3.json “

---
- name: F5 Ansible CFT AS3
  hosts: localhost
  connection: local
  gather_facts: false

  tasks:
  - name: Run CFT
    cloudformation:
       stack_name: "f5-ansible-cloudformation"
       state: "present"
       region: ""
       disable_rollback: true
       template: "~/files/f5-existing-stack-byol-1nic-bigip.template"
       template_parameters:
          Vpc: ""
          subnet1Az1: ""
          subnet1Az1Address: ""
          imageName:  "LTMOneBootLocation"
          instanceType: "m5.large"
          licenseKey1: "XXXXXXXXXXXXXXXXXXXXXXXX"  
          sshKey: ""
          restrictedSrcAddress: ""
          restrictedSrcAddressApp: ""
          ntpServer: "0.pool.ntp.org"
          timezone: "UTC"
          declarationUrl: ""
       tags:
           Stack: "f5-ansible-cloudformation"

 

 

There are a number of parameters that the CFT referenced by the playbook needs:

 

Parameter Name

Required

Description

Vpc

Yes

Common VPC for the deployment.

managementSubnetAz1

Yes

Management subnet ID.

subnet1Az1

Yes

Public or External subnet ID.  Same as management in a single NIC deployment.

subnet1Az1Address

No

Optional. If you want to assign static IP address(es) in the subnet, type them here. Separate multiple IP addresses with a comma (the first is the Primary IP address, all others are Secondary). Otherwise leave DYNAMIC and a dynamic address is assigned based on the subnet you specified.

imageName

Yes

F5 BIG-IP Performance Type – see below for more infor

instanceType

Yes

Size for the F5 BIG-IP virtual instance (m5.large works fine for this)

sshKey

Yes

Name of an existing EC2 KeyPair to enable SSH access to the instance.

restrictedSrcAddress

Yes

The IP address range that can be used for management access to the EC2 instances.

restrictedSrcAddressApp

Yes

The IP address range that can be used for management access to the EC2 instances.

ntpServer

Yes

NTP server you want to use for this implementation (the default is 0.pool.ntp.org).

timezone

Yes

Olson timezone string from /usr/share/zoneinfo (the default is UTC).

owner

No

Owner Tag (the default is f5owner).

costcenter

No

Cost Center Tag (the default is f5costcenter).

declarationUrl

No

URL for the AS3 declaration JSON file to be deployed. Leave as none to deploy without a service configuration.

 

A note on imageName

The imageName parameter selects which BIG-IP image to boot from - you can find a list by exploring the CFT file and looking in the ‘mappings’ section. In our case as we are deploying a single NIC, LTM only configuration I selected “LTMOneBootLocation. Note you need the actual name from the template not the AMI  - e.g. LTMOneBootLocation not “ami-aa4ea2c9” , as the F5 CFT combines the imangeName with the selected AWS region to select the correct AMI.

 

Doing this with a free PAYG trial

Doing the same thing with a free trial is slightly easier a but has a small gotcha.

The template for the same implementation is in the payg directory:

~/f5-aws-cloudformation/supported/standalone/1nic/existing-stack/payg/f5-existing-stack-payg-1nic-bigip.template

 Copy it to your ~/files directory

Unfortunately, there is a limit of 51200 bytes for a CloudFormation Template in Ansible – if you run the playbook you’ll get an ugly error along the lines of:

“Member must have length less than or equal to 51200”

So we need to go do a bit of pruning on our CFT – I suggest removing the region AMI mappings for the regions you aren’t interested in:

e.g. find the mapping section of your CFT


"BigipRegionMap": {
   "ap-northeast-1": {
    "AdvancedWaf1000Mbps": "ami-f52e5318",
    "AdvancedWaf200Mbps": "ami-472855aa",
    "AdvancedWaf25Mbps": "ami-88334e65",
    "Best1000Mbps": "ami-65285588",
    "Best200Mbps": "ami-80334e6d",
    "Best25Mbps": "ami-7128559c",
    "Best5000Mbps": "ami-92334e7f",
    "Better1000Mbps": "ami-072f52ea",
    "Better200Mbps": "ami-062f52eb",
    "Better25Mbps": "ami-e62c510b",
    "Better5000Mbps": "ami-8e295463",
    "Good1000Mbps": "ami-63334e8e",
    "Good200Mbps": "ami-062d50eb",
    "Good25Mbps": "ami-b02f525d",
    "Good5000Mbps": "ami-0a2d50e7",
    "PerAppVeAwaf200Mbps": "ami-88cebc65",
    "PerAppVeAwaf25Mbps": "ami-d03f423d",
    "PerAppVeLtm200Mbps": "ami-233d40ce",
    "PerAppVeLtm25Mbps": "ami-7f3f4292"
   },
"ap-northeast-2": {
    "AdvancedWaf1000Mbps": "ami-0f05872fb7d0e05d1",
    "AdvancedWaf200Mbps": "ami-015506735d78469e2",


Delete entire regions you don’t need – that will bring the file size down to a limit that Ansible finds acceptable.

To make PAYG licensing works, you need to subscribe to the software. For the 200Mb Good instance, the subscription page is here:  

https://aws.amazon.com/marketplace/pp/B079C3WS75?qid=1553800192480&sr=0-1&ref_=srh_res_product_title#pdp-pricing

Click the ‘Subscribe button”

After that all you need to do is

  1. Remove the license parameter
  2. Change the imageName parameter  to “Good200Mbps”

 

Running the playbook

This is the easy part! We use the ansible-playbook command:

ansible-playbook <your playbook>

$ ansible-playbook ./bigip_as3_payg.json (my PAYG playbook)

or

$ ansible-playbook ./bigip_as3.json (my eval licensed playbook)

You can then follow the deployment by going to the AWS cloud formation console.

Once it’s up and running you can find the elastic IP address of your new big-IP instance from the cli or AWS console, and use your favorite SSH client and your SSH key to access the BIG-IP, change the admin password and view the configuration - remember AS3 declarations create their own partition.

Hopefully you’ll end up with something that looks like this

So there we have a BIG-IP booted and configured via Ansible using F5’s supported Cloudformation Templates and AS3 declarative API.

 

Deleting it

You can easily delete the stack using the AWS CLI:

$ aws cloudformation delete-stack --stack-name f5-ansible-cloudformation

Where can you go from here?

There are number of next steps from here – leave me a comment if you’d like to see a particular one

  • Make the example more robust and repeatable with environment variables and external file parameters
  • Include the backend server creation in the playbook
  • Explore service discovery – so you don’t have to know the IP addresses of the back end servers  - just their tag names
  • Add a security policy to the AS3 Declaration  - an BIG-IP ASM policy can be exported as XML and kept as a (non-editable) artifact in the same S3 Bucket
  • Integrate running the playbook with an orchestration tool like Jenkins