In this article, dive into how to harness AKS’ powerful features like auto-scaling and high availability to manage variable workloads and monitoring to maintain continuous service.
What We'll Cover:
- Deploying on Azure Kubernetes Service (AKS)
- Configuring AKS for dynamic application scaling
- Ensuring High Availability with Azure Load Balancer and AKS
Scaling Intelligent Apps on Azure Kubernetes Services (1)
In the first topic of this week, we explored how to use Azure Cognitive Services for Vision within Azure Kubernetes Service (AKS) to create an Intelligent App capable of analyzing images and extracting valuable data through optical character recognition (OCR). The week’s second article, focused on the advantages of Azure Cosmos DB, highlighting its multi-model database and ability to handle diverse data resulting from OCR analysis in our Intelligent App.
In this third topic, we’ll configure Azure Kubernetes Service (AKS) to manage varying workloads and smoothly scale our Intelligent App, ensuring it can handle intense and fluctuating workloads without compromising performance or availability.
Mastering Azure Kubernetes Services (AKS) for Intelligent App
At the core of the Azure ecosystem lies AKS. It plays a crucial role in optimizing application performance and efficient resource use. We can deploy, manage, and scale applications across different environments by precisely orchestrating containerized workloads.
AKS offers several robust features that help applications scale smoothly and stay available even during heavy demand, including the following:
- The cluster autoscaler adjusts the number of nodes in your cluster based on how much work you have. This way, it uses resources efficiently and keeps costs in check.
- The Azure Load Balancer is created automatically with AKS and acts like an intelligent traffic controller. It ensures incoming network traffic is distributed well across your cluster’s nodes, optimizing resource usage.
Together, these features give your applications a solid base to handle different workloads and keep running nonstop.
The following sections provide hands-on demonstrations of setting up, monitoring, and optimizing AKS to drive application efficiency and performance
Prerequisites
To follow this tutorial, ensure you have the following:
- Read through the first and second topics of this week
- Python 3.7 or later installed
- VS Code or another integrated development environment (IDE) for writing Python code
- The sample Python application from the second article of this series downloaded. Open the project before getting started.
- pip, the package manager for Python, installed
- A free Azure account. Sign up if you don’t have one yet.
- The Azure command-line interface (CLI)
Check out the complete project code for a preview of the final project.
Complete the Apps Cloud Skills Challenge to build on your AKS app dev skills.
To start with the basics for developing Kubernetes