How to Visualize Your Cloud deployments - Cloudcraft

This is the second in a ‘series’ of posts highlighting tools I have found particularly useful for visualizing AWS and Azure, including:

In this post we will take a look at Cloudcraft.


Cloudcraft is an online diagraming tool that allows you to both create diagrams through a designer interface and also pull in live inventory from AWS via a secure connection. Cloudcraft is all in on AWS. In fact if you are doing a fair amount of work in AWS there is a good chance you are already familiar with Cloudcraft, but if not it is worth checking out. I liken their designer as a “Visio on Steroids” for AWS. The design below was built using the CloudCraft visual designer to illustrate a web app deployment on AWS.


Within the designer you can perform a search to highlight AWS components including a region, tag, or component name. Below we are highlighting all components in the us-east-1 region. This search could be refined for example to show all EC2 instances within the us-east-1 region tagged for production.


In addition to visualizing the deployment, Cloudcraft also offers a pretty impressive budget feature. This breaks down the anticipated cost and allows you to modify the design by exploring different compute, database, storage and networking sizes broken down by cost. When making changes within the budget view your design is automatically updated to reflect the updates. You can also export your design as a PDG for PNG, as well as share via a link to others on your team.


Once deployed, Cloudcraft offers a ‘Live’ mode as part of the professional subscription which allows you to discover and import your AWS inventory into the designer view. Below is the Cloudcraft visualization of the web application deployment highlighted in several of my Terraform posts.



  • Allows you to produce an architecture diagram without any need for deployment. After all, sometimes we just want to diagram things without actually deploying them.

  • The web interface is really spectacular. Cloudcraft in my opinion has the best looking 3D and 2D (with integration to diagrams, which I find useful for presentations, papers and web posts.

  • Pricing Breakdown: Cloudcraft is completely free for single users to design and save an unlimited number of private diagrams. This includes designer, cost calculations, design documentation and export. The Live features are included in the Pro version along with team collaboration and support which is currently listed as $49/month. For a complete pricing/feature break down check out CloudCraft’s pricing guide.

Nice to Haves:

  • The auto-layout within the ‘Live’ import can be a little clunky and sometime hard to manage. Based on some reading, Cloudcraft recognizes this and has start to improve their auto-layout algorithms.

  • Support only for AWS, it would nice to be able to see support for other clouds (Azure, GCP, etc.)

Remembering to Clean Up with Terraform

One of my favorite uses of Terraform is to quickly turn up an infrastructure environment with only a few lines of code. Of equal importance is the ability to tear down parts of the environment when they are no longer needed or need to be rebuilt. Terraform helps me leverage elasticity both in building, destroying and rebuilding as necessary.


If you are like me, you tend to forget things and need reminders. I have been building out environments now for some time in an automated way, but I am not always the best at remembering to tear them down when I am done. Don’t get me wrong, the act of tearing things down is easy with commands like terraform destroy, but remembering to do so is where I have a gap.

To close that gap I wanted to create a monitoring and trigger mechanism that would remind me when my infrastructure is running idle, and to go clean it up. Since many of my deployments are in AWS, the two tools I will leverage to accomplish this are CloudWatch and SNS. For those not familiar, CloudWatch is a monitoring and management service provided by Amazon that provides operational metrics on the health of a given environment. SNS is a notification service that allows you to send messages to a variety of endpoints - including SMS text messages which is a great way to remind me of doing things.

Incorporating Monitoring into My Build

Defining CloudWatch and SNS is relatively easy in Terraform as both resources can be defined using the Terraform AWS provider. Examples for both can be found on the Terraform website, and I have folded them both into a module I created on GitHub.

We will use these resources to monitor when the our autoscaling group goes idle, which I define as less then 2% CPU every minute for 5 minutes. When that occurs send a text message to the supplied phone number. To keep it simple the module accepts both the autoscaling group to monitor and the phone number to send messages to as variables. There is nothing preventing us from also defining the thresholds and polling intervals as variables as well, and in fact is something that we should probably do in the future to make the module more robust.

Using the Cloud-Watch Module

To make use of this module, we simply need to edit the file we have been using in development to include the cloud-watch module, which we will call from GitHub. We will pass the name of the auto scaling group created within the webserver_cluster module as an input for monitoring and prompt for the phone number to send the alert message to.

Now when we deploy our fleet there will be a two cloud watch alarms created against the deployed auto-scaling group. One that will report on idle time in a 5 minute window, and the other reporting on idle time in a 5 hour window. The idea being that if I missed one text message, I will get the second so that I can perform a terraform destroy to tear down the environment when it is not being utilized.

Now that I have included the cloud-watch module to my development file let’s initialize (terraform init), plan (terraform plan), and deploy (terraform apply).

Notification and Clean UP

I can see that it successfully created my alarm in CloudWatch and tied it to the auto-scaling group it created when deploying the fleet.

Output from running a terraform apply, listing the DNS name and autoscaling group of the sever fleet.

Output from running a terraform apply, listing the DNS name and autoscaling group of the sever fleet.

CloudWatch Alarm - Two were created, one for 5 minute intervals and the other for 5 hour intervals.

CloudWatch Alarm - Two were created, one for 5 minute intervals and the other for 5 hour intervals.

Now when the environment goes idle, an alarm will trigger and send me a text message. Should I not take care of it at that time, another text message in 5 hours will be send should the environment remain idle.

Text Message from AWS SNS notifying me that my auto-scaling group has had idle CPU for the last 5 minutes.

Text Message from AWS SNS notifying me that my auto-scaling group has had idle CPU for the last 5 minutes.

Since Terraform makes it easy to cleanup (terraform destroy), I will be sure to perform that step to not incur costs for unused assets and environments. Terraform destroy will be sure to cleanup not only the environment it deployed but also the alarms and SNS notifications it created during buildout.

This is part of a Terraform series in which we have covered:

Managing VMware Proactively with Runecast

As technologists, how do you troubleshoot problems? Does this cycle sound familiar:

  1. bad thing happens

  2. Google the error message

  3. land on a knowledge base/blog article

  4. fix the issue

  5. if not…. rinse and repeat.

I am sure we can all relate.

As admins/operators we are constantly pressed for getting things working in short order. This results in much of our time reactively troubleshooting issues, digging through logs and using Google to try to find a fix. While most of us have the desire to stay on top of issues and become more proactive - the reality is that it is an uphill battle.

I am sure we can all relate.

Runecast - For VMWare admins, built by VMWARE ADMINS

At VMworld this year I got to learn a little bit about Runecast during their Tech Field Day presentation. Runecast is a company focused on helping VMware admins with the task of keeping their environments healthy, secure and in line with published best practices. The company was founded by a set of practitioners who ran the VMware Center of Excellence for IBM, where they were in the trenches living and breathing VMware for many years. From these trenches arose the idea for product which would help tackle many of the common operational problems VMware operators face: combing through VMware knowledge base articles and logs, awareness and adherence to VMware best practices, security compliance checks, and spending too much time troubleshooting. A product was born to address these problems and it is called Runecast Analyzer.

Runcast Analyzer deploys as a single VM that aggregates information from a variety of sources including the VMware Knowledge Base, social media, and security hardening guides and synthesizes them into a central repository. Using this information, the analyzer runs a discovery against the environment to identify potential issues before they have the ability to cause outages. The technical details of how this is done is nicely explained in the white board/chalk talk presented by Runecast co-founder, Stanimir Markov.


Once deployed, Runecast Analyzer can be accessed via it’s web interface and presents several different views highlighting the health of your VMware environment. Runecast is not a performance and capacity alerting tool (as there are many of those available), but rather places its focus on configuration, manageability, security and VMware best practice conformance. The dashboard below shows the overall health based on those standards and allows you to drill down into items that may be of most importance in your organization.


Looking at the inventory view for critical items across this vSphere environment, it is easy to see a series of patches that should be installed on the vCenter and ESXi hosts. This details of this critical alert provides the relative risk rating, KB article reference, and resolution details for how to address the problem. Runecast does not currently provide the ability to take remediation action from within the web interface, but that is something that may be provided in the future.


Best Practices, Security Hardening, Compliance & VMware KBs

In it’s first iterations Runecast Analyzer was focused on analyzing configuration items contained within VMware Knowledge Base articles, best practices and security hardening guides. Recently the Analyzer has been expanded to include log analysis and specific security/compliance standards (DISA STIG and PCI DSS). This means that it can cross check against VMware logs for known issues, as well as call out items that don’t comply with specific security standards.

Below is a shot of the inventory view in which all items can be categorized, sorted and filtered based on what is most important including a categorization by product and impact. It is encouraging to see these new items added into the product, and I can envision additional sources and levels of analysis being included moving forward.


Try it Out

During the Tech Field Day presentation there was a cool demo of Runecast Analyzer which you should check out, but why not try it for yourself? Runecast provides both an online/interactive demo as well as a free trial of Runecast Analyzer for you to run in your environment. Also, if you happen to be a vExpert you can take advantage of their NFR offering. This was my first exposure to Runecast, and overall I would have to say I am highly impressed. This is a product for VMware admins, built by VMware admins and aimed at helping VMware admins move ever close to proactive management of their environments.

Disclaimer:  I was personally invited to attend Tech Field Day Xtra at VMworld 2018. I was not compensated for my time or travel.  I am not required to blog on any content; blog posts are not edited or reviewed by the presenters or Tech Field Day team before publication.

Creating A CloudMapper Virtual Appliance using Packer

One of my favorite visualization tools for diagraming Amazon Web Services (AWS) environments is Duo CloudMapper. CloudMapper helps you understand visually what exists in your AWS accounts by running a collection against the environment and providing an interactive web page. This is extremely handy for identifying possible network misconfigurations, along with a slew of other benefits. For a full listing why I like this tool check out my post on How to Visualize Your Cloud Deployments with CloudMapper.

Despite it’s power, one of the challenges I have found is to simply get it started and working. CloudMapper is open source built upon other open source products and I have found that there are inevitably build and dependency issues that suck up my time before I can simply use the tool. For these reasons and to make things easier in general, I chose to create and deploy CloudMapper as virtual appliance.

Building the Virtual Appliance

I utilized Packer to provision my CloudMapper virtual appliance. Packer is excellent for creating machine images for multiple platforms from a single source configuration. In this case we will build out an Amazon Machine Image (AMI) with Packer, which will take care of all package installation and dependencies for the build out. You can learn more about all the Packer goodness on the HashiCorp website and Paul Kirby provides a nice overview in his Packer PluralSight course.

  1. Install Packer

  2. Download the cloudmapper.packer template from my GitHub account. (Packer templates are simply JSON files that specify the various components used to create the machine image, and where the build of the image will be saved. In our case we will be creating and deploying our virtual appliance into AWS, but Packer comes with support to build images for Amazon EC2, CloudStack, DigitalOcean, Docker, Google Compute Engine, Microsoft Azure, QEMU, VirtualBox, VMware, and more.)

  3. Specify AWS Credentials for creating our virtual appliance. There are a number of ways to accomplish this but we will use environment variables.

       $ export AWS_ACCESS_KEY_ID="awsaccesskey" 
       $ export AWS_SECRET_ACCESS_KEY="awsecretkey"
  4. Build the image.

    $ packer build -var aws_region="us-west-2" -var ami_id="ami-6cd6f714" -var python_version="3.5.6" cloudmapper.packer
        # aws_region is where the image will be stored.
        # ami_id is the base Amazon Linux image in the region.
        # python version of your choice.

    There are currently some issues with CloudMapper and Python 3.7, so I am using the recommend version of 3.5.6

  5. The build process will take ~10-15 minutes as it needs to compile and pull down all of the components. Once it is complete, Packer will notify of your unique AMI that can now be used for deployment.


Deploying the Virtual Appliance

Now that the image for our virtual appliance is available in AWS, let deploy it and run CloudMapper. My preferred way to deploy would be using Terraform but for purposes of this post we will step through the manual steps.

  • Launch an instance using the newly created CloudMapper image. You can accept the defaults providing your instance a public IP with SSH access.


Configure CloudMapper by logging in via SSH and performing the final initialization steps. (While these could be automated and built into the image, I get sensitive about saving AWS credentials anywhere even if my image is private. I prefer to specify them when needed.)

  • $ aws configure

    You can specify a full access account to run CloudMapper but I like least privilege so have setup a “Visualization” IAM user with the privileges specified in the CloudMapper readme.

  • Configure CloudMapper’s account information in the config.json file to match aws credentials:

    $ cd ~/cloudmapper
    $ pipenv run python3 configure add-account --config-file config.json --name AWS_USERNAME --id AWS_ACCESS_KEY_ID
       #AWS_USERNAME is “friendly name” tied to IAM account   
       #AWS_ACCESS_KEY_ID is the AWS Access Key ID specified in aws configure.

  • Run CloudMapper’s collection against the environment. The collection phase can take some time, as it is truly pulling all the metadata information for your entire AWS account across all components and regions.

    $ pipenv run python3 collect --account AWS_USERNAME
  • Prepare the results and launch the webserver to display them.

    $ pipenv run python3 prepare --config config.json --account AWS_USERNAME
    $ pipenv run python3 webserver --public

  • Create and attach a security group to the instance to make the site publicly available.

  • Browse to public DNS address of your virtual appliance on port 8000

Please note that these steps show running this instance with a publicly available website. You can certainly deploy this to a private subnet and access through a bastion server, etc which is recommended. It would also make sense to put this site behind a login which I have noted as an opportunity for further improvement. Be sure to stop this instance when you are done using it.


Further Improvements

Having a readily available virtual appliance that just works is perfect, but there are some further improvements that I think would be handy:

  • Create a docker image of CloudMapper that can be run as a container. (There are some folks who have built this)

  • Save the collection data to an external volume so that it doesn’t live in the running appliance.

  • Create the virtual appliance that can be deployed within other Packer supported platforms, namely vSphere and Azure.

  • Lock down the website behind a username and password.