Tuesday, July 17, 2018

Cloud Governance: Making DevOps Automation Effective

I see cloud automation of all types implemented to control and/or secure cloud assets. Examples of this type of automation using Amazon Web Services (AWS) include the following:
  • Preventing unauthorized entries in security groups allowing ingress from (security)
  • Alerts for Creating IAM users (possible security risk)
  • Forwarding application logs to Splunk (operational effectiveness)
  • Scheduling up-time for non-production assets (cost savings)
While these examples are AWS-specific, the principles discussed in this article are equally applicable if you're using Azure, GCP, or another cloud. 

Some of these examples (which are real-life examples, by the way) were very effective. Some were only marginally effective. For instance, the first two examples were extremely effective. If I defined a security group allowing unauthorized access publicly, that entry in the security group was automatically removed and an alert issued to the security team. There was a process to add an "authorized" security group allowing public access, but I had to justify the need. As for the second example regarding IAM user creation, if I created an IAM user, an alert was generated and sent to the security team. The new IAM user wasn't automatically removed, but a similar alert was generated and I was asked to justify the action.

The example regarding forwarding application logs to Splunk was absolutely horrible. It took many hours to implement. It was very fragile; new version deployments to applications broke the automation. The coding needed to automatically fix forwarding after an application deployment was cost prohibitive. Consequently, logs were not reliably forwarded.

Scheduling up-time for non-production assets using the AWS Instance Scheduler is marginally effective. The solution works. However, it depends on instance tags for functionality. Instances scheduled must have required tags if up-time for them is to be scheduled. Often, developers are inconsistent in the implementation of these tags. This dependency makes the solution marginally effective for the customer as the tags are often not consistently applied.  This entire discussion leads to an obvious question.

What characteristics make DevOps Automation effective?

The characteristics that make DevOps automation effective can be attributed to the following characteristics:

  • DevOps automation must be resilient
  • DevOps automation must be easy to install
  • DevOps automation must minimize ongoing manual labor.
DevOps automation must be resilient to be effective. In other words, automation isn't effective if it breaks or doesn't work as intended when seemingly unrelated changes are made. Essentially, the benefit to the automation isn't realized. In the case of the fragile Splunk forwarding, broken forwarding meant that support could not trust the results of Splunk searches. Essentially, the benefit of having all application logs in one place was not realized. Developers had to go to the Cloudwatch source.

DevOps automation must be easy to install to be effective. Automation that's difficult to install is a classic "barrier to entry".  In other words, installation difficulty increases the effective price of the automation. Automation that's not implemented doesn't provide benefit.  The Splunk forwarding example was extremely difficult to install due to complexity as well as poor and inaccurate documentation. As a consequence, many teams didn't even attempt to implement the solution.

DevOps automation must minimize or eliminate ongoing manual labor to be effective. The purpose of automation is to eliminate manual labor. If the automation itself has manual labor requirements, that just negates some portion of the benefit. The AWS Instance Scheduler example requires manual labor in terms of tag maintenance. Consequently, the complete benefit of minimizing instance runtime costs is never realized. There's labor to pay for.

How do I use these principles to improve the automation I create?

Test your install documentation. Write your documentation and let somebody, not familiar with your automation, install it. If they have questions, it means that your documentation has a defect. It might be that your documentation wasn't clear. It might be that you missed documenting something. Whatever the reason, take those questions as defect reports.

Support your automation when it breaks or doesn't work as intended. If a consumer has trouble with your automation, help them fix. After you fix the issue, do a root cause analysis. Figure out why the automation broke and improve it to prevent that problem from repeating. It could be that your automation makes invalid assumptions about the environment. In which case, there is a code change to make. It could be that the user didn't understand how to properly use your automation. This can be fixed by improving your documentation. Whatever caused the issue, fix it.

Solicit feedback for ongoing care and feeding your automation requires. Ongoing care and feeding required by your automation detract from the benefits your automation provides. If there are changes you can make that further minimize or eliminate that ongoing work, you should consider it. 

Let's consider the AWS Instance Scheduler example that requires ongoing work with regard to documenting instance schedules as tags on the instances themselves. If we wrote that automation, how could we further minimize that ongoing work? One way I can think of is to provide a way to specify a default schedule for an entire account. Tags on the individual instances would be optional.

Many companies use different AWS accounts for non-production and production resources. If I could provide a default schedule for all EC2 and RDS instances in the entire non-production account, individual tags on individual instances would be optional. There would still be a need for tagging options on the instance themselves for resources that need a custom schedule. But the default schedule would apply to all instances in an account without developers remembering to place scheduling tags. A large percentage of the ongoing work required by the AWS Instance Scheduler would go away. With that, the amount of money the organization saved using the automation would increase.

Consider leveraging the single installation model. Most custom automation I've seen are written to work in their default region in the account in which they are installed. For example, if I install the automation in us-east-1 for account 1111111, that's the only place the automation is available. If I want to use that automation in other regions or other accounts belonging to the same organization, I need to install separate copies of that automation. 

It is possible to code automation so that it operates in multiple regions and multiple accounts. That said, the code within the automation would get more complicated and likely require cross-account roles. That automation would also require a centralized configuration where it runs. For example, the AWS Instance Scheduler supports the single installation model. If you use that feature, there's additional configuration to specify the accounts and regions the scheduler will operate in. Furthermore, cross-account roles are required to provide it access. Providing guidance on implementing the single installation model effectively is a separate topic and may be the subject of a future article.

I hope this helps you with the automation you create. Comments and questions are welcome.

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