Kubernetes has become the de-facto standard container orchestration framework for cloud-native deployments. Development teams have turned to Kubernetes to support their migration to new microservices architectures and a DevOps culture for continuous integration and continuous deployment.
At the same time, many organizations are going through a digital transformation process. Their goal is to change how they connect with their customers, suppliers and partners. These organizations are taking advantage of innovations offered by technologies such as IoT platforms, big data analytics, or machine learning to modernize their enterprise IT and OT systems. They realize that the complexity of development and deployment of new digital products require new development processes. Consequently, they turn to agile development and infrastructure tools such as Kubernetes.
In fact, Kubernetes addresses many of the complexity challenges that development teams see when building and deploying IoT solutions.
Enabling DevOps for IoT
Customer and market demands often require IoT solutions to have the ability to quickly deploy new features and updates. Kubernetes provides a unified deployment model that allows DevOps teams to quickly and automatically test and deploy new services. It supports zero-downtime deployments in form of rolling updates. This allows mission-critical IoT solutions to be kept up-to-date with no impact on end users (customers).
Scalability is a key concern for many IoT solutions. The ability to handle thousands or even millions of device connections, sending terabytes of data and messages and providing services such as real-time analytics requires a deployment infrastructure that can scale up and down to meet the demands of an IoT deployment. Kubernetes provides the ability to automatically scale Kubernetes Pods across network clusters.
Many IoT solutions are considered business critical systems that need to be reliable and available. For instance, an IoT solution critical to the operation of a factory needs to be available at all time. Kubernetes provides the tooling required to deploy highly available services. It’s architecture also allows for workloads to run independently. In addition, they can be restarted or recreated with no effect to end-users.
Efficient use of cloud resources
IoT solutions are often a set of connected services. They handle device connectivity and management, data ingestion, data integration, analytics or integration with IT and OT systems, among others. These services will often run on public cloud providers, such as AWS or MS Azure. This makes the efficient use of cloud provider resources an important consideration towards the total cost to manage and deploy these services. Kubernetes creates an abstraction layer on top of the underlying virtual machines. Administrators are able to focus on the deployment of the IoT services across the optimal number of VMs as opposed to a single service on a single VM.
A key trend in the IoT industry is the deployment of IoT services to the edge of the network. For instance, to improve the responsiveness of a predictive maintenance solution, it might be more efficient to deploy the data analytics and machine learning services closer to the equipment being monitored. Running IoT services in a distributed and federated manner creates a new management challenge for system administrators and developers. However, Kubernetes provides a common platform that could be used for deploying IoT services at the edge. In fact, a new Kubernetes IoT Working Group is investigating how it can provide a consistent deployment model for IoT cloud and IoT Edge.
The Kubernetes community is rapidly advancing and innovating around the Kubernetes platform. These advancements are making it possible to build cloud-native IoT solutions that are scalable, reliable and deployed in a distributed federation. It is clear Kubernetes has become a key enabling technology for IoT solutions.