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Simplified view showing how Services interact with Pod networking in a Kubernetes cluster. A Kubernetes service is a set of pods that work together, such as one tier of a multi-tier application. The set of pods that constitute a service are defined by a label selector. [31]
Container clusters need to be managed. This includes functionality to create a cluster, to upgrade the software or repair it, balance the load between existing instances, scale by starting or stopping instances to adapt to the number of users, to log activities and monitor produced logs or the application itself by querying sensors.
A cluster IP is a term in cloud computing to refer to a proxy that represents a computer cluster with a single IP address. [1] It is a term used by the cloud computing system Kubernetes (stylised as ClusterIP ) to provide load balancing to IP addresses for devices in the internal network.
Cilium began as a networking project and has many features that allow it to provide a consistent connectivity experience from Kubernetes workloads to virtual machines and physical servers running in the cloud, on-premises, or at the edge. Some of these include: Container Network Interface (CNI) [65] - Provides networking for Kubernetes clusters
This level of control enables the cluster to support upgrades and patches of the control plane nodes with minimal effort. The compute nodes can be running Red Hat CoreOS, RHEL or even Windows. OpenShift introduced the concept of routes - points of traffic ingress into the Kubernetes cluster. The Kubernetes ingress concept was modeled after this ...
OpenNebula is an open source cloud computing platform for managing heterogeneous data center, public cloud and edge computing infrastructure resources. OpenNebula manages on-premises and remote virtual infrastructure to build private, public, or hybrid implementations of infrastructure as a service (IaaS) and multi-tenant Kubernetes deployments.
Kubeflow is an open-source platform for machine learning and MLOps on Kubernetes introduced by Google.The different stages in a typical machine learning lifecycle are represented with different software components in Kubeflow, including model development (Kubeflow Notebooks [4]), model training (Kubeflow Pipelines, [5] Kubeflow Training Operator [6]), model serving (KServe [a] [7]), and ...
TiDB can be deployed in a Kubernetes-enabled cloud environment by using TiDB Operator. [14] An Operator is a method of packaging, deploying, and managing a Kubernetes application. It is designed for running stateful workloads and was first introduced by CoreOS in 2016. [ 15 ]