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Orchestrate Landing Zone with Landing Zone Accelerator on AWS

Fri, 22 Sep 2023 23:05:04 -0400

As a continuation to the last post, we explore the Landing Zone Accelerator on AWS (LZA) as an orchestration tool in this post. LZA borrows a lot from the ASEA, an accelerator project to deploy the security reference architecture (SRA). LZA is a multi-purpose project that consists of both the orchestration engine (the accelerator itself) and a few reference architectures (as configuration files).

Comparison with Control Tower

First, let’s sort out how LZA is related to Control Tower. Control Tower’s main functionalities are available as an AWS service, with some customization capabilities available as a standalone solution on top of the service, as I discussed in the last post. Unlike Control Tower, LZA as a whole is a standalone solution. Luckily, the installation of the solution itself is highly automated.

I see LZA both as an extension of Control Tower, and as a complement to Control Tower. It is an extension of Control Tower because LZA can co-exist with Control Tower. We can configure LZA to enable Control Tower and use its Account Factory to provision new accounts (alternatively but not recommended, we can opt out of Control Tower and manage account creation on our own). I also see LZA as a complement to Control Tower because it comes with full end to end automation scheme for networking infrastructure and most of the services involved. This is missing in Control Tower, which leaves it with users to provision networking infrastructure in the customization.

Thanks to the infrastructure automation capability, even if you do not have a strong regulatory requirement, there are still good reason to go with LZA for its low-code automation capability. Below is a table that summarizes the differences:

Control Tower	Landing Zone Accelerator
– Multi-account management tool – Governance layer – Customization Framework to bring your own infrastructure automation	– can manage Control Tower – low-code automation engine for infrastructure automation and service deployment based on CDK – reference configurations based on common industry profiles and regulatory requirements

Comparison between Control Tower and Landing Zone Accelerator

As the name suggests, LZA is an accelerator so there is no expectation of its user knowing how to program infrastructure as code. However, it still expects its users to know YAML very well. Knowing how CloudFormation and CDK works can greatly help the users troubleshoot deployment.

Reference architectures in LZA

The input of LZA is configuration as code in YAML format. The LZA repository comes with a number of sample configurations to implement some industry-based best practices. The reference architectures currently include:

General best practices reference configuration: for clients other than the categories below;
Government customers: US Gov Cloud (FedRAMP compliant, on aws-us-gov partition), US State and Local Government, China (on aws-cn partition), Canada Federal (CCCS compliant) TSE-SE (Highly Trusted Secure Enclave Sensitive Edition) on commercial partition for governments, national security, defence, and law enforcement customers reference architecture;
Election: for election customers including elections agencies, committees and campaigns;
Healthcare: for healthcare customers. However, the document does not mention HIPAA compliance or anything related to the HIPAA Reference Architecture;
Finance and Taxation: for tax workload to secure Federal Tax Information (FTI) data;
Education: for education industry customers.

Many of these reference architecture shares a few common traits in the networking design. Take the CCCS reference as an example, the networking involves the followings:

Workload VPCs: consisting of a number VPCs for production and test environments;
Shared services VPC: hosting common services such as pipelines, Active Directories, etc
Endpoint VPCs: centrally hosting interface endpoints
Perimeter VPCs: acting as ingress, egress and inspection VPCs.

The Perimeter VPC hosts firewalls (either AWS Network Firewall or NGFW appliances behind Gateway Load Balancers). All the VPCs are centrally managed in an AWS network account, and are shared to other accounts using Resource Access Manager. The reference architecture document keeps the details of this architecture, which was derived from the security reference architecture (SRA).

Special Purpose VPCs

I consider the non-workload VPCs as special purpose VPCs. The shared services VPC is the most straight-forward. The Endpoint VPC is the most standardized. It is used to centrally host VPC interface endpoints for security and cost reasons. Unlike Gateway endpoint which is only available for S3 and DynamoDB, interface endpoint carries a standing charge and therefore should be consolidated. In addition, since interface endpoints are based on interfaces, we can centrally control the security group and interface policy.

To integrate the endpoint VPC, not only do we need to create those interface endpoint. We also need to account for routing (using Transit Gateway route tables) and name resolution. For name resolution, we need to create a Route53 private hosted zone for each DNS name, such as ec2.us-east-1.amazonaws.com and associate them with each workload VPC. Note that the interface endpoints DNS name may not always follow the same format. See the exceptions in my old post. Also note that this would create a lot of associations (between Private Hosted Zone for each Interface endpoint and each workload VPC). For example, 20 workload VPC with 30 private hosted zones will create 600 associations. To overcome this, use Route53 profile (introduced in April 2024).

Another special purpose VPC is the perimeter VPC. This VPC vary greatly between customers because of different requirement and historical preferences. One of the key design areas is the placement of NGFW, which is discussed in this post.

LZA Orchestration Engine

The installation process may feel complex at the beginning because we have to first install the pipeline to that installs the pipeline. The initial setup consists the following steps:

CloudFormation installs the installer. We start with a CloudFormation template to deploy the LZA installer itself. It deploys resources such as CodePipeline (AWSAccelerator-Installer) and CodeBuild project (AWSAccelerator-InstallerProject). These resources are in the INSTALLER circle in the diagram below;
The installer installs the accelerator core. In the LZA installer, the CodePipeline (AWSAccelerator-Installer) and CodeBuild project (AWSAccelerator-InstallerProject) drive the installation of the LZA. The input is the official LZA GitHub and we need a GitHub token for this step. The output is the actual LZA orchestration engine, including CodePipeline (AWSAccelerator-Pipeline) and CodeBuild (AWSAccelerator-BuildProject and AWSAccelerator-ToolkitProject). The user may specify their own GitHub repo as the configuration repo. Otherwise, a CodeCommit repo will be created. The LZA resources are shown in the CORE circle in the diagram below;
The acceleration core configures the landing zone. The LZA orchestration engine deploys actual resources in the landing zone, with the CodeCommit repo (aws-accelerator-config) or the specified GitHub repo as input.

If we enable Control Tower with LZA, we should first log in to management account and configure Landing Zone with Control Tower. we can also create (and register) the required OUs and accounts from Control Tower. Then we can deploy Landing Zone Accelerator with default configuration.

After the initial setup, we will need to iterate over the aws-accelerator-config repo. We implement our landing zone design in YAML configuration following the schema documentation. Changes in the configuration repo will trigger the pipeline (aka LZA’s orchestration engine) to redo step 3, whereas step 1 and step 2 are performed only once. The duration of step 3 is significantly longer than the first two steps.

Pitfalls

If LZA manages Control Tower, it expects existing OUs registered in Control Tower or it will report error. For account, LZA can create accounts listed in the manifest but not yet created. However, with the lengthy account vendor process for multiple account we run the risk of task time out in the pipeline.

During the installation, some account may run into quota limit. For example, the Networking Account usually have more than five VPCs whereas the quota is 5 VPCs per region per account. We need to increase the quota on those accounts.

The full deployment usually creates some SCPs. However, if we ever need to re-deploy a configuration, some steps steps might be blocked by certain SCPs. Attempts to temporarily detach SCPs from OUs, or modify SCPs often get reverted. The cause is an EventBridgeRule in us-east-1 region named RevertScpChangesModifySc. The rule should be disabled temporarily to perform the troubleshooting activity. We can do this with the following steps:

Disable the EventBridgeRule RevertScpChangesModifySc , which is only present in us-east-1 region;
Detach SCPs and note down what are detached, one OU at a time;
Go to the failed CF stack in the region, delete the failed stacks (after turning off termination protection);
Rerun the pipeline step from where it failed. This time it should go past the failure to the end, if SCP is the cause as per our assumption;
Re-attach SCPs. Suppose Security and Infrastructure OUs share one group of SCPs, and Dev, Test, and Prod OUs share a different group of SCPs;
Re-enable the EventBridgeRule;

Even with the EventBridgeRule RevertScpChangesModifySc disabled, when you re-run LZA deployment pipeline, the Accounts step will re-attach the SCPs using the AWSAccelerator-AccountsStack in the management account in us-east-1 region.

In general, how SCP works with organization structure is something to be very careful about, especially when the hierarchy consists of multiple layers of OUs. It is important to keep in mind, that deny statements in SCP take effect down the hierarchy, where as allow statements only affects the immediate child account of the OU where the SCP is attached to, as per the evaluation logic. As a result, an SCP with allow * statement (in the LZA-AWSFullAWSAccess managed policy) must be applied to Root, every OU at each level, and every account, for LZA to function.

In addition, there are some hard limits for SCP. Each SCP has a size limit of 5120 characters, and each OU can attach a limit of 5 SCPs.

Challenges

Powered by CDK, LZA automates the creation of a lot of resources. The configuration files uses the deploymentTargets attribute to allow users to specify to which accounts or OUs the declared resources will be deployed to.

Supporting many resources is a double-edge sword. Because the accelerator needs to go through every aspect of a landing zone, it is very slow to run. The accelerator pipeline may take as long as 40 minutes without any change to the configuration code. This is extremely slow if you just want to make some small changes in the configuration (e.g. update route table, add IAM role).

Even though LZA supports many resources, it’s not flexible with every resource. For example, today we can deploy IAM roles using RoleSet. However, in the trust policy of the IAM role you can only specify a two types of principals under the assumedBy attribute: account and service types. On the other hand a trust policy can support many other types of principals such as another IAM role.

AZ Mapping

Another important ability that LZA does not support is consistent AZ mapping across accounts. (Correction: this is now supported in LZA v1.5 as of Oct 2023). In some example LZA configurations, we deploy VPCs across multiple accounts using two or three availability zones referenced by their logical ID, such as us-east-1a and us-east-1b. However, AWS maps logical ID to physical ID and the mapping may be different in each AWS account. Using the same logical ID cannot guarantee the physical AZ are the same across account. As of LZA 1.5, the ability to reference physical ID in availabilityZone is supported in LZA configuration file.

Summary

I’ve spent a lot of time on LZA recently. It is extremely powerful. LZA streamlined the landing zone deployment process with configuration as code. It also allows users to customize their landing zone towards their own architectural needs and compliance requirement. For example, you can declare arbitrary SSM parameters in each account.

On the down side, the LZA deployment is time consuming through the pipelines. It tries to automate too many aspects of the infrastructure, which makes itself quite a complex project. Expect lots of changes in each new version.

The idea of being a low-code solution is to make it simple for end users but it often sacrifices flexibility. For example, if you want to create an IAM role in each new account that references the Management account ID, it is not possible until such feature is implemented in the accelerator. When the accelerator pipeline fails, it still requires deep CloudFormation knowledge to troubleshoot.

Orchestrate Landing Zone with AWS Control Tower

Sat, 19 Aug 2023 17:25:00 -0400

Following an introduction to AWS Landing Zone, I’ll dive deeper into Control Tower as an orchestration tool in this post.

More on Landing Zone

In data center operation, there are numerous tasks that other teams have to complete before the the deployment of an application. For example, the 42U server cabinet must be in place with dual powers. The cabinet comes with a network switch in the middle and each ethernet port must be provisioned. Once the server is connected to the central network, the NOC team assigns IP address, configure dynamic VLAN on the switches, and configures firewalls etc according to the connectivity requirement. The new server also needs to report to centralized monitoring solutions such as SolarWinds. In cloud operations, the scope and target of a landing zone is similar to those data center operations work, with networking being the core. The idea is that the landing zone ensures security, compliance and governance, so that applications (analogous to paratroopers) can focus on their primary responsibility.

Although Landing Zone is a general concept for any cloud service provider (CSP), each CSP has some prescriptive guidances on setting up landing zone in their particular environment. For example, an AWS prescriptive landing zone typically covers the following apsects:

Multi-account structure
Identity and Access Management
Governance (controls and guardrails)
Networking
Additional Security Services

The multi-account best practice is an important aspect in AWS as the OU structure dictates how effective Service Control Policies (SCPs) can govern the entire footprint. A landing zone orchestration solution should also apply guardrails and controls based on the organization’s security and compliance requirement. In addition, it is common expectation that landing zone orchestration solutions create required networking resources such that applications are ready to deploy securely.

This post is about Control Tower.

Intro to Control Tower

Control Tower is a landing zone orchestration solution available as an AWS service. I have three impressions over Control Tower. First, it is good with managing multi-account structure. It integrates closely with AWS Organizations and requires client to have a dedicated log archive account and a dedicated security tooling account, which also serves as audit account.

The second impression is Control Tower makes governance more straightforward. AWS seems to use the term control and guardrail interchangeably. Below is an illustration:

The detective controls only captures and reports violations. The preventive controls stops the violating API request. The proactive controls remediates the detected violations. Another way to look at the controls are the priority of the controls:

Mandatory controls: there are about 23 mandatory controls that are enforced on each account. As soon as a new workload account becomes part of Control Tower, these controls will come enforced.
Optional controls including strongly recommended controls and Elective controls.

There are many existing controls and AWS is still releasing new one (example). AWS Config service uses conformance pack to organize relevant controls. For example, there are sample conformance packs such as “Operational Best Practices for “NIST 800 53 rev4” and “Security Best Practices for EKS”. In addition, you can even build your own conformance pack. Once you deploy a conformance pack, AWS config deploys additional rules against the current environment.

The third impression is that Control Tower falls short with infrastructure automation. It provides a number of customization mechanism to leave infrastructure (mostly networking) automation with users. I will explain later.

Mandatory Accounts

The function of Control Tower does not depend on a specific account structure that aligns with the multi-account best practice. However it does require three mandatory accounts to set up automatically when creating control tower. The account where Control Tower is configured is the management account. The log archive account is the owner of S3 buckets that hosts buckets for loggings. The audit account is a restricted account that’s designed to give your security and compliance teams read and write access to all accounts in your landing zone. We also use this account as delegated administrator account for several security services at organization level. I’ll elaborate in the next section.

The management account is directly under root OU and there is no SCPs applied to the management account. Also Control Tower does not turn on AWS Config recorder and configure delivery channel on the management account itself.

The log archive account and audit account are under the Security OU (or otherwise named during the setup). At the end of Control Tower setup a set of mandatory controls will be applied at the OU level and thus effective to each account.

Apart from the mandatory accounts, oftentimes there is an Infrastructure OU. Typically we place shared services account under this OU. The account is for common services such as central networking, managed Active Directory, DevOps pipeline etc. Another optional OU is workload OU, where we can create new workload accounts or enrol existing workload accounts.

Each enrolled workload account have its CloudTrail configured with an organization-level trail, which sends log to the central bucket that log archive account owns. Each enrolled account also has AWS Config recorder configured with a delivery channel pointing to the configuration log bucket also owned by log archive account. Note that Control Tower does not turn on recorder on the management account itself.

Account Enrolment

Most likely a customer is already in use of AWS Organization. They can delegate one account as management account, and use Control Tower console to create a landing zone for the intended regions. During the creation, they will have to create two additional account, one as log archive account, and the other as security tooling or audit account. This is in alignment with multi-account best practices. After the creation of landing zone, the two new account are automatically enrolled in Control Tower.

Going forward, the client should create new accounts using Control Tower’s Account Factory to save a separate enrolment step. For existing accounts and OUs we’ll need to enroll them into Control Tower. At the beginning, the Control Tower Landing Zone (CTLZ) only has the mandatory controls, which are the bare minimum governance. When we enroll existing OUs and accounts, these controls (mostly detective and preventive) will extend to the newly enrolled OUs or accounts. Therefore, it is important to not introduce numerous controls prior to having all accounts and OUs enrolled. It is important to have a proper OU hierarchy upfront because both detective and preventive controls (Config Rules and SCPs) are typically applied at OU level and effective to all the children of the the OU.

To enroll an existing account, the account must meet some prerequisites. An IAM role with cross-account trust must be manually created. Since each account can have only one AWS Config configuration recorder and delivery channel, if an existing account already has one of them, they must be manually deleted using CLI.

Account Factory defines what happens when we create a new account and it is where we can bake in the customizations. When it comes to infrastructure automation, Control Tower leaves pretty much everything up to the account factory customization.

Delegated Administrator

Many AWS services are available as organization wide service. For example, CloudTrail, GuardDuty and even AWS Organization. Most of these organization level services allows you to specify one account as delegated administrator for the entire organization. For example, for System Manager and Service Catalog, the best practice is to designate an operation account or shared services account as the delegated administrator.

There are several security-related, organization-wide services, such as GuardDuty, Detective, Macie, Security Hub, Inspector, Audit Manager, and Firewall Manager. The best practices for these services is to designate the audit account as the delegated administrator. We think of the audit account as an aggregation point (or points for organizations that split the functionality across multiple accounts) for these AWS services. You can find this recommendation from the documentation of each service. However, currently Control Tower does not enforce this best practice. Even though Control Tower dashboard gives you visibility to security services such as GuardDuty and SecurityHub, you are still on your own to configure these services outside of Control Tower.

Whether you had never used these services, or you had previously designated another account as administrator for these services, it is good time to align with the best practice for delegated administrator right after Control Tower setup.

If you had previously designated other accounts, you need to take additional steps to revoke the old delegation and designate new delegated administrator account. These general steps can be performed from console or CLI and apply to all these services:

From the old administrator account, remove all the members
From the management account, revoke delegation
From the management account, delegate new administrator account (audit account)
From the new administrator account, invite all the members, and make sure to tell the service to automatically include new accounts going forward

Note that when you invite an account, that account must not have created, invited or associated with other accounts (as a member). In other words, we should never let a non-delegated administrator account create, invite or associate other account as member.

Control Tower Customization

Control Tower by itself does not play a big role in networking automation. You can create a VPC when provisioning a new account in Control Tower. That’s about what you can do. It does not address how the new VPC connects to existing networking space. It does not distinguish the existing VPC topology. To address this, Control Tower has a few customization options:

	AFC Account Factory Customization	AFT Account Factory Terraform	CfCT Customizations for Control Tower
Supported IaC language	Anything that Service Catalog Support (e.g. CloudFormation and Terraform HCL)	Terraform HCL	CloudFormation
Readiness	AFC is a native mechanism. You can specify the Service Catalog product during creation of an Account in Control Tower console.	Users need to first bootstrap the solution, which creates Terraform pipelines and other components.	Users need to first bootstrap the solution which creates a pipeline and other components.
How it works	Control Tower launches the specified Service Catalog product (aka blueprint) during account creation.	Quite complex. Explained in the section below	Control Tower posts a lifecycle events to Amazon EventBridge. A lambda function will process the event using pre-baked CloudFormation templates and Step Functions.
Prebuilt customizations	No. However, for certain products, there are some partner-maintained blueprints	Yes	No

AFC is fairly straightforward to use based on this post, as long as you know Service Catalog. It is a native capability of Control Tower. The other two ways for CT landing zone customization are not native capability and each require a separate bootstrapping process to deploy the pipeline-based solution.

Customizations for Control Tower

In CfCT, we first bootstrap the solution from management account, using this CloudFormation template on GitHub. We usually need to version control the custom configuration so we should select “AWS CodeCommit” as the value for the CodePipelineSource parameter. The bootstrapping (execution of this CloudFormation template) takes about 5 minutes.

The solution is based on Control Tower’s lifecycle event to trigger the pipeline with a source code repository (or S3 bucket). The source code repository (AWS CodeCommit) stores the custom configuration. A custom configuration consists of a manifest file in YAML format, made up with a number of resource sections. Each resource section references an artifact, either a CloudFormation template or a policy file to apply. The artifact can either be stored in the same repository, or in a remote S3 bucket.

Account Factory Terraform

Introduced in late 2021, the AFT solution is based on the terraform-aws-control_tower_account_factory repository. It is too complex as a solution in my opinion. At the beginning you need to have a designated OU and account for AFT and install the solution itself with Terraform. Note that, the solution does not address the state storage of the installation of the solution itself (chicken-or-egg). The installation also installed a few IAM roles required on the AFT management account. Having a landing zone management account and a AFT management account is not a neat setup.

Once installed, the solution consists of four repositories. Their purposes are as follows (copied from workshop instruction):

Account requests – handles placing or updating account requests. See example here.
AFT account provisioning customizations – manages non-Terraform customizations that are applied to all accounts. This stage runs before the global customizations stage. Examples available here
Global customizations – Global customizations – manages Terraform-based customizations that are applied to all accounts created by and managed with AFT. Examples available
Account customizations – Account customizations – manages Terraform-based customizations that are applied only to specific accounts created by and managed with AFT. Examples available

With this solution in place, when we create a new account via account request repo, quite a number of steps will happen after that, as the workshop instruction illustrated:

When I first looked into how this work I’m very concerned about the maintainability of this “solution”. The amount of services and pipelines involved in this solution makes it difficult to troubleshoot end to end. To improve that AWS even added an enhancement for request tracing. Nonetheless, I would not go with AFT just with how complex it looks like. For more details, check out the Control Tower workshop.

Summary

Control Tower helps you set up a Landing Zone without network infrastructure automation. It provides a couple of customization mechanisms, allowing you to bake in your own infrastructure automation. To build a landing zone, you can go with Control Tower in the following situations:

you have general regulatory requirement and need to enable governance capabilities
your networking stack isn’t centrally managed or isn’t large enough to warrant infrastructure automation
If you do need networking automation, then you will need the customization capability of Control Tower. In this case, your team should have good handle on infrastructure as code (CloudFormation or Terraform)

Out of the customization options, AFC seems the least complex to me. The other two, especially AFT, is too complex. We use pipelines to trail and error with other stacks, and the pipeline solution itself must be simple.

On the other hand, if you environment has strong regulatory requirement, or you are seeking a prescriptive network architecture, or your team does not have the capacity with CloudFormation or Terraform Templates, consider Landing Zone Accelerator on AWS.

Landing Zone in AWS – An Introduction

Thu, 08 Dec 2022 22:37:00 -0400

Cloud adoption has gone through phases. Hashicorp’s CTO Armon Dadgar has a great stream on Hashcorp’s narrative of the three Phases of Cloud Adoption:

Phase 1, with main focus on agility, app teams in wild west, account sprawl, inconsistent configuration, security & compliance challenge
Phase 2. consistent platform layer providing an opinionated way of configurations, security and compliance control, introduction of platform team, whose customers are application team, scaling challenges with platform team capacity not keeping up with application team’s demand
Phase 3. self-service platform at scale for many application teams. CICD, infra-as-code

New greenfield clients today should aim at phase 2 or phase 3 depending on their target operation size. Either way, they need a landing zone for consistency. This post focuses on the landing zone options on AWS and the key constructs.

Overview

AWS Prescriptive Guidance defines landing zone as a well-architected (secure, scalable, compliant, etc), multi-account AWS environment that is a secure baseline from which you can deploy workloads and applications. A landing zone may consists of:

Multi-account architecture
Identity and Access management solution
Governance, compliance, logging and auditing solutions
Security and networking design

Landing zone reflects an enterprise opinion on how to configure networking and IAM. Establishing a landing zone can be a very involving process. In early days of cloud operation people used build landing zone by “clickops” on AWS console, which cannot keep up with the growth of landing zone and associated security services. Compared to SMB clients, some sectors such healthcare and national security have much more regulations and compliance frameworks.

In fact, landing zone is such a buzzword that I have learned to be very sensitive to the context. If a solution has words “landing zone” in its name, given the complexity and loose use of the words, there is a good chance that the solution only delivers some of the aspects above. Cloud consultants are still to address the gaps. Therefore I decided to write this post about what I learned about landing zone in AWS.

Multi-account and Organization

Since 2017, AWS has been officially advocating the use of multiple accounts as a best practice and security strategy. They encourage clients to view an account as a resource container, just like Resource Group in Azure (here is an article on the account structure between AWS and Azure) Client builds an account for security boundary and financial container. Many blog post came along (such as this one) on the implementation details. From tooling perspective, AWS launched AWS Organization in 2017 to facilitate multi-account management. This presentation from re:Inforce 2019 is a good material to understand multi-account environment with AWS Organizations. In 2021, AWS published a new white paper on best practices with multiple accounts. My previous coworkers authored two blog posts here and here to reflect multi-account setup as of late 2020.

AWS Organization is an account management service that provides a vehicle to centrally manage AWS accounts by groups, which brings many benefits, such as centralized logging, compliance management, consolidated billing, etc. As an administrator, you can create accounts in your organization and invite existing accounts to join the organization.

Typical account structure involves:

A root organization named Root
A organization hierarchy with one or more OUs under root, with each OU having one or more child OUs.
Each OU can have multiple accounts, with each account having one email address.

You can also configure service control policies (SCPs), a type of organization-level policy that you can use to manage permissions in your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization. SCPs are a means of implementing guardrails in your AWS organization.

To vend multiple account automatically, AWS introduced the Account Factory (which became part of Control Tower later in 2019) and let users create new account from AWS console and specify which OU it belongs to. It also allows users to implement customization after account creation with Service Catalog products. Some legacy orchestration solution (e.g. Augmented Account Factory) were based on this mechanism. Another orchestration solution prior to the launch of Control Tower was the AWS Landing Zone solution (ALZ, introduced in 2018), which uses AWS CodePipeline to provision accounts and deploy resources.

From Landing Zone Solution (ALZ) to Control Tower

The launch of Control Tower was a game changer in 2019 to bring users to multi-account best practices. Control Tower, as an AWS product (instead of a solution by some service teams at AWS) matured over several years. It gradually deprecated the previous generation orchestration solutions. Newer orchestration solutions all have to support Control Tower. According to this page, we can customize Control Tower based Landing Zone in these ways:

AWS Control Tower console:
- Instead of creating OU in AWS organization, do it under Control Tower in the console, under “create required OUs”.
Outside of AWS Control Tower console
- using Account Factory for Terraform (AFT): Terraform-based account provisioning pipeline, for heavy Terraform shops.
- using Customizations for AWS Control Tower (CfCT) solution. The LZ created remains in sync with Control Tower.
  - You first launch a standardized CF stack to set up the mechanisms for customization.
  - Then you create a custom package to define the customization. This includes a manifest file.

On the other hand, the original ALZ solution is currently in long-term support and will not receive any additional features. It is deprecated. AWS advises its customers migrate to AWS Control Tower based landing zone. The ALZ page is redirected to a page about customizing Control Tower landing zone.

The Control Tower based landing zone configures OUs, accounts, SSO and guardrails. It however does not entail networking design except for the guardrails.

Security Reference Architecture (SRA)

SRA is just a reference architecture and accompanying recommendations on AWS security services, and how they work together in a multi-account environment to host a single-page application. It comes with a repository to demonstrate how to configure a secure multi-account environment with Control Tower, CfCT, as well as security services. Alternatively, you can deploy it with CloudFormation StackSets. For fully automated deployment of this architecture, check out Landing Zone Accelerator down below.

One important feature that reflects the notion of centralized security in a multi-VPC topology, is the use of a dedicated VPC for centralized interface endpoints. This pattern is seen in all generations of security reference architectures.

AWS Security Environment Accelerator (ASEA)

Regulated customers often find they need to add additional controls and capabilities to be defined and setup outside of Control Tower. ASEA, as an orchestration solution, aims to remove the complexity of having to develop and maintain a separate codebase to manage the additional customizations, by providing a tool to help deploy and operate secure multi-account, multi-region AWS environments on an ongoing basis.

Overtime, as Control Tower introduces new capabilities to support the customizations required in heavily regulated environments, the capabilities will be removed from ASEA and enabled directly within the Control Tower managed service, further reducing operational burden. Read this for its relationship with ALZ and Control Tower.

ASEA was first released in late 2020. It covers more on networking design and has a fairly comprehensive installation process. ASEA primarily cater to government of Canada‘s PBMM Security Configuration Profile with an opinionated configuration. The first few revisions were referred to as PMBB architecture. While it provides a great reference architecture for highly regulated landing zone, it has not gotten much traction elsewhere. The recommendation going forward, is to use Landing Zone Accelerator, which incorporates the features and lessons learned from ASEA.

Landing Zone Accelerator (LZA)

First released in May 2022, the Landing Zone Accelerator on AWS solution deploys a cloud foundation that is architected to align with AWS best practices and multiple global compliance frameworks. LZA operates on top of Control Tower managed landing zone. This page in the documentation brings a good explanation of how it works. Basically it employs CodeBuild as an orchestration engine, and leverages CDK to drive resource deployment. There are two repositories: the GitHub repository for Landing Zone Accelerator itself, and a CodeCommit configuration repository provisioned during preparation.

As document states, LZA is a fully automated implementation of the architecture guidelines documented in the SRA. LZA also incorporates features and lessons learned from ASEA and Compliant Framework for Federal and DoD Workloads in GovCloud (US), neither of which are recommended for new deployment. In addition, LZA aims to enable iterations and extensions of a secure environment over time. The vision is to eventually replace AFT, CfCT, and ASEA. As AWS releases newer versions of LZA, client should be able to upgrade it in a pipeline run.

Baseline architecture for LZA

The Landing Zone Accelerator project also provides a samples configurations in each regulated frameworks. For example, the healthcare best practice come up in Oct 2022 for healthcare industry. The healthcare best practice sample incorporates healthcare specific configurations, such as the detective guardrails defined in the Operational Best Practices for HIPAA Security conformance pack. To deploy the best practices, modify the configuration in config repo, and run the pipeline again.

Landing Zone Orchestration Options

To summarize, we have the following landing zone options in AWS:

Orchestration Layer	Lifecycle	Summary
Solutions based on account factory with Service Catalog (e.g. ALZ, Augmented Account Factory)	All solutions pre-dates the launch of control tower have been or will soon be deprecated.	Pre-Control Tower solutions should migrate to current alternatives.
AWS Control Tower	GA in June 2019	To customize the landing zone, use AWS console, or alternatively, one of the following three options: – CfCT: Customization for Control Tower – AFT: Account Factory Terraform – AFC: Account Factory Customizations For example, CfCT can be used to deploy Security Reference Architecture (SRA)
AWS Secure Environment Accelerator (ASEA)	Released 2020	Canadian Centre for Cyber Security (CCCS) Medium Cloud Control Profile, formerly known as PBMM. This approach will be replaced by LZA
AWS Landing Zone Accelerator (LZA)	Released in 2022	A low-code deployment option. Samples provided in support of reference architectures that align with industry best practices or compliance frameworks. Examples for industry best practices: – general best practice – healthcare – finance and tax – education Example for compliance frameworks: – US state local government – FedRAMP for US Federal and Department of Defence (DoD) – CCCS Medium for Canadian government – AWS Trusted Secure Enclave (TSE) Sensitive Edition (SE), which also aligns with other medium level security profiles such as NIST 800-53, ITSG-33, FedRAMP moderate, CCCS-Medium, IRAP, etc

If your organization has a compliance framework that LZA supports, it makes sense to start with LZA. Otherwise, it is sufficient to use Control Tower for multi-account setup.

Networking constructs

Landing zone involves multiple VPCs so it’s important to understand VPC peering and Transit Gateway. VPC peering can only be setup between two VPCs and transitive peering relationship is not supported. For 6 VPCs to talk to all each other, we’d need 15 peering setups, which is not sustainable. We have two options:

We can connect many VPCs to a single Transit Gateway and the VPCs will be able to talk to each other. We can also connect Transit Gateway to site-to-site VPN or Direct Connect. This is a good explanation.
We can still use peering, but dedicate one VPC as “Transit VPC” in a hub and spoke model.

When you attach a VPC to a transit gateway, you must specify one subnet from each AZ to be used by the transit gateway to route traffic. Specifying one subnet from an AZ enables traffic to reach resources in every subnet in that AZ.

Here is a comparison table. Read the white-paper “Building a Scalable and Secure Multi-VPC AWS Network Infrastructure” for more about network design. For example, Some clients need to inspect traffic. When deploying multiple VPCs, we also need to ensure DNS resolution works across VPCs, and between on-prem networks, by configuring Amazon DNS server.

Our VPCs also need to connect to managed AWS services. For services like S3 or DynamoDB (of the same or different account), workload in VPC can access them via public DNS. The traffic goes through Internet Gateway of VPC and then public Internet. It is neither secure nor economical. We often want all network traffic to stay on the global AWS backbone. There are three types of Endpoint under VPC to help us.

	Gateway VPC Endpoint or Gateway Endpoint	Interface VPC Endpoint or Interface Endpoint	Gateway Load Balancer Endpoint
Purpose	VPC access native AWS services	VPC access native AWS services	VPC access your own service (aka Endpoint Service)
Enabled by Private Link	No	Yes	Yes
Traffic remain on AWS network	Yes	Yes	Yes
Mechanism	Use the public IP address of the service along with configuration in routing table to access target resource. The routing table acts as gateway. You can use the public DNS name of the service.	Use private IP address from the VPC to access the target service. Require endpoint-specific DNS name for the target service. Incur extra charge. S3 as example. More secure because there is ENI in the VPC controlled by security group.	Your Endpoint Services is hosted in front of a fleet of network virtual appliances. You can select endpoint type as you create it in console under VPC
Access from clients on premise or from other region	Does NOT allow access from clients on premise or in VPC from other region	Allows access from clients on premise or clients in VPC from other region	Allowed

Private Link integrates with a subset of AWS services. To check the list of interface endpoint, use:

aws ec2 describe-vpc-endpoint-services --query "ServiceDetails[?ServiceType[0].ServiceType=='Interface'].ServiceName"

Given the inter-VPC connectivity, most deployment centralizes interface endpoint into a dedicated VPC.

Centralized Interface Endpoints

There are several benefits to use a single VPC as dedicated provider of interface endpoint in a multi-VPC topology. First, the interface endpoints incurs a standing charge and it makes financial sense to consolidate them in one VPC. Second, this setup centralizes the configuration and security aspects as well.

In most cases, interface endpoint services follow the format of com.amazonaws.<region>.<endpoint>, with the dns name looking like: <endpoint>.<region>.amazonaws.com. For example:

Endpoint Service Name: com.amazonaws.us-east-1.ssm
Private Hosted Zone: ssm.us-east-1.amazonaws.com
Zone Record (A alias): ssm.us-east-1.amazonaws.com

However, there are a few exceptions to that which can make it tricky to implement interface endpoint with infrastructure as code.

Exception 1. Private DNS name suffix is api.aws instead of amazonaws.com:

Endpoint Service Name: com.amazonaws.us-east-1.eks-auth
Private Hosted Zone: eks-auth.us-east-1.api.aws
Zone Record (A alias): eks-auth.us-east-1.api.aws

Exception 2. Endpoint Service Name doesn’t start with com.amazonaws

Endpoint Service Name: aws.sagemaker.us-east-1.notebook
Private Hosted Zone: notebook.us-east-1.sagemaker.aws
Zone Record (A alias): notebook.us-east-1.sagemaker.aws

Exception 3. Two A-records are required under the same PHZ

Endpoint Service Name: com.amazonaws.us-east-1.ecr.dkr
Private Hosted Zone: dkr.ecr.us-east-1.amazonaws.com
Zone Record (A alias): dkr.ecr.us-east-1.amazonaws.com and *.dkr.ecr.us-east-1.amazonaws.com

Exception 4. Two PHZs are required:

Endpoint Service Name: com.amazonaws.us-east-1.ec2
Private Hosted Zone: ec2.us-east-1.amazonaws.com and ec2.us-east-1.api.aws
Zone Record (A alias): ec2.us-east-1.amazonaws.com and ec2.us-east-1.api.aws

The infrastructure code that handles interface endpoints should be able to take care of the exceptions.

Security Constructs

AWS Network firewall is a configuration under VPC. You associate a Network Firewall with one or more subnets in the VPC. You also associate the Network Firewall with Firewall Policies. Each policy consists of rule groups. Each rule blocks or filters traffic. The log can be published to CloudWatch or S3 via Kinesis. Usecases of AWS Network firewall include:

inspect VPC-to-VPC traffic;
filter outbound traffic;
prevent inbound internet traffic;
secure AWS Direct Connect and VPN traffic

When you associate a firewall to your VPC, you must provide a subnet for each Availability Zone where you want to place a firewall endpoint to filter traffic

There are many points of configuration for firewall rules in AWS. AWS Firewall Manager is a place for central management. It connects with other services such as:

AWS WAF (L7)
AWS Network Firewall (L4)
AWS Shield (DDos)
Amazon Route 53 Resolver DNS Firewall
Security Groups
Third party firewall support

IAM Identity Center (previously AWS SSO) is for logging into AWS portal, giving each identity from an external identity store (such as AD) an identity on an AWS account. It addresses several challenges as a result of having multiple AWS accounts.

First, a user from AD needs to access multiple accounts. Second, in each account with access, s/he needs to have an IAM role. These IAM roles are based on attributes of his/her external identity, and can be different per AWS account. With Permission Set IAM Identity Center:

Allow you to login on different AWS account using the same credential
Allow you to federate with external identity store( e.g. using SAML)
Manage permission set for each account.

This video has a good walk-through of how to configure Azure AD as identity store, and tie it to specific AWS accounts and specify permission set.

Note that IAM Identity Center has a different purpose than Cognito. Cognito is to connect your app with an IdP. Your app can be an EC2-hosted application, serverless application on API gateway, or container-based application on Kubernetes, as long as the support open identity standards (e.g. OAuth 2.0, SAML 2.0 and OIDC). Cognito has two pools:

User pool for authentication. With a user pool, your app
Identity pool for authorization

So Cognito is for AuthN & AuthZ to your own app’s endpoint (business traffic) and it supports a number of standards. IAM Identity Center is for AuthN & AuthZ to your AWS account (management traffic). If you use it for your own application, your application user will have direct access your AWS resources. It mainly supports SAML (and OIDC but less used) and is commonly connected with Active Directory (Microsoft or AWS).

One can use AWS Directory Service to host a compatible and managed directory service. AWS Directory Service supports four modes:

AWS managed Microsoft AD: actual Microsoft Active Directory in AWS Cloud
Simple AD, powered by Linux-Samba Active Directory-compatible server
AD Connector: a proxy for redirecting directory request to your existing Microsoft AD without caching any information in the cloud
Cognito user pools

If you combine two AD domains, you will need domain trust.

Other security services

In addition to the core services above, LZA involves the following four services as well.

AWS Macie is a fully managed data security and privacy service based on ML and pattern matching. It continually evaluates your S3 environments to discovery PII and act on them. It also reports alerts on unencrypted buckets, publicly accessible buckets, etc.

AWS GuardDuty continuously analyze S3, container & instance workloads, user and accounts for potential threads, across account, based on ML for intelligent threat detection. In addition, GuardDuty also acts on findings.

AWS Config is an essential service that traces resource inventory, their changes and monitors for compliance:

When you turn on AWS Config, it first discovers the supported AWS resources in your account and generates a configuration item (poin-in-time view of attributes) for each resource. AWS Config also generates configuration items when the configuration of a resource changes, and it maintains historical records of the configuration items of your resources from the time you start the configuration recorder.
On an ongoing basis, AWS Config keeps track of all changes to your resources, whether or not it is initiated by the API
If you are using AWS Config rules, AWS Config continuously evaluates your AWS resource configurations for desired settings. You can deploy several related rules in a pre-built compliance pack.

As you may have noticed, there are many AWS services related to firewall, security and compliance. AWS Security Hub aims to be a a consolidated view of your security status in AWS. You can automate security checks, manage security findings, and identify the highest priority security issues across accounts in client environment. It can also:

consolidate security findings from GuardDuty, Inspector (vulnerability scanner and management), Macie, Config, Systems Manager, Firewall Manager, IAM Access Analyzer and other Integrated APN solutions
Check findings against best practices
Client can take action: i.e. investigate findings or take response and remediation actions

Summary

Landing zone deployment can take numerous iterations to finalize the requirement. It is important to have a vision of the client’s cloud operating model, which is usually discussed prior to implementation. At the implementation phase, I discuss the topic on two more separate posts on control tower and landing zone accelerator.

AWS CDK example in Typescript – provision an AWX server

Sat, 19 Dec 2020 17:14:00 -0400

This post provides an example of using AWS CDK in Typescript.

Ansible Tower and AWX

We have used open-source Ansible extensively in the past. While the automation is convenient, the lack of UI makes it not as suitable as a team collaboration tool. One way to allow team collaboration with open-source Ansible, is to use Jenkins to glue the components together, as discussed in the Automated Deployment Pipeline series. In this setup, the open-source Ansible remains command-line driven, with Jenkins building up the command, rather than a human user. There are many upsides in this configuration, but it is not built specifically for Ansible. Ansible is agent-less, and can be run from any host. This sounds appealing and can work well in smaller server fleet. However, since it requires some configuration on the controlling host for Ansible to function properly, it become unnecessary to configure Ansible environment on every single host (e.g. production). A typically environment only has Ansible environment configured on the bastion host. This brings the need for a dedicated controller server to drive all Ansible tasks.

Ansible Tower is Red Hat’s commercial enhancement to the open source Ansible, providing web-based console, REST API and other services such as Role-based Access Control (RBAC). Managing Ansible via REST API is still somewhat involving but this also enables other open-source contributions to simplify the use of API. For example Tower CLI allows you to use Ansible Tower with simplified command. Ansible Tower has an open-source upstream project called AWX, maintained by Red Hat. AWX is essentially a preview release of Ansible Tower without commercial support. AWX can serve as an engine for all Ansible related task. AWX server is essentially an Ansible control server. AWX, or Ansible Tower, also brings several concepts on top of Ansible:

Job template: defines how an Ansible playbook should be executed, including details such as machine credential, project, inventory, and playbook file.
Job: the actual execution of job template
Project: connects Ansible Tower to source control such as BitBucket. It is tied to a Git repository and a branch within that repository

To deploy AWX on EC2 instances, there is a reference deployment by AWS. However, it is provided as CloudFormation template and appears to be outdated (from 2018). In our project (late 2020, named ansible tower lab, or dubbed as “atlab”), we provide the infrastructure in AWS CDK (written in typescript), to provision the AWX environment.

The goal is that once the configuration is completed, you can run ansible ping against a target EC2 instance. The steps are as automated as possible. However, a number of key steps are purposefully left manual for learning purpose, such as the installation of AWX on EC2 instance.

Infrastructure as Code

In previous posting, I created infrastructure as code in AWS CDK with Python, so I decided to change to typescript in this project, with the assumption it is just a matter of syntax mapping. However, I underestimated the transition to a new language I never learned before. A fuzzy understanding of little details such as when to use let a=4 vs this.a=4, may produce elusive errors that takes hours to troubleshoot. I would therefore strongly recommend reading the basic syntax guide for typescript, before getting started.

In typescript, this example project provides an implementation of configuring autoscaling groups, including cloud init, user data, etc on AWS. Other than the language, everything else is very similar to the project in this post, which was developed in Python. Also, note that the project directory structure varies slightly based on the language being used.

If you are absolutely new to AWS cdk, start with this app. It is beyond the scope of this post, to cover extensively the installation and environment configuration of AWS CDK.

In the provision process for Bastion host, the cloudformation init script pulls a specific version from AWX repository, then makes slight modification. User will need to install it manually. Note that AWX can be installed on three types of platforms:

OpenShift
Kubernetes
Docker Compose

All are documented in their README file. For simplicity in this project, the installation is on standalone docker compose. This is the default mode so there is no need to modify the inventory file.

The code repo

The repository is version controlled here. To run the project, you need to have aws cli environment, then install the required packages including node js, and npm packages such as aws cdk. Once configured, validate with command:

cdk ls

This should display the stacks available. Use cdk deploy to deploy each stack.

When BastionStack is deployed, dependent packages should be installed with user data and cloud init. You will just need to SSH on to the server to manually install AWX, as explained in the instruction, to manually install AWX:

ansible-playbook -i inventory install.yml

Then you can browse to the server (at port 80 by default). Before the log-in page for the first time, the AWX will upgrade itself, with the following screen presented:

Now log on with default credential (in README.md), you will have the UI for AWX:

AWX Web Console

From here, you can edit inventory by adding the host. Or use the helper script (~/awxcompose-helper.sh) from bastion host to create a new inventory (named Private Instance Inventory), and populate it with the hosts in the stack. The helper script does so by querying aws resource, and isssue rest API calls to AWX. After executing the script, you can see a new inventory, and the Private instance inventory should contain all hosts in the stack:

Automatically populated inventory

You can then run Ansible ping against the host to validate connectivity. Note that during inventory creation, the ansible_user is already set to ec2-user (by the helper script):

Ping result

Some technical details

The initialization process on Bastion host creates an RSA key pair, stores the public key to AWS, for the upcoming private instances to uses. It keeps the private key locally in order to make outgoing SSH connection to the private instances. To ensure connectivity between AWX and private instances, there are a couple of (bash) helper scripts involved. Both reflects some technical details that I had to work through.

awxcompose-helper.sh: the initialization process pulls AWX installation file from git repo. The installation process will build a docker-compose file in ~/.awx/awxcompose, based on a template (~/awx-*/installer/roles/local_docker/templates/docker-compose.yml.j2). When user tells AWX to connect to private instance, the connection was made out of a docker container (instead of from the OS of bastion host), we need this script to map SSH key file from host to container, by modifying the template file. Without this helper, outgoing SSH connection will fail with error (Permission denied (publickey,gssapi-keyex,gssapi-with-mic)). This script is invoked in the cloud init process without requiring manual execution.
awxinvt-helper.sh: once the private stack is up and the installation has completed, we need to add the hosts to AWX inventory. This script gets the instance ID and IP addresses of the private instances, and uses Rest API calls to create inventory and populate it with hosts. Ansible has multiple ways of authentication. This script uses the non-stateful basic authentication with each curl command requiring credential. Ansible Rest API guide is provided here and be wary of the convention where URI must end with a slash.

This project is just a start of AWX on AWS CDK project using Typescript. In real life scenarios, there are some work to do to make this even more automated. For example, use cfn-hup service to monitor changes of private stack, and therefore update inventories accordingly.

AWS CDK example in Python – provision Kubernetes Nodes

Thu, 03 Dec 2020 21:14:00 -0400

There are two mechanisms to initialize instances in AWS. Cloud init and CloudFormation Init. Both are widely used and we discuss each of them in this posting. Then we will give an example of using AWS CDK in Python.

Cloud-Init

Cloud-Init is a service originally built for Ubuntu, as a bootstrapping utility to customize a Linux VM as it boots for the first time. It has evolved to be an industry standard multi-distribution method for cross-platform (public or private) cloud instance initialization, or even bare-metal installation. In cloud-init you can install packages and write files, or configure users and security. Because cloud-init is called during the initial boot process, there are no additional steps or required agents to apply your configuration.

Cloud-Init uses UserData, which is part of instance metadata. With AWS, you can pass two types of user data to Amazon EC2: shell scripts and cloud-init directives.

The cloud-config files are text files encoded in base64, with more details covered in the documentation here. cloud-init also works across distributions. For example, you don’t use apt-get install or yum install to install a package. Instead you can define a list of packages to install. cloud-init automatically uses the native package management tool for the distro you select.

CloudFormation Init

The cloudformation init mechanism does not only initialize instance, it also provides a mechanism for the resource being created to communicate with other resources. It allows an instance to emit signal to a different resource (via cfn-signal). It can also monitor changes to external resource and invoke local action (using cfn-hup with hooks). CloudFormation Init requires several components to work together:

The cloudformation resource should have metadata. The metadata must have a key AWS::CloudFormation::Init in which configsets are declared.
The UserData must use helper script (cfn-init) to invoke configuration jobs
The UserData can use helper script (cfn-signal) to signal with a CreationPolicy or WaitCondition (of the same or different resource), so you can synchronize other resources in the stack when the prerequisite resource or application is ready.
The cfn-hup service on the instance can be configured, to check for updates to metadata and execute custom hooks when changes are detected.

Comparison

While there are overlaps between the functionalities of Cloud Init and CloudFormation Init, the major difference is the latter support extended features (signal, update, etc); whereas the former is vendor neutral. The table below summarized some the differences:

	Cloud Init	CloudFormation Init
Works on	Linux OS distribution	CloudFormation resource, in combination with cfn helper scripts
Usecase	Initialization only	Both initialization and resource update
Trigger	cloud-init systemd service	Initial: from UserData Update: by cfn hook
Adoption	Multiple cloud vendors and bare-metal system	AWS cloud instances
Action Playbook	/var/lib/cloud/ Instance Metadata -> User Data, encoded in base 64	CloudFormation Resource -> Metadata section -> AWS::CloudFormation::Init -> configSets and configs
Log file and stdout	/var/log/cloud-init.log /var/log/cloud-init-output.log	/var/log/cfn-init.log /var/log/cfn-init-cmd.log

Comparison between cloud-init and cfn-init

AWS Cloud Development Toolkit (CDK)

Traditionally, AWS CloudFormation uses template in YAML or JSON for resource declaration. As the size of system grows, the amount of resource involved grows quickly and the size of such declaration file may grow beyond manageable. Nested stacks and export of output are mechanisms designed to combat the template sprawling, but to a very limited extent. Two reasons it is hard to control template size are:

In declarative statements, each line carries very small piece of information. Without flow controls such as if-else, loops, object oriented structure, the level of code reusability is very low;
Some auxiliary resources (such as AWS::EC2::VPCGatewayAttachment) must be declared explicitly, even though they are insignificant to the stack functionality

To address these challenges, AWS introduced AWS CDK (cloud development tookkit), which supports multiple languages (JavaScript, TypeScript, Python, Java, and C#). The CDK was natively developed in TypeScript, which is supposed to be the preferred development language. A tutorial is provided here, with detailed API documentation here.

To install aws cdk and create a hello world project, follow this example.

An Example in Python

I have created an example for AWS CDK in Python. The purpose is to create some EC2 instance to complete a lab for Kubernetes (without using managed EKS service). The example provisions the followings:

VPC, a public and private subnets, Internet and NAT gateways;
Relevant security groups and permissions
Bastion host, public instances in public subnet
Private instances in private subnet, with public route through NAT gateway

The private instances forms a cluster for Kubernetes lab. We will use kubespray to initialize these instances. During the bootstraping, we download kubespray, install ansible, etc.

Here is the code repo for this example. With CloudFormation only, the single template could go well beyond 1000 lines. With CDK, the code are organized into several different python files, each representing a stack. The stacks can be stood up with command:

cdk deploy vpc-stack
cdk deploy security-stack
cdk deploy bastion-stack
cdk deploy private-stack

Although the documentation in Python is available, there are generally not a lot of examples built out on the Internet. The pypi site provides some Python specific examples for each module (e.g. core and aws-ec2). Given these libraries are available for only 2 years (since 2018), many advocates TypeScript as the language. However, I have implemented some CloudFormation init, used helper script, and UserData in this example, without running into any language specific issues.It should be noted that the EC2 instance by default will call cfn-init. So there is no need to explicitly run cfn-signal or cfn-init from user data in python code (example). This can be verified in file /var/lib/cloud/instances/<instance-id>/user-data.txt which automatically includes the following lines:

# fingerprint: e1b32ead13878deb
(
 set +e
 /opt/aws/bin/cfn-init -v --region us-east-1 --stack bastion-stack --resource bastionhost5F466975da9934ba490de456 -c config_set_1,config_set_2
 /opt/aws/bin/cfn-signal -e $? --region us-east-1 --stack bastion-stack --resource bastionhost5F466975da9934ba490de456
 cat /var/log/cfn-init.log >&2
)

In addition to Python, AWS CDK also supports other languages. In the next post, we will discuss use of CDK in Typescript.

Automatic deployment of Orthanc on AWS

Sun, 08 Nov 2020 00:54:06 -0400

[Update] I changed reverse proxy from Nginx to Envoy. Here‘s the detail.

[Update] Some security improvement was introduced in may 2021. Here‘s detail.

[Update] Here’s the link to the orthweb repository.

In this project we introduce a medical imaging web service based on Orthanc, an open-source project of DICOM server, and a pipeline to deploy such server automatically and consistently. We deploy Orthanc on AWS automatically. This little project involves a number of technical deets in DevOps, to deliver a web application prototype with an automated deployment pipeline.

A brief on imaging

In medical imaging, scanning devices are the data collectors. It consists of various categories of scanners, such as Computed Tomography (CT), and Ultrasound (US). They are collectively referred to as modality, but vary significantly in terms of image generation and hardware manufacturing. The challenges to exchange data between these heterogeneous scanning devices and centralized computers came around as early as the 1980s, which brought about ACR-NEMA standard in 1985, under the initiative between American College Radiology (ACR) and National Electrical Manufacturers Association (NEMA). The standard lately evolved into DICOM (Digital Imaging Communication in Medicine), a comprehensive set of standard in the ISO framework that governs modern imaging data storage and exchange across several disciplines (radiology, cardiology, pathology, etc) that operate around images in medicine.

In addition to defining a file format to store imaging data, DICOM also includes an upper layer protocol that dictates how two compliant devices (referred as application entity, each identified by AE title) can negotiate a common syntax to transfer objects (e.g. an image, a report or a discovery). Upper layer refers to layer 5-7 in OSI model, or application layer in TCP/IP model.

Imaging server

Once scanner acquires images from patient, they stores the exams to imaging server for persistent storage. The functionalities of such server expands overtime since 1990s and hence go by different names in different eras, such as PACS (Picture Archive and Communication Systems), VNA (Vendor Neutral Archive) and EI (enterprise imaging) archive. Regardless of naming, they can be generally seen as a highly specialized variation of enterprise content management system. They are usually hosted with a centralized database to index clinical information at patient, exam and image levels. The other key component is the persistent storage devices, usually in the form of a NAS.

Orthanc is an open-source initiative for such imaging servers. It provides a DICOM endpoint, allowing scanning devices to store medical images. It also provides a web viewer allowing users to see the images stored. It is released for many platforms, including Docker images.

Infrastructure as code

We use Amazon Web Service (AWS) for infrastructure as service, and Terraform as the tool to provision resources off AWS, in a reliable and consistent mechanism, known as Infrastructure-as-Code. Terraform is an alternative to CloudFormation, AWS’s proprietary infrastructure-as-code technology. Terraform is developed by Hashicorp as an open-source project, and therefore is vendor neutral. It supports multiple public cloud vendor through different providers. Each provider accesses the vendor specific SDK. For example, the AWS provider integrates with AWS SDK. As a result, the code used in one vendor cannot just be applied to a different vendor without a major overhaul. Terraform’s current version is 0.13 as of Oct 2020, and has gone through some syntax changes since version 0.11. Terraform also produces files for state management locally in the working directory.

When executing, Terraform combines all files in the working directory to assess variables, and create required resources. It is compatible with the most of AWS resources. For example, you can specify user data with templates when creating EC2 instances. You can also create managed service instance as long as it is supported by the provider.

Architecture

Orthanc web server stores data in sqlite by default, but also has a plugin to support PostgreSQL, an open-source relational database. AWS has managed service (RDS) based on PostgreSQL. In this project, we create an RDS instance that span across two availability zones for minimum high availability. Orthanc also supports storing imaging data including pixels in PostgreSQL, which obviates the need for a dedicated file storage system.

We deploy the application in Docker’s containers for compatibility and portability. The Orthanc server is shipped in Docker images, available in Docker hub registry. The docker environment is configured as part of EC2 instance bootstrapping, including installing packages with YUM, initializing and customizing environment variables. The docker-compose file, and the auxiliary configuration files are provided in the repo. The bootstrapping script installs git and pulls required files from this GitHub repo.

This demo project does not include load balancing, DNS management, or container orchestration.

Security

Orthanc’s web browser natively supports HTTPS. However, the DICOM port does not support TLS natively, as their development has made clear in the FAQ. This leaves a severe security vulnerability because all patient data (protected health information in HIPPA context) would be sent across the Internet in the clear, visible to every network interface along the route. To address this issue we brought in Nginx as a reverse proxy to work at TCP layer to terminate encrypted traffic for Orthanc’s DICOM end point. DICOM upper layer works on top of TCP layer.

In Nginx literature, this use case is referred to as SSL Termination for TCP Upstream Servers. Note that Nginx is providing layer 4 capability in this use case so the certificate and key configuration should not be placed under http section of the configuration file. This layer 4 capability in fact enables security configurations of all protocol that operates in upper layers and can be used in a broad range of situations. It is also noteworthy that Nginx can re-encrypt the traffic on the way out to upstream, for even tighter security control measure as outlined in this use case.

user nginx;
worker_processes 1;

error_log /var/log/nginx/error.log warn;
pid /var/run/nginx.pid;

events {
 worker_connections 1024;
}

stream {
 upstream dicom_backend {
 server orthanc-backend:4242;
 }

 server {
 listen 11112 ssl;
 proxy_pass dicom_backend;

 ssl_certificate conf.d/site.pem;
 ssl_certificate_key conf.d/site.pem;
 ssl_protocols SSLv3 TLSv1 TLSv1.2 TLSv1.3;
 ssl_ciphers HIGH:!aNULL:!MD5:ECDH+AESGCM;
 ssl_session_cache shared:SSL:20m;
 ssl_session_timeout 4h;
 ssl_handshake_timeout 30s;
 }
}

It is also helpful to use Nginx to terminate HTTPS traffic, using a pair of certificate and key. When testing with self-signed certificate I realized that Chrome browser has specific requirement on self-signed certificate, or it won’t load the page. So the certificate has to be created as instructed here.

For better security, it is advisable that the RDS instance is provisioned in private subnet, with its data encrypted both in-transit and at-rest. Docker service should also manage sensitive information as secrets.

Summary

The deliverable is stored in this Github repo. The docker part of it can be executed on MacBook with PostgreSQL. The entire hardware stack represented by terraform code, can be executed against AWS to create required resources. Checkout README for further instruction. To emulate a modality, one will need a TLS supported DICOM application entity, Horos is a great project on MacOS to serve this purpose, both as DICOM-compliant sender and a viewer. Alternatively, consider some command-line based DICOM toolkit such as dcmtk, or grassroot dicom.

AWS Certified DevOps Engineer Exam Tips

Sat, 17 Aug 2019 23:07:59 -0400

The last 30 days have been exhausting for me. I studied hard on the new AWS Certified DevOps Engineer exam and thank goodness I passed (750 out of 1000 is required). This was the hardest professional certification I ever worked on. The exam was re-launched recently in March 2019 so there is still a shortage of information around the community. I was hoping to share my experience to help demystify this new exam.

I had a somewhat solid background to begin with, having taken the AWS Certified Solution Architect Professional exam (before the 2019 update), and worked quite a bit on CloudFormation, automation and Git. However, I still did not anticipate the exam to be this difficult until I was halfway through and already had the exam and materials paid for.

Without clear guideline on study material other than the white papers, I first skimmed through the ACloudGuru course, which helped me form a high level sense of exam coverage. Nonetheless it does not cover any topic in-depth and therefore by no means makes an essential part of my study. I checked out LinuxAcademy course and they are much more in-depth for the major topics indeed. I like the course material in Lucid chart. However, the LinuxAcademy course along does not cover everything you need to know.

What I found extremely helpful is the free training videos from the official training website. I strongly recommend the 7-hour course Exam Readiness: AWS Certified DevOps Engineer – Professional. The instructor did a great job outlining the services and knowledge areas in the assessment . The course also comes with quality sample questions with answers and explanations on what the thinkings are behind the correct answers or why some choices are obviously wrong. I went through these questions twice and feel much better at not only understanding the question, but also understanding the intent of the question.

Apart from the Exam Readiness course, other free introductory courses from the official training website are helpful as well especially for those services that you only need to know the basics. Most of those courses are 5 ~ 10 minutes long, with brief but sufficient introduction and a demo session. The other extremely helpful resource is the official practice questions. The practice exam is harder than the actual exam but they closely resemble the actual question style in the exam. Unfortunately, no answer is provided but they made me spend time finding answers across the documentations and blogs. It is worth-noting that the AWS blogs provides plenty of use cases that are covered in the questions.

When it comes to the real exam, it covers a lot more topics than its predecessor. Many questions are long and confusing. And I wish I could run a diff command to highlight the differences between choices. During exam preparation you really need to train yourself on reading efficiently. I found myself sometimes eyeball through all four choices at the same time, which get my mind scattered.

During my study, I divide all services into three categories based on the level of familiarity, and here is my list:

Category 1. Know these services very well, in and out:

AWS ElasticBeanstalk, OpsWorks Stacks, OpsWorks Chef Automate, CloudFormation, CloudWatch, CodeBuild, CodeCommit, CodeDeploy, CodePipeline, CodeStar, Lambda, API Gateway, Config, Trusted Advisor, CloudTrail, Systems Manager, Autoscaling Group in EC2, DynamoDB

Category 2. Know these services well, but not necessarily down to every single detail:

Amazon Kinesis Firehose, Kinesis Analytics, Kinesis Streams, Step Functions, Elastic Load Balancer, Secrets Manager, Serverless Application Model (SAM), Route53, RDS, Certificate Manager, ElasticSearch, ECS, ECR

Category 3. Know about these services at a high level, but do not skip any:

AWS Organization, X-Ray, GuardDuty, Macie, Inspector, Service Catalog, KMS, Batch, Athena, Single-Sign-On, Data LifeCycle Manager, CloudSearch, Health Dashboard, Glue, QuickSight, LightSail

Although I did not mention much about the white papers, I want to highlight their importance again. I would not attempt the exam without reading and understanding the required white papers.

If you aspire to take the AWS certified DevOps Engineer exam I hope this helps you a little bit. Good luck.