Jurisdiction-based requirements: For example, data residency limitations imposed by governing bodies.Data gravity requirements: For example, bringing elements of the application as close to fixed data sources as possible.Network latency requirements: For example, bringing the application as close to users as possible.Location: Location-based concerns include the following:.

The requirements concerning workload location/proximity, isolation, and reliability have been the primary catalyst for the emergence of deployment scenarios where a single logical workload spans multiple Kubernetes clusters: Image one: AWS Cloud Map MCS-Controller solution deployment overview diagram Background Image one here illustrates the demonstrated solution, where example service, “nginx-hello,” deployed to Kubernetes cluster, “cls1,” is both discoverable and consumable from Kubernetes cluster “cls2,” using the MCS-Controller. This post also provides comprehensive instruction on how to make a Kubernetes service automatically discoverable and accessible across multiple clusters using the MCS-Controller. This blog post will provide an overview of Kubernetes multi-cluster workload management, the mcs-api, and the MCS-Controller which is the AWS open source implementation of the mcs-api. A lot of progress has been made in open source tooling to help manage multi-cluster workloads, including the upstream Kubernetes Multi-Cluster Services API (mcs-api), and the open source Amazon Web Services (AWS) Cloud Map MCS Controller (MCS-Controller). As a result, teams can now move at increasing velocities.Īs the rate of Kubernetes adoption continues to increase, there has been a corresponding increase in the number of use cases that require workloads to break through the perimeter of the single cluster construct. Declarative configuration, immutability, auto-scaling, and self-healing have vastly improved the paradigm of workload management within the cluster. With its implementation of the cluster construct, Kubernetes has streamlined the ability to schedule workloads across a collection of virtual machines (VMs) or nodes.

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