Cluster multi-tenancy

This page explains cluster multi-tenancy on Google Kubernetes Engine. This includes clusters shared by different users at a single organization, and clusters that are shared by per-customer instances of a software as a service (SaaS) application. Cluster multi-tenancy is an alternative to managing many single-tenant clusters.

This page also summarizes the Kubernetes and GKE features that can be used to manage multi-tenant clusters.

What is multi-tenancy?

A multi-tenant cluster is shared by multiple users and/or workloads which are referred to as "tenants". The operators of multi-tenant clusters must isolate tenants from each other to minimize the damage that a compromised or malicious tenant can do to the cluster and other tenants. Also, cluster resources must be fairly allocated among tenants.

When you plan a multi-tenant architecture you should consider the layers of resource isolation in Kubernetes: cluster, namespace, node, pod, and container. You should also consider the security implications of sharing different types of resources among tenants. For example, scheduling pods from different tenants on the same node could reduce the number of machines needed in the cluster. On the other hand, you might need to prevent certain workloads from being colocated. For example, you might not allow untrusted code from outside of your organization to run on the same node as containers that process sensitive information.

Although Kubernetes cannot guarantee perfectly secure isolation between tenants, it does offer features that may be sufficient for specific use cases. You can separate each tenant and their Kubernetes resources into their own namespaces. You can then use policies to enforce tenant isolation. Policies are usually scoped by namespace and can be used to restrict API access, to constrain resource usage, and to restrict what containers are allowed to do.

The tenants of a multi-tenant cluster share:

Operating a multi-tenant cluster has several advantages over operating multiple, single-tenant clusters:

  • Reduced management overhead
  • Reduced resource fragmentation
  • No need to wait for cluster creation for new tenants

Multi-tenancy use cases

This section describes how you could configure a cluster for various multi-tenancy use cases.

Enterprise multi-tenancy

In an enterprise environment, the tenants of a cluster are distinct teams within the organization. Typically, each tenant has a corresponding namespace. Alternative models of multi-tenancy with a tenant per cluster, or a tenant per GCP project, are harder to manage. Network traffic within a namespace is unrestricted. Network traffic between namespaces must be explicitly whitelisted. These policies can be enforced using Kubernetes network policy.

The users of the cluster are divided into three different roles, depending on their privilege:

Cluster administrator
This role is for administrators of the entire cluster, who manage all tenants. Cluster administrators can create, read, update, and delete any policy object. They can create namespaces and assign them to namespace administrators.
Namespace administrator
This role is for administrators of specific, single tenants. A namespace administrator can manage the users in their namespace.
Developer
Members of this role can create, read, update, and delete namespaced non-policy objects like Pods, Jobs, and Ingresses. Developers only have these privileges in the namespaces they have access to.

SaaS provider multi-tenancy

The tenants of a SaaS provider's cluster are the per-customer instances of the application, and the SaaS's control plane. To take advantage of namespace-scoped policies, the application instances should be organized into their own namespaces, as should components of the SaaS's control plane. End users can't interact with the Kubernetes control plane directly, they use the SaaS's interface instead, which in turn interacts with the Kubernetes control plane.

For example, a blogging platform could run on a multi-tenant cluster. In this case, the tenants are each customer's blog instance and the platform's own control plane. The platform's control plane and each hosted blog would all run in separate namespaces. Customers would create and delete blogs, update the blogging software versions through the platform's interface with no visibility into how the cluster operates.

Multi-tenancy policy enforcement

GKE and Kubernetes provide several features that can be used to manage multi-tenant clusters. The following sections give an overview of these features.

Access control

GKE has two access control systems: Cloud Identity & Access Management (Cloud IAM) and role-based access control (RBAC). Cloud IAM is GCP's access control system for managing authentication and authorization for GCP resources. You use Cloud IAM to grant users access to GKE and Kubernetes resources. RBAC is built into Kubernetes and grants granular permissions for specific resources and operations within your clusters.

Refer to the Access control overview for more information about these options and when to use each.

Refer to the RBAC how-to guide and the Cloud IAM how-to guide to learn how to use these access control systems.

Network policies

Cluster network policies give you control over the communication between your cluster's Pods. Policies specify which namespaces, labels, and IP address ranges a Pod can communicate with.

See the network policy how-to for instructions on enabling network policy enforcement on GKE.

Follow the network policy tutorial to learn how to write network policies.

Resource quotas

Resource quotas manage the amount of resources used by the objects in a namespace. You can set quotas in terms of CPU and memory usage, or in terms of object counts. Resource quotas let you ensure that no tenant uses more than its assigned share of cluster resources.

Refer to the resource quotas documentation for more information.

Pod security policies

PodSecurityPolicies are a Kubernetes API type that validate requests to create and update Pods. PodSecurityPolicies define default values and requirements for security-sensitive fields of Pod specification. You can create policies that restrict the deployment of Pods that access the host filesystem, networks, PID namespaces, volumes, and more.

Refer to the PodSecurityPolicies how-to for more.

Pod anti-affinity

You can use Pod anti-affinity to prevent Pods from different tenants from being scheduled on the same node. Anti-affinity constraints are based on Pod labels. For example, the Pod specification below describes a Pod with the label "team": "billing", and an anti-affinity rule that prevents the Pod from being scheduled alongside Pods without the label.

apiVersion: v1
kind: Pod
metadata:
  name: bar
  labels:
    team: "billing"
spec:
  affinity:
    podAntiAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
      - topologyKey: "kubernetes.io/hostname"
        labelSelector:
          matchExpressions:
          - key: "team"
            operator: NotIn
            values: ["billing"]

The drawback to this technique is that malicious users could circumvent the rule by applying the team: billing label to an arbitrary Pod. Pod anti-affinity alone cannot securely enforce policy on clusters with untrusted tenants.

Refer to the Pod anti-affinity documentation for more information.

Dedicated nodes with taints and tolerations

Node taints are another way to control workload scheduling. You can use node taints to reserve specialized Nodes for use by certain tenants. For example, you can dedicate GPU equipped nodes to the specific tenants whose workloads require GPUs.

To dedicate a node pool to a certain tenant, apply a taint with effect: "NoSchedule" to the node pool. Then only pods with a corresponding toleration can be scheduled to nodes in the node pool.

The drawback to this technique is that malicious users could add a toleration to their Pods to get access to the dedicated node pool. Node taints and tolerations alone cannot securely enforce policy on clusters with untrusted tenants.

See the node taints how-to page to learn how to control scheduling with node taints.

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