Industry use cases of AKS(Azure Kubernetes Service)

What is Azure Kubernetes Service?

Microsoft Azure is a world-renown cloud platform for SMBs to large-scale businesses, while Kubernetes is a modern-day approach that is rapidly becoming the regular methodology to manage cloud-native applications in a production environment. Azure Kubernetes Service (AKS) has brought both solutions together that allow customers to create fully-managed Kubernetes clusters quickly and easily.

AKS is an open-source fully managed container orchestration service that became available in June 2018 and is available on the Microsoft Azure public cloud that can be used to deploy, scale, and manage Docker containers and container-based applications in a cluster environment.

Azure Kubernetes Service offers provisioning, scaling, and upgrades of resources as per requirement or demand without any downtime in the Kubernetes cluster and the best thing about AKS is that you don’t require deep knowledge and expertise in container orchestration to manage AKS.

AKS is certainly an ideal platform for developers to develop their modern applications using Kubernetes on the Azure architecture where Azure Container Instances are the pretty right choice to deploy containers on the public cloud. The Azure Container Instances help in reducing the stress on developers to deploy and run their applications on Kubernetes architecture.

AKS features and benefits

The primary benefits of AKS are flexibility, automation and reduced management overhead for administrators and developers. For example, AKS automatically configures all Kubernetes masters and nodes during the deployment process, and handles a range of other tasks, including Azure Active Directory integration, connections to monitoring services and configuring advanced networking features, such as HTTP application routing.

Since AKS is a managed service, Microsoft handles all Kubernetes upgrades for the service, as new versions become available. Users can decide whether and when to upgrade the Kubernetes version in their own AKS cluster to reduce the possibility of accidental workload disruption.

In addition, AKS nodes can scale up or down to accommodate fluctuations in resource demands. For additional processing power, AKS also supports node pools enabled by graphics processing units (GPUs). This can be vital for compute-intensive workloads, such as scientific applications.

Users can access AKS via an AKS management portal, an AKS command-line interface (CLI), or using templates through tools such as Azure Resource Manager. The service also integrates with the Azure Container Registry (ACR) for Docker image storage, and supports the use of persistent data with Azure Disks.

Accelerate containerized application development

Easily define, deploy, debug, and upgrade even the most complex Kubernetes applications, and automatically containerize your applications. Use modern application development to accelerate time to market.

Add a full CI/CD pipeline to your AKS clusters with automated routine tasks, and set up a canary deployment strategy in just a few clicks. Detect failures early and optimize your pipelines with deep traceability into your deployments.

Gain visibility into your environment with the Kubernetes resources view, control-plane telemetry, log aggregation, and container health, accessible in the Azure portal and automatically configured for AKS clusters.

Increased operational efficiency

Rely on built-in automated provisioning, repair, monitoring, and scaling. Get up and running quickly and minimize infrastructure maintenance.

• Easily provision fully managed clusters with Prometheus based monitoring capabilities.
• Use Azure Advisor to optimize your Kubernetes deployments with real-time, personalized recommendations.
• Save on costs by using deeply discounted capacity with Azure Spot.
• Elastically add compute capacity with serverless Kubernetes, in seconds.
• Achieve higher availability and protect applications from datacenter failures using availability zones

Build on an enterprise-grade, more secure foundation

1. Dynamically enforce guardrails defined in Azure Policy at deployment or in CI/CD workflows. Deploy only validated images to your private container registry.
2. Get fine-grained identity and access control to Kubernetes resources using Azure Active Directory.
3. Enforce pod security context and configure across multiple clusters with Azure Policy. Track, validate, reconfigure, and get compliance reports easily.
4. Achieve superior security with a hardened operating system image, automated patching, and more. Automate threat detection and remediation using Azure Security Center.
5. Use Azure Private Link to limit Kubernetes API server access to your virtual network. Use network policy to secure your communication paths.

Run any workload in the cloud, at the edge, or as a hybrid

Orchestrate any type of workload running in the environment of your choice. Whether you want to move .NET applications to Windows Server containers, modernize Java applications in Linux containers, or run microservices applications in the public cloud, at the edge, or in hybrid environments, Azure has the solution for you.

Common uses for Azure Kubernetes Service (AKS)

Lift and shift to containers with AKS

Easily migrate existing application to container(s) and run within the Azure managed Kubernetes service (AKS).

Microservices with AKS

Use AKS to simplify the deployment and management of microservices based architecture. AKS streamlines horizontal scaling, self-healing, load balancing, secret management.

Secure DevOps for AKS

DevOps and Kubernetes are better together. Implementing secure DevOps together with Kubernetes on Azure, you can achieve the balance between speed and security and deliver code faster at scale.

Bursting from AKS with ACI

Use the AKS virtual node to provision pods inside ACI that start in seconds. This enables AKS to run with just enough capacity for your average workload.

Azure IoT reference architecture

This reference architecture shows a recommended architecture for IoT applications on Azure using PaaS (platform-as-a-service) components

Machine Learning model training with AKS

Training of models using large datasets is a complex and resource intensive task. Use familiar tools such as TensorFlow and Kubeflow to simplify training of Machine Learning models.

Data Streaming scenario

Use AKS to easily ingest & process a real-time data stream with millions of data points collected via sensors. Perform fast analysis and computations to develop insights into complex scenarios quickly.