Azure Kubernetes Service (AKS) and it’s use cases
Microsoft Azure is one of the foremost players in the cloud computing market presently. On the other hand, we also have Kubernetes as the top instrument for the management of application containers. The drastic rise in the application of both these tools in enterprise use cases is explicitly evident. Kubernetes provides a platform for automation of deployment, scaling, and operations of application containers across clusters of hosts.
Also, Kubernetes helps in reducing the cost of cloud computing investments alongside simplifying architecture and operations. Therefore, Azure Kubernetes Service combines the functionalities of Kubernetes and Microsoft Azure for better outcomes in application development.
Defining Azure Kubernetes Service (AKS)
AKS is a managed container orchestration platform that has its foundation in the opensource Kubernetes system. AKS is available on Microsoft Azure public cloud, and its applications are ideal for deploying, scaling and managing Docker containers. Also, container-based applications come within the scope of AKS.
The deployment, scaling, and management of containers and container-based applications are possible across a cluster of container hosts. As of June 2018, Azure Kubernetes Service AKS is available for general access. AKS helps in managing a hosted Kubernetes environment that supports flexible and quick deployment and management of containerized applications.
The best thing here is that we don’t need profound expertise in container orchestration for using AKS. Furthermore, we are able to reduce the stress of ongoing maintenance and operations as AKS supports efficient provisioning, upgrades, and scaling of resources according to demand without taking the applications offline.
AKS is surely an improvisation for developers to use Kubernetes on the Azure cloud platform. Azure Container Service or Azure Container Instances can be the right example of tools for deploying containers on Azure public cloud. The Azure Container Instances can reduce the stress on a developer to implement Kubernetes for deploying and running containers.
Azure Container Service was the predecessor of AKS and supported various opensource container orchestration platforms. However, ACS and AKS have many differences other than the fact that AKS is ideal for Kubernetes. For instance, AKS implements managed disks, thereby implying the need for converting unmanaged disks before assigning to AKS nodes.
On similar grounds, users should convert the persistent storage volumes or the customized storage class objects related to Azure disks. Furthermore, users should also ensure due diligence for stateful applications before migration from ACS to AKS.
Now, it’s time to reflect on the features of AKS Azure!
Features of AKS
- Security, Access, and Monitoring : Common Azure Kubernetes Service documentation can shed light on the improvements in areas of access, security, and monitoring. AKS helps in integration with Azure Active Directory and using Kubernetes role-based access controls. Also, you could monitor the status of your cluster and resources.RBAC helps in controlling access to Kubernetes resources and namespaces as well as permission to resources. Azure AD integration helps in configuration of Kubernetes access based on existing identity and group membership. Azure Monitor can help in observing the performance of your AKS Azure cluster and deployed applications.
- Clusters and Nodes :Azure Kubernetes Service tutorial also include elaborations on the feature of clusters and nodes in AKS. AKS nodes run on Azure Virtual Machines, and AKS provides support for Kubernetes clusters running multiple node pools. With the changes in demand for resources, the number of cluster nodes or pods running services can be scaled accordingly.AKS also provides different Kubernetes versions and the cluster upgrades through the Azure Portal or CLI. You could also find support for creating GPU enabled node pools. Also, you have the privilege of mounting storage volumes for persistent data. You can use static and dynamic volumes according to your preference. The two options for storage volume support with Azure Kubernetes Service include Azure Disks and Azure Files.
- Virtual Networks and Ingress: Azure Kubernetes Service documentation will also include virtual networks and ingress. You can deploy an AKS cluster into an existing virtual network. Every pod in the cluster is attached with an IP address in the virtual network. The pods can communicate with other pods in the cluster as well as other nodes in the virtual network. The add-on of HTTP application routing can help in having flexible access to applications deployed on an AKS cluster. HTTP application routing solution can help in configuration of an ingress controller in an AKS cluster.
- Development Tooling Integration: The next important concern in Azure Kubernetes Service is Development tooling integration. You can find a wide ecosystem having development and management tools such as Draft and Helm. The seamless functioning of these tools with AKS is a prolific reason to consider using AKS. Most important of all, the Azure Dev Spaces can give a faster, iterative Kubernetes development experience. You could run and debug containers directly in AKS with the least stress on the configuration.Azure Kubernetes Service also provides support for Docker image format. Furthermore, you could also integrate AKS with Azure Container Registry (ACR) for the private storage of Docker images. Also, AKS establishes credibility through the CNCF certification of “Kubernetes conformant.” The regulatory compliance of AKS with SOC, HIPAA, ISO, and PCI DSS make it reliable for application across diverse industries.
Use Cases of Azure Kubernetes Services
1. Migration of existing applications:
You can easily migrate existing apps to containers and run them with Azure Kubernetes Service. You can also control access via Azure AD integration and SLA-based Azure Services like Azure Database using Open Service Broker for Azure (OSBA).
2. Simplifying the configuration and management of microservices-based Apps:
You can also simplify the development and management of microservices-based apps as well as streamline load balancing, horizontal scaling, self-healing, and secret management with AKS.
3. Bringing DevOps and Kubernetes together:
AKS is also a reliable resource to bring Kubernetes and DevOps together for securing DevOps implementation with Kubernetes. Bringing both together, it improves the security and speed of the development process with Continuous Integration and Continuous Delivery (CI/CD) with dynamic policy controls.
4. Ease of scaling:
AKS can also be applied in many other use cases such as ease of scaling by using Azure Container Instances (ACI) and AKS. By doing this, you can use AKS virtual node to provision pods inside Azure Container Instance (ACI) that start within a few seconds and enables AKS to run with required resources. If your AKS cluster is run out of resources, if will scale-out additional pods automatically without any additional servers to manage in the Kubernetes environment.
5. Data streaming:
AKS can also be used to ingest and process real-time data streams with data points via sensors and perform quick analysis.
6. Constant monitoring can also help in accelerating the feedback loop. Azure pipelines can help in faster delivery alongside compliance with critical policies specified by Azure. Microsoft Azure also gives features for observing the build and release pipelines in real-time and features for easy application of compliance audit and reconfigurations.
7. Azure Kubernetes Service is also applicable in many other use cases. One of them is the ease of scaling by using AKS and Azure Container Instances. Also, you can find the applications of AKS for on-demand IoT device deployment and management. AKS can provide scalable compute resources for IoT solutions according to demand. AKS could also find applications in use cases that involve machine learning model training. Flexible tools in AKS such as Kubeflow and TensorFlow help in simplifying the training of machine learning models.