Multi-cluster & Hybrid Cloud Management

The Challenge

Kubernetes was designed to manage workloads within a cluster. It was not designed to manage fleets of clusters across clouds, regions, on-premises environments, and edge locations — but that’s the reality most organizations operating at scale are living in.

The problems compound quickly. Each cluster has its own RBAC configuration, its own version, its own security policies, and its own observability setup. Edge clusters and constrained edge nodes add another layer of complexity: intermittent connectivity, smaller resource footprints, and regional autonomy requirements. When a vulnerability needs to be patched or a new policy needs to be enforced, doing it consistently across dozens of clusters is a coordination problem with no good manual solution. Meanwhile, developers and operators lack a unified view — understanding where a workload is running and how it’s performing requires logging into multiple systems.

Cost and placement decisions become harder too. Without a fleet-level view, teams can’t make informed choices about where to run which workloads, or respond dynamically to availability and cost signals across environments.

Our Approach

We design multi-cluster architectures with a clear separation between the management plane and the workload plane. The management plane provides fleet-wide visibility, policy enforcement, and cluster lifecycle control. Workload clusters remain autonomous and operational even if the management plane is unavailable — resilience doesn’t depend on centralized control. That same model is especially important for edge clusters, where local operations must continue even during WAN interruptions.

Policy federation is a core concern. Security policies, network policies, RBAC templates, and admission controls should be defined once and applied consistently across the fleet, with overrides where environments legitimately differ. Drift from policy should be detectable and correctable automatically.

We also design for the day-two operational questions: where should a new workload run, how do workloads migrate between clusters, how should workloads be placed between core and edge, and how does observability work across cluster boundaries.

Technology Options

• Red Hat Advanced Cluster Management (RHACM) — fleet management for OpenShift and upstream Kubernetes clusters, with policy enforcement, application lifecycle management, and observability across environments
• Argo CD ApplicationSets — GitOps-based multi-cluster application delivery, deploying and syncing workloads across a fleet of clusters from a single control point
• Cluster API (CAPI) — declarative cluster lifecycle management across cloud providers and on-prem infrastructure; clusters defined and updated as Kubernetes resources
• Kubernetes Distrobutions — lightweight and optimized distributions for edge orchestration patterns, constrained nodes, remote sites, and intermittently connected environments
• Submariner — cross-cluster network connectivity, enabling services in one cluster to reach services in another with direct L3 routing and service discovery
• Cilium Cluster Mesh — eBPF-based multi-cluster networking with shared service discovery, network policies, and observability across clusters
• Kyverno / OPA Gatekeeper — policy-as-code engines that enforce consistent security and operational standards across a fleet when applied via a management plane
• Thanos / Grafana — cross-cluster metrics aggregation and dashboarding, providing a single observability view across environments