Why Bosch choose the cloud way?
Kubernetes is an open-source container orchestration platform that enables the operation of an elastic web server framework for cloud applications. Kubernetes can support data center outsourcing to public cloud service providers or can be used for web hosting at scale.
More and more industries in the market have either completely migrated to this containerized architecture or are in the way of doing so.
The fast deployment and security are just a tip of the iceberg when we consider the benefits of such architecture.
Pods are the smallest, most basic deployable objects in Kubernetes. A Pod represents a single instance of a running process in your cluster. Pods contain one or more containers, such as Docker containers. When a Pod runs multiple containers, the containers are managed as a single entity and share the Pod’s resources.
Most of the industries use kubernetes over a cloud platform. This allows them to leverage the benefits of the cloud services along with the kubernetes.
So today I would be dwelling about one such company.
Bosch
Robert Bosch GmbH, commonly known as Bosch, is a German multinational engineering and technology company headquartered in Gerlingen. The company was founded by Robert Bosch in Stuttgart in 1886. Bosch is 92% owned by Robert Bosch Stiftung, a charitable institution.
Bosch invests heavily in research and development (R&D), staffing more than 70,000 employees in R&D alone. These R&D staffers, along with 27,000 software and IT experts and 1,000 AI specialists, help maintain its position as Europe’s top provider of automotive solutions.
We don’t use any technology without any specific reason until we have a problem to solve.
The 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.
Solution
The team decided to offload the work of scaling and cluster maintenance to a managed service in a public cloud with a global reach. Thanks to the trusted partnership Bosch had with Microsoft, Azure Kubernetes Service was the obvious choice. 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 also offered the simplicity of a managed Kubernetes service in the cloud. It provided the elastic provisioning that Bosch wanted, without the need to manage its own infrastructure. In addition, the developers did not have to rethink all their design decisions. Instead, they could take the core business logic developed on-premises using the open-source tools they knew and run the solution virtually as is, within a faster infrastructure with a worldwide reach. The developers can deploy self-managed AKS clusters as needed, and they get the benefit of running their services within a secured network environment.
In addition, by running their solution on Azure and AKS, the average time to calculate whether a driver is going the wrong way could be improved to approximately 60 milliseconds.
