Subscribe to the feed

Typically, when a new technology or pattern emerges, various approaches are taken to determine how it best fits into a transformed model. Proponents of traditional methods are sometimes initially met with resistance by eager early adopters, who may discard lessons learned from foundational practices. 

Established orchestration approaches versus GitOps methodologies demonstrate this inflection point.

While it would be an oversimplification to assume that one approach entirely can replace the other, we’ll examine the strengths and weaknesses of each in this post and find a balance between the two. 

Closed-loop orchestration

Traditional closed-loop orchestration refers to the automation of the complete life cycle of infrastructure, platforms, and applications by deploying, monitoring, and correcting issues as they are detected throughout the lifetime of the managed element.

Figure 1.

This orchestration solution continuously receives events, alarms, metrics, and logs from the orchestrated elements—and all data sources are analyzed by specialized controller elements and compared to the predefined policies. The policy controller identifies and targets policy violations for remediation action, which triggers the corrective cycle. The remedial action based on variance from the desired state completes the feedback loop.

These concepts are similar to capabilities native to Kubernetes because it implements the closed-loop orchestration for containers. Kubernetes controllers (replication, endpoint, etc.), which possess the knowledge to remediate deviations, are control loops with awareness of the “desired state” and the “current state.”

Policies and configurations

Traditional orchestrators assume they are the source of truth, governance, and enforcement for configuration policies regarding artifacts (e.g., application components, infrastructure elements, and platform stacks) configurations. 

In contrast, Kubernetes challenges the assumptions and role of the traditional orchestrator. The etcd database is the source of truth of the configuration and desired state of any artifact defined on/for the platform. Kubernetes controllers maintain a closed-loop cycle in which events and metrics are analyzed to drive remediation tasks to remedy deviations.

These two models might not seem compatible. There cannot be two different sources of truth, nor two closed-loop remediation cycles acting over the same artifacts. However,  these two models complement each as illustrated:

Figure 3.

When examining the closed-loop “incompatibilities” between a traditional orchestrator and Kubernetes, the principal differentiators are the source of truth for configurations and policies, and who acts over the managed artifacts. 

Kubernetes orchestration is a domain-specific closed-loop orchestrator for containers, pods, microservices, artifacts configurations, but still has dependencies on external ancillary services (e.g., DNS, CA, DHCP servers, artifact repositories, container registries, network infrastructure configurations, etc.). 

Traditional closed-loop orchestration handles the preparation of infrastructure and ancillary services (and the definition of the Kubernetes cluster). The role of the orchestrator performed on the Kubernetes platform deployment is directly related to the Kubernetes platform and associated tools.

Kubernetes-native capabilities replace many tasks that were traditionally expected from the external orchestrator. When Kubernetes Operators are used for the platform and plugins, the platform, including the operating system (OS), is managed through the Kubernetes-specialized controllers. 

The same is the case for OpenShift with the OpenShift Cluster Operators, Kubernetes Operators for Applications, and Kubernetes Operators for Plugins and platform extensions. In OpenShift, the source of truth for the desired state and configuration of the OS, platform, applications, and Kubernetes artifacts is maintained in the platform etcd database.

In this instance, a valid deployment could set new desired states or configurations and have the external closed-loop orchestrator interacting directly with the Kubernetes and OpenShift application programming interface (API) to modify artifact configurations. However, in such a scenario where artifacts or configuration could be modified by the orchestrator or even by intelligence in the Kubernetes Operators, what holds the single source of truth if the cluster needs to be rebuilt? 

This is where the GitOps model comes in.

The GitOps Model

The GitOps Model uses a Git repository as the single source of truth representing the desired state of the infrastructure, platform, and applications. This definitive repository is used by continuous delivery pipelines on closed-loop orchestration and automation flows to automatically synchronize the “current state” with the “desired state.”

Figure 3.

GitOps controllers are tools that receive or detect updates notifications from changes in Git, interpreting them as the policies for the “desired state” and controlling the managed artifacts. The GitOps Model contains a high level of flexibility, applicable in many instances and configurations. For example:

  • Infrastructure - used as Orchestrator-driven triggers of Continuous Delivery (CD) pipelines or invoking workflows in a tool like Red Hat Ansible Automation Platform. 

  • Within the Red Hat OpenShift Container Platform - the GitOps controller can be a tool like ArgoCD (part of the OpenShift GitOps Operator). When multiple OpenShift clusters are being managed with the GitOps pattern, Red Hat Advanced Cluster Management for Kubernetes has native GitOps capabilities integration with GitOps channels from Git, Helm release registry, or object storage repositories.

With the domain-specific specialization of GitOps controllers, the need for coordination between multiple GitOps became apparent. This evolved into the GitOps app-of-apps pattern, where a parent GitOps controller is used to deploy and manage multiple instances of GitOps controllers, each controller focusing on a particular task or domain.

With this GitOps app-of-apps pattern, supported by tools like ArgoCD and Red Hat Advanced Cluster Management, powerful combinations are possible. For example, using Red Hat Advanced Cluster Management Application resources (RHACM App) in app-of-apps configurations consisting of a mix of RHACM App of RHACM Apps, RHACM App of ArgoCD Apps, or other nested setups such as RHACM App of Helm Apps. A more recent evolution of the apps-of-apps pattern is the ApplicationSet, which extends the concept to multi-cluster configurations.

The evolving role of the orchestrator

What role does the traditional closed-loop orchestrator have in a GitOps-driven operation? The traditional orchestrator needs to become a composable GitOps controller for closed-loop automation applied to infrastructure and ancillary services. For platform configurations, the orchestrator just updates the Git repository and the corresponding GitOps controller takes care of reconciling the “current state” and its remediation to the “desired state.”

Instead of using proprietary formats in a database, in an ideal scenario, the external closed-loop orchestrator will also use the Git repository as the single source of truth for version control of the configuration and artifact definitions.

In real-world practice, the actualization of an ideal state will require integrating the traditional database into GitOps-driven operations. From here, the external orchestrator is required to write the artifact definition and configuration into the Git repository, which enables the corresponding GitOps controller to consume it and execute the needed reconciliation cycles. For the Orchestrator to track “desired state” progress or status, it must (when the app-of-apps pattern or ApplicationSets are in use) consult the API exposed by the GitOps controller or the parent GitOps controllers.

Figure 2. As illustrated, the adoption of the GitOps models evolves upon the role of the traditional closed-loop orchestrator. It uses Git to communicate the desired state and Git also becomes the versioned single source of truth for the definition of artifacts and desired state. The Orchestrator then consumes APIs from the GitOps controllers to track the status and progress of the desired state of an artifact.

In the event of catastrophic failure, a new cluster can be created and the GitOps controllers will manage to bring it to a desired good state of the previous cluster. Because artifacts definitions and configurations are contained with version control, there is an exact record of the previously used (and working) configurations. Restoration or “rollback” requires only the importation of the correct version of the configuration or artifact definition from Git.

Closing statements and looking ahead

As with any new technology, cultural resistance will be present from advocates of traditional approaches and a tendency to discard past lessons from enthusiastic adopters of novel approaches. It is up to the platform and orchestration providers to discover the right balance between the traditional and new patterns. 

The operational models of Kubernetes and Kubernetes Operators promote the use of granular and specialized controllers. Within this consideration, a natural evolution is the use of the GitOps methodology to decouple and version control the artifacts definition and configuration from the cluster hosting them. If a cluster needs to be replaced, a single source of truth exists to seamlessly replicate a cluster with configuration and artifacts definitions identical to the cluster it is replacing.

GitOps methodologies and principles are evolving as customers apply the pattern to new fields. The industry and communities are actively working to consolidate principles, develop tools, and document best practices. The Cloud Native Computing Foundation (CNCF) GitOps working group is one community fostering collaboration among companies and organizations. Within the telecom industry, the Telecom Infra Project is exploring similar principles within closed-loop automation and orchestration for Core networks and Open RAN deployments.

Red Hat is an active member of these communities and collaborates with the ecosystem towards consolidation and cross-pollination of related work. 

Learn more about a portion of the Red Hat product portfolio supporting GitOps methodologies, including Red Hat Advanced Cluster Management for KubernetesRed Hat OpenShift for GitOps, and the Red Hat OpenShift Container Platform.

About the author

William is a seasoned professional with 25 years of experience enabling Telco business transformation through emerging technologies. He works with Telco and MSO partners and customers at the forefront of digital disruption on architecting solutions that transform markets. 

Read full bio

Browse by channel

automation icon


The latest on IT automation for tech, teams, and environments

AI icon

Artificial intelligence

Updates on the platforms that free customers to run AI workloads anywhere

open hybrid cloud icon

Open hybrid cloud

Explore how we build a more flexible future with hybrid cloud

security icon


The latest on how we reduce risks across environments and technologies

edge icon

Edge computing

Updates on the platforms that simplify operations at the edge

Infrastructure icon


The latest on the world’s leading enterprise Linux platform

application development icon


Inside our solutions to the toughest application challenges

Original series icon

Original shows

Entertaining stories from the makers and leaders in enterprise tech