Case Study: Azure Kubernetes Services
Azure Kubernetes Service(AKS) is Managed Kubernetes Services of Azure that many industries are benefitting. In this article, we are going to discuss what is AKS, how AKS works, and how industries are getting benefits from this.
Before jumping to the use cases let’s understand what is Azure and Kubernetes.
What is Azure?
Azure is a public cloud platform that provides more than 200+ services including Infrastructure as a Service(IaaS), Platform as a Service(Paas), Software as a Service(SaaS).
In Azure public cloud, you ask whatever you need for your infrastructure, you don’t have to worry about management, Azure manages all the backend activities for you.
Using public cloud you actually don’t have to pay an upfront amount of infrastructure, you pay what you use.
What is Kubernetes?
Kubernetes is a portable, extensible, and open-source platform that provides container management service. It facilitates both declarative configuration and automation.
Kubernetes has a large, rapidly growing ecosystem. In this era, when everyone is running for 100% uptime of their environment, Kubernetes is a boon.
What is AKS?
Azure Kubernetes Services(AKS) is a Managed Kubernetes Service provided by Azure. In AKS, you can deploy and manage containerised applications in s very easy manner.
Azure Kubernetes provide serverless, an integrated continuous integration and continuous delivery (CI/CD).
On AKS, you can unite your development and operations teams on a single platform to rapidly build, deliver and scale applications.
Why to use AKS?
- Accelerate containerized application development: To accelerate the market use of modern application development. In AKS, you can easily define, deploy, and upgrade the most complex Kubernetes structures.
- Increased Optional efficiency: AKS provides an automated provisioning, repair, monitoring, and scaling.
- Easily provision your cluster with Prometheus and other monitoring tools.
- Azure Advisor helps in real-time optimization of Kubernetes deployment.
- Azure Spot saves your cost a lot by deeply discounted capacity.
- Secured Foundation: Azure active directory help to get fine-grained identity and access control to Kubernetes.
Common uses with AKS
Lift and shift to container 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.
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.
Case Study on BOSCH
When Robert Bosch GmbH set out to solve the problem of drivers going the wrong way on highways, the goal was to save lives. Other services like this existed in Germany, but precision and speed cannot be compromised. Could Bosch get precise enough location data—in real time—to do this? The company knew it had to try.
The result is the wrong-way driver warning (WDW) service and software development kit (SDK). Designed for use by app developers and original equipment manufacturers (OEMs), the architecture pivots on an innovative map-matching algorithm and the scalability of Microsoft Azure Kubernetes Service (AKS) in tandem with Azure HDInsight tools that integrate with the Apache Kafka streaming platform.
The right way to solve wrong way problem
The Bosch team had to solve two major issues: first, to get the last piece of information out of the noisy sensor data; and second, to develop a highly scalable and ultra-flexible service to process the data in near real time. The question was how to build a real-time data ingestion and processing pipeline capable of returning notifications to drivers within seconds.
The problem was speed. The team assumed that devices emitting location information, such as smartphone apps and automotive head units, could eventually send thousands of data points to the solution per second, from all over Europe and eventually other countries. Bosch needed lightning fast compute capable of filtering events and pushing a notification back to an end device within 10 seconds—the time estimated to make the solution viable.
A team of Microsoft cloud solution architects worked closely with Bosch engineers, who provided valuable feedback to Azure product teams. Microsoft continues to work with Bosch teams around the world. Working together, they devised a solution that produced the speed Bosch needed.
The key was orchestration. By orchestrating the deployment of containers using AKS, Bosch would get repeatable, manageable clusters of containers. Bosch already had a continuous integration (CI) and continuous deployment (CD) process to use in producing the container images and orchestration. The result: increased speed and reliability of deployments.
AKS provides the elastic provisioning that Bosch wanted, without the need to manage its own environment. The developers can deploy self-managed AKS clusters as needed, and they get the benefit of running their services within a secured network environment.
How solution works?
The wrong-way driver warning solution runs as a service on Azure and provides an SDK. Service providers, such as smartphone app developers and OEM partners, can install the WDW SDK to make use of the service within their products. The SDK maintains a list of hotspots within which GPS data is collected anonymously. These hotspots include specific locations, such as segments of divided highways and on-ramps. Every time a driver enters a hotspot, the client generates a new ID, so the service remains anonymous.
When a driver using a WDW-configured app or in-car system enters a hotspot, the WDW SDK begins to collect GPS signals and sensor events, such as acceleration and rotational data and heading information. These data points are packaged as observations and sent in the frequency of 1 Hertz (Hz)—one event per second—via HTTP to the WDW service on Azure, either directly or to the service provider’s back end, and then to Azure. The SDK supports both routes so that service providers stay in charge of the data that is sent to the WDW system.
If the WDW service determines that the driver is going the wrong way within a hotspot, it sends a notification to the originating device and to other drivers in the vicinity who are also running an app with the WDW SDK.
Getting accuracy from GPS Data
The team’s biggest technical challenge was to improve the reliability of the incoming GPS data. Bosch developed a custom sensor data-fusion and map-matching algorithm to verify a driver’s location and driving direction. Then the algorithm filters all suspicious trips and forwards them to the alert validator app. This multistep classification approach was used to reduce the computational complexity required for a cost-effective solution architecture.
Additional Azure services
Bosch also used following set of services:
- Azure API Management provides the gateway to the back end. It pushes observations from client devices, currently serving about 6 million requests per day.
- Azure App Service was used to build and host multiple internal front ends used by the team for debugging and monitoring. For example, a real-time dashboard shows all the drivers currently passing a hotspot. App Service supports both Windows and Linux and works with the team’s automated deployment pipeline.
- Azure Content Delivery Network (CDN) uses the closest point of presence (POP) server to cache static objects locally, thus reducing load times, saving bandwidth, and speeding responsiveness of the WDW service.
- Azure Databricks is an Apache Spark–based analytics platform designed to support team collaboration. It enables Bosch data scientists, data engineers, and business analysts to make the most of the WDW service’s big data pipeline.
Azure Kubernetes Service is a very powerful auto managed service. It automatically scale up, scale down the cluster nodes as per the needs.
Like Bosch many other industries using AKS and solving their use cases in a very effective manner.
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