Hybrid cloud orchestration on AWS addresses a practical problem: how to centrally manage thousands of on-premises servers and clusters without moving the workloads to the cloud.
When infrastructure is distributed across multiple sites, degradation begins not with hardware failure, but with procedural discrepancies. The same Kubernetes cluster can be deployed differently at different sites. The reason is simple: different vendors, different networks, different compliance requirements, and different local practices. Without centralized orchestration, unified operations become unpredictable.
Lifecycle operations create an additional layer of complexity. This includes BIOS, firmware, power management, OS installation, patches, cluster creation and updates, scaling, and application maintenance. For a single server, this is a workflow. For thousands of machines across hundreds of locations, it becomes a bottleneck that consumes engineers’ time and turns maintenance windows into a logistical challenge.
In the original solution, a pragmatic compromise was chosen: centralize management in AWS while keeping execution on-premises. This utilizes AWS Lambda, AWS Step Functions, and Amazon DynamoDB as the framework for an event-driven orchestration engine. This approach does not attempt to replace local infrastructure. It establishes a unified control layer while leaving execution where the servers and clusters reside.
The key trade-off here is evident. Centralization simplifies control, audit trails, and event response. However, it requires reliable hybrid connectivity between AWS and the sites. AWS Direct Connect or AWS Site-to-Site VPN is used for this purpose. This allows AWS services to coordinate operations without taking control away from the on-premises environment.
The architecture itself is divided into three layers. The first is the central orchestration engine in AWS. The second is the distributed on-premises infrastructure with Amazon EKS Anywhere. The third is the communication channel between them. This division is important because it allows for scaling management across hundreds of sites without mixing the state of management with the state of execution.
At the core of the model is an Inventory Management System on DynamoDB. It stores sites, servers, clusters, orders, and template catalogs. This is not just a directory. It is a single source of truth for the state of resources and their relationships. It is here that the system understands which server is located where, what its BIOS and firmware versions are, which cluster it belongs to, and what workflow is currently being executed.
On top of this, an API layer is built. Amazon API Gateway provides a REST interface for CRUD operations. Lambda validates requests and creates orders. Then, Amazon EventBridge routes the event to the appropriate Step Functions workflow. This approach reduces component coupling. The API does not wait for the operation to complete. It immediately returns the order ID, while execution proceeds asynchronously.
This behavior is crucial for large sites. An operation can take minutes or hours. For example, firmware updates or cluster deployments should not block the operator interface. Instead of synchronous waiting, the system converts the request into a trackable workflow. The order status is updated as the state changes.
Step Functions acts as an orchestrator here, not just a task runner. It adds retry logic, error handling, and state checkpointing. The callback pattern is especially important. The workflow can pause a task, hand it off to an external system, and continue only after receiving a callback with a token. For hybrid infrastructure, this is essential: on-premises operations can take a long time and require a break between initiation and confirmation of completion.
Scaling is achieved using the Distributed Map state. This allows management to transition from a single resource to thousands of resources across different sites. Practically, this means that one workflow can coordinate mass operations on servers without breaking the execution model. However, such scale requires strict conflict control. Therefore, the inventory does not allow a new order to be initiated if another operation is already in progress on the same resource. This protects the system from overlaps, such as scaling a cluster during an upgrade.
An additional layer involves security and configuration. AWS Systems Manager Parameter Store holds parameters, AWS Secrets Manager stores secrets, and IAM provides granular access control. IAM Roles Anywhere extends AWS access to on-premises clusters without long-term credentials. Systems Manager hybrid activations are used for registering local instances. AWS Private Certificate Authority is employed for secure communication. The logic here is consistent: each component receives only the permissions necessary for its function.
Amazon EKS Anywhere is chosen for a reason. It runs a Kubernetes cluster on its own hardware and uses the same Amazon EKS Distro as EKS in the cloud. This provides consistency at the platform level. However, the responsibility for lifecycle clusters remains with the team. It is this operational burden that the orchestration engine alleviates.
From an engineering perspective, the solution improves not only automation but also manageability. A unified control plane for the distributed on-premises environment emerges, with centralized inventory, order tracking, and event-driven execution. The original material does not include metrics, so the quantitative effect is not specified. However, architecturally, the goal is clear: to reduce manual operations, eliminate disparate tools, and make infrastructure behavior predictable.