Azure Kubernetes Service (AKS) production baseline
In this reference architecture, we’ll build a baseline infrastructure that deploys an Azure Kubernetes Service (AKS) cluster. This article includes recommendations for networking, security, identity, management, and monitoring of the cluster based on an organization’s business requirements. The requirements are assessed by using Azure Well-Architected Framework.
An implementation of this architecture is available on GitHub: Azure Kubernetes Service (AKS) Secure Baseline Reference Implementation. You can use it as a starting point and configure it as per your needs.
This reference architecture requires knowledge of Kubernetes and its concepts. If you need a refresher, complete this workshop to deploy a multi-container application to Kubernetes on Azure Kubernetes Service (AKS).
This architecture uses a hub-spoke network topology. The hub and spoke(s) are deployed in separate virtual networks connected through peering. Some advantages of this topology are:
Segregated management. It allows for a way to apply governance and control the blast radius. It also supports the concept of landing zone with separation of duties.
Minimizes direct exposure of Azure resources to the public internet.
Organizations often operate with regional hub-spoke topologies. Hub-spoke network topologies can be expanded in the future and provide workload isolation.
All web applications should require a web application firewall (WAF) service to help govern HTTP traffic flows.
A natural choice for workloads that span multiple subscriptions.
It makes the architecture extensible. To accommodate new features or workloads, new spokes can be added instead of redesigning the network topology.
Certain resources, such as a firewall and DNS can be shared across networks.
The hub virtual network is the central point of connectivity and observability. Within the network, three subnets are deployed.
Subnet to host Azure Firewall
Azure Firewall is firewall as a service. The firewall instance secures outbound network traffic. Without this layer of security, the flow might communicate with a malicious third-party service that could exfiltrate sensitive company data.
Subnet to host a gateway
This subnet is a placeholder for a VPN or ExpressRoute gateway. The gateway provides connectivity between the routers in the on-premises network and the virtual network.
Subnet to host Azure Bastion
This subnet is a placeholder for Azure Bastion. You can use Bastion to securely access Azure resources without exposing the resources to the internet. This subnet is used for management and operations only.
The spoke virtual network will contain the AKS cluster and other related resources. The spoke has three subnets:
Subnet to host Azure Application Gateway
Azure Application Gateway is a web traffic load balancer operating at Layer 7. The reference implementation uses the Application Gateway v2 SKU that enables Web Application Firewall (WAF). WAF secures incoming traffic from common web traffic attacks. The instance has a public frontend IP configuration that receives user requests. By design, Application Gateway requires a dedicated subnet.
Subnet to host the ingress resources
To route and distribute traffic, Traefik is the ingress controller that is going to fulfill the Kubernetes ingress resources. The Azure internal load balancers exist in this subnet.
Subnet to host the cluster nodes
AKS maintains two separate groups of nodes (or node pools). The system node pool hosts pods that run core cluster services. The user node pool runs the Contoso workload and the ingress controller to facilitate inbound communication to the workload. The workload is a simple ASP.NET application.
For additional information, Hub-spoke network topology in Azure.
Plan the IP addresses
The address space of the virtual network should be large enough to hold all subnets. Account for all entities that will receive traffic. IP addresses for those entities will be allocated from the subnet address space. Consider these points.
AKS updates nodes regularly to make sure the underlying virtual machines are up to date on security features and other system patches. During an upgrade process, AKS creates a node that temporarily hosts the pods, while the upgrade node is cordoned and drained. That temporary node is assigned an IP address from the cluster subnet.
For pods, you might need additional addresses depending on your strategy. For rolling updates, you'll need addresses for the temporary pods that run the workload while the actual pods are updated. If you use the replace strategy, pods are removed, and the new ones are created. So, addresses associated with the old pods are reused.
Take into consideration the node count of all system and user nodes and their maximum scalability limit. Suppose you want to scale out by 400%. You'll need four times the number of addresses for all those scaled-out nodes.
In this architecture, each pod can be contacted directly. So, each pod needs an individual address. Pod scalability will impact the address calculation. That decision will depend on your choice about the number of pods you want to grow.
Azure Private Link addresses
Factor in the addresses that are required for communication with other Azure services over Private Link. In this architecture, we have two addresses assigned for the links to Azure Container Registry and Key Vault.
Certain addresses are reserved for use by Azure. They can't be assigned.
The preceding list isn't exhaustive. If your design has other resources that will impact the number of available IP addresses, accommodate those addresses.
This architecture is designed for a single workload. For multiple workloads, you may want to isolate the user node pools from each other and from the system node pool. That choice may result in more subnets that are smaller in size. Also, the ingress resource might be more complex. You might need multiple ingress controllers that will require extra addresses.
For the complete set of considerations for this architecture, see AKS baseline Network Topology.
For information related to planning IP for an AKS cluster, see Plan IP addressing for your cluster.
Configure compute for the base cluster
In AKS, each node pool maps to a virtual machine scale set. Nodes are VMs in each node pool. Consider using a smaller VM size for the system node pool to minimize costs. This reference implementation deploys the system node pool with three DS2_v2 nodes. That size is sufficient to meet the expected load of the system pods. The OS disk is 512 GB.
For the user node pool, here are some considerations:
Choose larger node sizes to pack the maximum number of pods set on a node. It will minimize the footprint of services that run on all nodes, such as monitoring and logging.
Deploy at least two nodes. That way, the workload will have a high availability pattern with two replicas. With AKS, you can change the node count without recreating the cluster.
Actual node sizes for your workload will depend on the requirements determined by the design team. Based on the business requirements, we've chosen DS4_v2 for the production workload. To lower costs one could drop the size to DS3_v2, which is the minimum recommendation.
When planning capacity for your cluster, assume that your workload can consume up to 80% of each node; the remaining 20% is reserved for AKS services.
The maximum pods per node, is set to 30, which is also the default. Increasing this value can impact performance because of an unexpected node failure or expected node maintenance events.
Integrate Azure Active Directory for the cluster
Securing access to and from the cluster is critical. Think from the cluster's perspective when you're making security choices:
Outside-in access. Authorize only those external entities that are allowed access to the Kubernetes API server and Azure Resource Manager.
Inside-out access. Authorize only those resources that the cluster is allowed access.
There are two ways to manage access through Azure Active Directory (Azure AD): service principals or managed identities for Azure resources.
Of the two ways, managed identities is recommended. With service principals, you are responsible for managing and rotating secrets, either manually or programmatically. With managed identities, Azure AD manages and performs the authentication and timely rotation of secrets for you.
It’s recommended that managed identities is enabled so that the cluster can interact with external Azure resources through Azure AD. You can enable this setting only during cluster creation. Even if Azure AD isn't used immediately, you can incorporate it later.
As an example for the inside-out case, let’s study the use of managed identities when the cluster needs to pull images from a container registry. This action requires the cluster to get the credentials of the registry. One way is to store that information in the form of Kubernetes Secrets object and use
imagePullSecrets to retrieve the secret. That approach isn't recommended because of security complexities. Not only do you need prior knowledge of the secret but also disclosure of that secret through the DevOps pipeline. Another reason is the operational overhead of managing the rotation of the secret. Instead, grant
acrPull access to the managed identity of the cluster to your registry. This approach addresses those concerns.
In this architecture, the cluster accesses Azure resources that are secured by Azure AD and perform operations that support managed identities. Assign role-based access control (RBAC) and permissions to the cluster’s managed identities, depending on the operations that the cluster intends to do. The cluster will authenticate itself to Azure AD and then be allowed or denied access based on the roles it has been assigned. Here are some examples from this reference implementation where Azure RBAC built-in roles have been assigned to the cluster:
Network Contributor. The cluster’s ability to control the spoke virtual network. This role assignment allows AKS cluster system assigned identity to work with the dedicated subnet for the Internal Ingress Controller services.
Monitoring Metrics Publisher. The cluster’s ability to send metrics to Azure Monitor.
AcrPull. The cluster’s ability to pull images from the specified Azure Container Registries.
Azure AD integration also simplifies security for outside-in access. Suppose a user wants to use kubectl. As an initial step, sends the
az aks get-credentials command to get the credentials of the cluster. Azure AD will authenticate the user’s identity against the Azure Resource Manager RBAC roles that are allowed to get cluster credentials. For more information, see Available cluster roles permissions.
Associate Kubernetes RBAC to Azure Active Directory
Kubernetes supports role-based access control (RBAC) through:
A set of permissions. Defined by a
ClusterRoleobject for cluster-wide permissions.
Bindings that assign users and groups who are allowed to do the actions. Defined by a
Kubernetes has some built-in roles such as cluster-admin, edit, view, and so on. Bind those roles to Azure Active Directory users and groups to use enterprise directory to manage access. For more information, see Use Kubernetes RBAC with Azure AD integration.
There’s also an option of using Azure RBAC roles instead of the Kubernetes built-in roles. For more information, see Azure RBAC roles.
Integrate Azure Active Directory for the workload
Similar to having Azure Managed Identities for the entire cluster, you can assign managed identities at the pod level. A pod managed identity allows the hosted workload to access resources through Azure Active Directory. For example, the workload stores files in the Azure Storage. When it needs to access those files, the pod will authenticate itself against the resource.
In this reference implementation, managed pod identities is facilitated through aad-pod-identity.
Deploy Ingress resources
Kubernetes Ingress resources route and distribute incoming traffic to the cluster. There are two portions of Ingress resources:
Internal load balancer. Managed by AKS. This load balancer exposes the ingress controller through a private static IP address. It serves as single point of contact that receives inbound flows.
In this architecture, Azure Load Balancer is used. It’s placed outside the cluster in a subnet dedicated for ingress resources. It receives traffic from Azure Application Gateway and that communication is over TLS. For information about TLS encryption for inbound traffic, see Ingress traffic flow.
Ingress controller. We have chosen Traefik. It runs in the user node pool in the cluster. It receives traffic from the internal load balancer, terminates TLS, and forwards it to the workload pods over HTTP.
The ingress controller is a critical component of cluster. Consider these points when configuring this component.
As part of your design decisions, choose a scope within which the ingress controller will be allowed operate. For example, you might allow the controller to only interact with the pods that run a specific workload.
Avoid placing replicas on the same node to spread out the load and ensure business continuity if a node does down. Use
podAntiAffinityfor this purpose.
Constrain pods to be scheduled only on the user node pool by using
nodeSelectors. This setting will isolate workload and system pods.
Open ports and protocols that allow specific entities to send traffic to the ingress controller. In this architecture, Traefik only receives traffic from Azure Application Gateway.
Ingress controller should send signals that indicate the health of pods. Configure
livenessProbesettings that will monitor the health of the pods at the specified interval.
Consider restricting the ingress controller’s access to specific resources and the ability to perform certain actions. That restriction can be implemented through Kubernetes RBAC permissions. For example, in this architecture, Traefik has been granted permissions to watch, get, and list services and endpoints by using rules in the Kubernetes
The ingress controller uses routes to determine where to send traffic. Routes specify the source port at which the traffic is received and information about the destination ports and protocols.
Here’s an example from this architecture:
Traefik uses the Kubernetes provider to configure routes. The
entrypoints indicate that routes will be served over HTTPS. The
middlewares specifies that only traffic from the Azure Application Gateway subnet is allowed. The responses will use gzip encoding if the client accepts. Because Traefik does TLS termination, communication with the backend services is over HTTP.
apiVersion:networking.k8s.io/v1beta1 kind: Ingress metadata: name: aspnetapp-ingress namespace: a0008 annotations: kubernetes.io/ingress.allow-http: "false" kubernetes.io/ingress.class: traefik-internal traefik.ingress.kubernetes.io/router.entrypoints: websecure traefik.ingress.kubernetes.io/router.tls: "true" traefik.ingress.kubernetes.io/router.tls.options: default traefik.ingress.kubernetes.io/router.middlewares: app-gateway-snet@file, gzip-compress@file spec: tls: - hosts: - bu0001a0008-00.aks-ingress.contoso.com rules: - host: bu0001a0008-00.aks-ingress.contoso.com http: paths: - path: / backend: serviceName: aspnetapp-service servicePort: http
Secure the network flow
Network flow, in this context, can be categorized as:
Ingress traffic. From the client to the workload running in the cluster.
Egress traffic. From a pod or node in the cluster to an external service.
Pod-to-pod traffic. Communication between pods. This traffic includes communication between the ingress controller and the workload. Also, if your workload is composed of multiple applications deployed to the cluster, communication between those applications would fall into this category.
Management traffic. Traffic that goes between the client and the Kubernetes API server.
This architecture has several layers of security to secure all types of traffic.
Ingress traffic flow
The architecture only accepts TLS encrypted requests from the client. TLS v1.2 is the minimum allowed version with a restricted set of cyphers. Server Name Indication (SNI) strict is enabled. End-to-end TLS is set up through Application Gateway by using two different TLS certificates, as shown in this image.
The client sends an HTTPS request to the domain name: bicycle.contoso.com. That name is associated with through a DNS A record to the public IP address of Azure Application Gateway. This traffic is encrypted to make sure that the traffic between the client browser and gateway cannot be inspected or changed.
Application Gateway has an integrated web application firewall (WAF) and negotiates the TLS handshake for bicycle.contoso.com, allowing only secure ciphers. Application Gateway is a TLS termination point, as it's required to process WAF inspection rules, and execute routing rules that forward the traffic to the configured backend. The TLS certificate is stored in Azure Key Vault. It’s accessed using a user-assigned managed identity integrated with Application Gateway. For information about that feature, see TLS termination with Key Vault certificates.
The traffic moves from Application Gateway to the backend, the traffic is encrypted again with another TLS certificate (wildcard for *.aks-ingress.contoso.com) as it’s forwarded to the internal load balancer. This re-encryption makes sure traffic that is not secure doesn’t flow into the cluster subnet.
The ingress controller receives the encrypted traffic through the load balancer. The controller is another TLS termination point for *.aks-ingress.contoso.com and forwards the traffic to the workload pods over HTTP. The certificates are stored in Azure Key Vault and mounted into the cluster using the Container Storage Interface (CSI) driver. For more information, see Add secret management.
You can implement end-to-end TLS traffic all at every hop the way through to the workload pod. Be sure to measure the performance, latency, and operational impact when making the decision to secure pod-to-pod traffic. For most single-tenant clusters, with proper control plane RBAC and mature Software Development Lifecycle practices, it's sufficient to TLS encrypt up to the ingress controller and protect with Web Application Firewall (WAF). That will minimize overhead in workload management and network performance impacts. Your workload and compliance requirements will dictate where you perform TLS termination.
Egress traffic flow
For zero-trust control and the ability to inspect traffic, all egress traffic from the cluster moves through Azure Firewall. You can implement that choice using user-defined routes (UDRs). The next hop of the route is the private IP address of the Azure Firewall. Here, Azure Firewall decides whether to block or allow the egress traffic. That decision is based on the specific rules defined in the Azure Firewall or the built-in threat intelligence rules.
If you use a public load balancer as your public point for ingress traffic and egress through Azure Firewall using UDRs, you might see an asymmetric routing situation. This architecture uses internal load balancers in a dedicated ingress subnet behind the Application Gateway. This design choice not only enhances security but also eliminates asymmetric routing concerns. Alternatively, you could route ingress traffic through your Azure Firewall before or after your Application Gateway. That approach isn't necessary or recommended for most situations. For more information about asymmetric routing, see Integrate Azure Firewall with Azure Standard Load Balancer.
An exception to the zero-trust control is when the cluster needs to communicate with other Azure resources. For instance, the cluster needs to pull an updated image from the container registry. The recommended approach is by using Azure Private Link. The advantage is that specific subnets reach the service directly. Also, traffic between the cluster and the service isn't exposed to public internet. A downside is that Private Link needs additional configuration instead of using the target service over its public endpoint. Also, not all Azure services or SKUs support Private Link. For those cases, consider enabling a Service Endpoint on the subnet to access the service.
If Private Link or Service Endpoints aren't an option, you can reach other services through their public endpoints, and control access through Azure Firewall rules and the firewall built into the target service. Because this traffic will go through the static IP address of the firewall, that address can be added the service’s IP allow list. One downside is that Azure Firewall will need to have additional rules to make sure only traffic from specific subnet is allowed.
By default, a pod can accept traffic from any other pod in the cluster. Kubernetes
NetworkPolicy is used to restrict network traffic between pods. Apply policies judiciously, otherwise you might have a situation where a critical network flow is blocked. Only allow specific communication paths, as needed, such as traffic between the ingress controller and workload. For more information, see Network policies.
Enable network policy when the cluster is provisioned because it can't be added later. There are a few choices for technologies that implement
NetworkPolicy. Azure Network Policy is recommended, which requires Azure Container Networking Interface (CNI), see the note below. Other options include Calico Network Policy, a well-known open-source option. Consider Calico if you need to manage cluster-wide network policies. Calico isn't covered under standard Azure support.
For information, see Differences between Azure Network Policy and Calico policies and their capabilities.
AKS supports these networking models: kubenet and Azure Container Networking Interface (CNI). CNI is more advanced of the two models and is required for enabling Azure Network Policy. In this model, every pod gets an IP address from the subnet address space. Resources within the same network (or peered resources) can access the pods directly through their IP address. NAT isn't needed for routing that traffic. So, CNI is performant because there aren’t additional network overlays. It also offers better security control because it enables the use Azure Network Policy. In general, CNI is recommended. CNI offers granular control by teams and the resources they control. Also, CNI allows for more scaled pods than kubenet. Carefully consider this choice otherwise, the cluster will need to be redeployed. For information about the models, see Compare network models.
As part of running the cluster, the Kubernetes API server will receive traffic from resources that want to do management operations on the cluster, such as requests to create resources or the scale the cluster. Examples of those resources include the build agent pool in a DevOps pipeline, a Bastion subnet, and node pools themselves. Instead of accepting this management traffic from all IP addresses, use AKS’s Authorized IP Ranges feature to only allow traffic from your authorized IP ranges to the API server.
For more information, see Define API server authorized IP ranges.
Add secret management
Store secrets in a managed key store, such as Azure Key Vault. The advantage is that the managed store handles rotation of secrets, offers strong encryption, provides an access audit log, and keeps core secrets out of the deployment pipeline.
Azure Key Vault is well integrated with other Azure services. Use the built-in feature of those services to access secrets. For an example about how Azure Application Gateway accesses TLS certificates for the ingress flow, see the Ingress traffic flow section.
Accessing cluster secrets
You'll need to use pod managed identities to allow a pod to access secrets from a specific store.
To facilitate the retrieval process, use a Secrets Store CSI driver. When the pod needs a secret, the driver connects with the specified store, retrieves secret on a volume, and mounts that volume in the cluster. The pod can then get the secret from the volume file system.
The CSI driver has many providers to support various managed stores. In this implementation, we’ve chosen the Azure Key Vault with Secrets Store CSI Driver to retrieve the TLS certificate from Azure Key Vault and load it in the pod running the ingress controller. It's done during pod creation and the volume stores both public and the private keys.
The workload used in this architecture is stateless. If you need to store state, persisting it outside the cluster is recommended. Guidance for workload state is outside the scope of this article.
To learn more about storage options, see Storage options for applications in Azure Kubernetes Service (AKS).
Node and pod scalability
With increasing demand, Kubernetes can scale out by adding more pods to existing nodes, through horizontal pod autoscaling (HPA). When additional pods can no longer be scheduled, the number of nodes must be increased through AKS cluster autoscaling. A complete scaling solution must have ways to scale both pod replicas and the node count in the cluster.
There are two approaches: autoscaling or manual scaling.
The manual or programmatic way requires you to monitor and set alerts on CPU utilization or custom metrics. For pod scaling, the application operator can increase or decrease the number of pod replicas by adjusting the
ReplicaSet through Kubernetes APIs. For cluster scaling, one way is to get notified when the Kubernetes scheduler fails. Another way is to watch for pending pods over time. You can adjust the node count through Azure CLI or the portal.
Autoscaling is the approach because some of those manual mechanisms are built into the autoscaler.
As a general approach, start by performance testing with a minimum number of pods and nodes. Use those values to establish the baseline expectation. Then use a combination of performance metrics and manual scaling to locate bottlenecks and understand the application’s response to scaling. Finally, use this data to set the parameters for autoscaling. For information about a performance tuning scenario using AKS, see Performance tuning scenario: Distributed business transactions.
Horizontal Pod Autoscaler
The Horizontal Pod Autoscaler (HPA) is a Kubernetes resource that scales the number of pods.
In the HPA resource, setting the minimum and maximum replica count is recommended. Those values constrain the autoscaling bounds.
HPA can scale based on the CPU utilization, memory usage, and custom metrics. Only CPU utilization is provided out of the box. The HorizontalPodAutoscaler definition specifies target values for those metrics. For instance, the spec sets a target CPU utilization. While pods are running, the HPA controller uses Kubernetes Metrics API to check each pod’s CPU utilization. It compares that value against the target utilization and calculates a ratio. It then uses the ratio to determine whether pods are overallocated or underallocated. It relies on the Kubernetes scheduler to assign new pods to nodes or remove pods from nodes.
There might be a race condition where (HPA) checks before a scaling operation is complete. The outcome might be an incorrect ratio calculation. For details, see Cooldown of scaling events.
If your workload is event-driven, a popular open-source option is KEDA. Consider KEDA if your workload is driven by an event source, such as message queue, rather than being CPU- or memory-bound. KEDA supports many event sources (or scalers). You can find the list of supported KEDA scalers here including the Azure Monitor scaler; a convenient way to scale KEDA workloads based on Azure Monitor metrics.
The cluster autoscaler is an AKS add-on component that scales the number of nodes in a node pool. It should be added during cluster provisioning. You need a separate cluster autoscaler for each user node pool.
The cluster autoscaler is triggered by the Kubernetes scheduler. When the Kubernetes scheduler fails to schedule a pod because of resource constraints, the autoscaler automatically provisions a new node in the node pool. Conversely, the cluster autoscaler checks the unused capacity of the nodes. If the node is not running at an expected capacity, the pods are moved to another node, and the unused node is removed.
When you enable autoscaler, set the maximum and minimum node count. The recommended values depend on the performance expectation of the workload, how much you want the cluster to grow, and cost implications. The minimum number is the reserved capacity for that node pool. In this reference implementation, the minimum value is set to 2 because of the simple nature of the workload.
For the system node pool, the recommended minimum value is 3.
Business continuity decisions
To maintain business continuity, define the Service Level Agreement for the infrastructure and your application. For information about monthly uptime calculation, see SLA for Azure Kubernetes Service (AKS).
To meet the minimum level of availability for workloads, multiple nodes in a node pool are needed. If a node goes down, another node in the node pool in the same cluster can continue running the application. For reliability, three nodes are recommended for the system node pool. For the user node pool, start with no less than two nodes. If you need higher availability, provision more nodes.
Isolate your application from the system services by placing it in a separate node pool. This way, Kubernetes services run on dedicated nodes and don’t compete with your workload. Use of tags, labels, and taints is recommended to identify the node pool to schedule your workload.
Regular upkeep of your cluster such as timely updates is crucial for reliability. Also monitoring the health of the pods through probes is recommended.
Ensure pod resources. It’s highly recommended that deployments specify pod resource requirements. The scheduler can then appropriately schedule the pod. Reliability will significantly deprecate if pods cannot be scheduled.
Set pod disruption budgets. This setting determines how many replicas in a deployment can come down during an update or upgrade event. For more information, see Pod disruption budgets.
Configure multiple replicas in the deployment to handle disruptions such as hardware failures. For planned events such as updates and upgrades, a disruption budget can ensure the required number of pod replicas exist to handle expected application load.
Set resource quotas on the workload namespaces. The resource quota on a namespace will ensure pod requests and limits are properly set on a deployment. For more information, see Enforce resource quotas.
Setting resources quotas at the cluster level can cause problem when deploying third-party workloads that do not have proper requests and limits.
Set pod requests and limits. Setting these limits allows Kubernetes to efficiently allocate CPU and, or memory resources to the pods and have higher container density on a node. Limits can also increase reliability with reduced costs because of better hardware utilization.
To estimate the limits, test and establish a baseline. Start with equal values for requests and limits. Then, gradually tune those values until you have established a threshold that can cause instability in the cluster.
Those limits can be specified in your deployment manifests. For more information, see Set pod requests and limits.
Availability zones and multi-region support
If your SLA requires a higher uptime, protect against loss in a zone. You can use availability zones if the region supports them. The nodes in the user node pool are then able to spread across zones. If an entire zone is unavailable, a node in another zone within the region is still available. Each node pool maps to a separate virtual machine scale set, which manages node instances and scalability. Scale set operations and configuration managed by the AKS service. Here are some considerations when enabling multizone:
Entire infrastructure. Choose a region that supports availability zones. For more information, see Limitations and region availability. If you want to buy an Uptime SLA, choose a region that supports that option.
Cluster. Availability zones can only be set when the node pool is created and can't be changed later. The node sizes should be supported in all zones so that the expected distribution is possible. The underlying virtual machine scale set provides the same hardware configuration across zones.
Multizone support only applies to node pools. The AKS API server is in a single zone. If the API server becomes unavailable as part of a zone failure, pods deployed on node pools will continue to run, however Kubernetes will lose orchestration capabilities, and workload might be affected.
Dependent resources. For complete zonal benefit, all service dependencies must also support zones. If a dependent service doesn't support zones, it's possible that a zone failure could cause that service to fail.
For example, a managed disk is available in the zone in which it's provisioned. In case of a failure, the node might move to another zone, but the managed disk won’t move with the node to that zone.
For simplicity, in this architecture AKS is deployed to a single region with node pools spanning availability zones 1, 2, and 3. Other resources of the infrastructure, such as Azure Firewall and Application Gateway are deployed to the same region also with multizone support. Geo-replication is enabled for Azure Container Registry.
Enabling availability zones won’t be enough if the entire region goes down. To have higher availability, run multiple AKS clusters, in different regions.
Use paired regions. Consider using a CI/CD pipeline that is configured to use a paired region to recover from region failures. A benefit of using paired regions is reliability during updates. Azure makes sure that only one region in the pair is updated at a time. Certain DevOps tools such as flux can make the multi-region deployments easier.
If an Azure resource supports geo-redundancy, provide the location where the redundant service will have its secondary. For example, enabling geo-replication for Azure Container Registry will automatically replicate images to the selected Azure regions, and will provide continued access to images even if a region were experiencing an outage.
Choose a traffic router that can distribute traffic across zones or regions, depending on your requirement. This architecture deploys Azure Load Balancer because it can distribute non-web traffic across zones. If you need to distribute traffic across regions, Azure Front Door should be considered. For other considerations, see Choose a load balancer.
In case of failure in the primary region, you should be able to quickly create a new instance in another region. Here are some recommendations:
Use paired regions.
A non-stateful workload can be replicated efficiently. If you need to store state in the cluster (not recommended), make sure you back up the data frequently in the paired region.
Integrate the recovery strategy, such as replicating to another region, as part of the DevOps pipeline to meet your Service Level Objectives (SLO).
When provisioning each Azure service, choose features that support disaster recovery. For example, in this architecture, Azure Container Registry is enabled for geo-replication. If a region goes down, you can still pull images from the replicated region.
Kubernetes API Server Uptime SLA
AKS can be used as a free service, but that tier doesn't offer a financially backed SLA. To obtain that SLA, you must choose to add an Uptime SLA to your purchase. We recommend all production clusters use this option. Reserve clusters without this option for pre-production clusters. When combined with Azure Availability Zones, the Kubernetes API server SLA is increased to 99.95%. Your node pools, and other resources are covered under their own SLA.
There’s a cost-to-availability tradeoff for deploying the architecture across zones and especially regions. Some replication features, such as geo-replication in Azure Container Registry, are available in premium SKUs, which is more expensive. The cost will also increase because bandwidth charges that are applied when traffic moves across zones and regions.
Also, expect additional network latency in node communication between zones or regions. Measure the impact of this architectural decision on your workload.
Test with simulations and forced failovers
Ensure reliability through forced failover testing with simulated outages such as bring down a node, bringing down all AKS resources in a particular zone to simulate a zonal failure, or bringing down an external dependency.
Monitor and collect metrics
The Azure Monitor for containers feature is the recommended tool for monitoring and logging because you can view events in real time. It captures container logs from the running pods and aggregates them for viewing. It also collects information from Metrics API about memory and CPU utilization to monitor the health of running resources and workloads. You can use it to monitor performance as the pods scale. Another advantage is that you can easily use Azure portal to configure charts and dashboards. It has the capability to create alerts that trigger Automation Runbooks, Azure Functions, and others.
Most workloads hosted in pods emit Prometheus metrics. Azure Monitor is capable of scraping Prometheus metrics and visualizing them.
There are some third-party utilities integrated with Kubernetes. Take advantage of log and metrics platforms such as Grafana or Datadog, if your organization already uses them.
With AKS, Azure manages some core Kubernetes services. Logs from those services should only be enabled per request from customer support. However, it is recommended that you enable these log sources as they can help you troubleshoot cluster issues:
- Logging on the ClusterAutoscaler to gain observability into the scaling operations. For more information, see Retrieve cluster autoscaler logs and status.
- KubeControllerManager to have observability into pod scheduler.
- KubeAuditAdmin to have observability into activities that modify your cluster.
Monitor the health of pods by setting Liveness and Readiness probes. If an unresponsive pod is detected, Kubernetes restarts the pod. Liveness probe determines if the pod is healthy. If it does not respond, Kubernetes will restart the pod. Readiness probe determines if the pod is ready to receive requests/traffic.
AKS provides built-in self-healing of infrastructure nodes using Node Auto-Repair.
Keep the Kubernetes version up to date with the supported N-2 versions. Upgrading to the latest version of Kubernetes is critical because new versions are released frequently.
For more information, see Regularly update to the latest version of Kubernetes and Upgrade an Azure Kubernetes Service (AKS) cluster.
AKS provides new node images that have the latest OS and runtime updates. These new images are not automatically applied. You are responsible for deciding how often the images should get updated. It's recommended that you have a process to upgrade your node pools' base image weekly. For more information, see Azure Kubernetes Service (AKS) node image upgrade the AKS Release Notes.
Between image upgrades, AKS nodes download and install OS and runtime patches, individually. An installation might require the node VMs to be rebooted. AKS will not reboot nodes due to pending updates. Have a process that monitors nodes for the applied updates that require a reboot and performs the reboot of those nodes in a controlled manner. An open-source option is Kured (Kubernetes reboot daemon).
Keeping your node images in sync with the latest weekly release will minimize these occasional reboot requests while maintaining an enhanced security posture. Relying just on node image upgrades will ensure AKS compatibility and weekly security patching. Whereas, applying daily updates will fix security issues faster, they haven't necessarily been tested in AKS. Where possible, use node image upgrade as your primary weekly security patching strategy.
Monitor your container infrastructure for both active threats and potential security risks:
- Enable Azure Defender for Kubernetes for threat detection on your Kubernetes clusters.
- Use Azure Security Center (ASC) to monitor Kubernetes security posture.
- For information about security hardening applied to AKS virtual machine hosts, see Security Hardening in host OS.
Cluster and workload operations (DevOps)
Here are some considerations. For more information, see the Operational Excellence pillar.
Isolate workload responsibilities
Divide the workload by teams and types of resources to individually manage each portion.
Start with a basic workload that contains the fundamental components and build on it. An initial task would be to configure networking. Provision virtual networks for the hub and spoke and subnets within those networks. For instance, the spoke has separate subnets for system and user node pools, and ingress resources. A subnet for Azure Firewall in the hub.
Another portion could be to integrate the basic workload with Azure Active Directory.
Use Infrastructure as Code (IaC)
Choose an idempotent declarative method over an imperative approach, where possible. Instead of writing a sequence of commands that specify configuration options, use declarative syntax that describes the resources and their properties. One option is an Azure Resource Manager (ARM) templates another is Terraform.
Make sure as you provision resources as per the governing policies. For example, when selecting the right VM sizes, stay within the cost constraints, availability zone options to match the requirements of your application.
If you need to write a sequence of commands, use Azure CLI. These commands cover a range of Azure services and can be automated through scripting. Azure CLI is supported on Windows and Linux. Another cross-platform option is Azure PowerShell. Your choice will depend on preferred skillset.
Store and version scripts and template files in your source control system.
Pipelines for workflow and deployment must have the ability to build and deploy applications continuously. Updates must be deployed safely and quickly and rolled back in case there are issues.
Your deployment strategy must include a reliable and an automated continuous delivery (CD) pipeline. Changes to your workload container images should be automatically deployed to the cluster.
Instead of using an imperative approach like kubectl, use tools that automatically synchronize cluster and repository changes. To manage the workflow, such as release of a new version and validation of that version before deploying to production, consider a GitOps flow. An agent is deployed in the cluster to make sure that the state of the cluster is coordinated with configuration stored in your private Git repo. Kubernetes and AKS do not support that experience natively. A recommended option is flux. It uses one or more operators in the cluster to trigger deployments inside Kubernetes. flux does these tasks:
- Monitors all configured repositories.
- Detects new configuration changes.
- Triggers deployments.
- Updates the desired running configuration based on those changes.
You can also set policies that govern how those changes are deployed.
Here’s an example from the reference implementation that shows how to automate cluster configuration with GitOps and Flux.
A developer commits changes to source code, such as configuration YAML files, which are stored in a git repository. The changes are then pushed to a git server.
flux runs in pod in alongside the workload. flux has read-only access to the git repository to make sure that flux is only applying changes as requested by developers.
flux recognizes changes in configuration and applies those changes using kubectl commands.
Developers do not have direct access to the Kubernetes API through kubectl. Have branch policies on your git server. That way, multiple developers can approve a change before it’s applied to production.
Workload and cluster deployment strategies
Deploy any change (architecture components, workload, cluster configuration), to at least one pre-production AKS cluster. Doing so will simulate the change might unravel issues before deploying to production.
Run tests/validations at each stage before moving on to the next to make sure you can push updates to the production environment in a highly controlled way and minimize disruption from unanticipated deployment issues. This deployment should follow a similar pattern as production, using the same GitHub Actions pipeline or Flux operators.
Advanced deployment techniques such as Blue-green deployment, A/B testing, and Canary releases, will require additional process and potentially tooling. Flagger is a popular open-source solution to help solve for your advanced deployment scenarios.
Use the Azure pricing calculator to estimate costs for the services used in the architecture. Other best practices are described in the Cost Optimization section in Microsoft Azure Well-Architected Framework.
There are no costs associated for AKS in deployment, management, and operations of the Kubernetes cluster. The main cost driver is the virtual machine instances, storage, and networking resources consumed by the cluster. Consider choosing cheaper VMs for system node pools. The recommended SKU is DS2_v2.
Don’t have the same configuration for dev/test and production environments. Production workloads have extra requirements for high availability and will be more expensive. It may not be necessary in the dev/test environment.
For production workloads, add an Uptime SLA. However, there are savings for clusters designed for dev/test or experimental workloads where availability is not required to be guaranteed. For instance, the SLO is sufficient. Also, if your workload supports it, consider using dedicated spot node pools that run Spot VMs.
For non-production workloads that include Azure SQL Database or Azure App Service as part of the AKS workload architecture, evaluate if you are eligible to use Azure Dev/Test subscriptions to receive service discounts.
Instead of starting with an oversized cluster to meet the scaling needs, provision a cluster with minimum number of nodes and enable the cluster autoscaler to monitor and make sizing decisions.
Set pod requests and limits to allow Kubernetes to allocate node resources with higher density so that hardware is utilized to capacity.
Enabling diagnostics on the cluster can increase the cost.
If your workload is expected exist for a long period, you can commit to one- or three-year Reserved Virtual Machine Instances to reduce the node costs. For more information, see Reserved VMs.
Use tags when you create node pools. Tags are useful in creating custom reports to track the incurred costs. Tags give the ability to track the total of expenses and map any cost to a specific resource or team. Also, if the cluster is shared between teams, build chargeback reports per consumer to identify metered costs for shared cloud services. For more information, see Specify a taint, label, or tag for a node pool.
Data transfers within availability zones of a region are not free. If your workload is multi-region or there are transfers across billing zones, then expect additional bandwidth cost. For more information, see Traffic across billing zones and regions.
Create budgets to stay within the cost constraints identified by the organization. One way is to create budgets through Azure Cost Management. You can also create alerts to get notifications when certain thresholds are exceeded. For more information, see Create a budget using a template.
In order to monitor cost of the entire cluster, along with compute cost also gather cost information about storage, bandwidth, firewall, and logs. Azure provides various dashboards to monitor and analyze cost:
Ideally, monitor cost in real time or at least at a regular cadence to take action before the end of the month when costs are already calculated. Also monitor the monthly trend over time to stay in the budget.
To make data-driven decisions, pinpoint which resource (granular level) incurs most cost. Also have a good understanding of the meters that are used to calculate usage of each resource. By analyzing metrics, you can determine if the platform is over-sized for instance. You can see the usage meters in Azure Monitor metrics.
Act on recommendations provided by Azure Advisor. There are other ways to optimize:
Enable the cluster autoscaler to detect and remove underutilized nodes in the node pool.
Choose a lower SKU for the node pools, if your workload supports it.
If the application doesn’t require burst scaling, consider sizing the cluster to just the right size by analyzing performance metrics over time.
For other cost-related information, see AKS pricing.
- To learn about hosting Microservices on AKS, see Microservices architecture on Azure Kubernetes Service (AKS).
- The see the AKS product roadmap, see Azure Kubernetes Service Roadmap on GitHub.