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To prevent the loss of event data in the event of node or pod failures, container logs can be saved to a central log store with a search/browsing interface. Kubernetes provides no native storage for log data, but one can integrate many existing logging solutions into the Kubernetes cluster.
OpenShift is a family of containerization software products developed by Red Hat.Its flagship product is the OpenShift Container Platform — a hybrid cloud platform as a service built around Linux containers orchestrated and managed by Kubernetes on a foundation of Red Hat Enterprise Linux.
Rancher Labs is an open source software company based in Cupertino, California.The company helps manage Kubernetes at scale. Rancher Labs was founded in 2014 and, according to the company, its flagship product is used by more than 30,000 active teams.
Multi-master replication can be contrasted with primary-replica replication, in which a single member of the group is designated as the "master" for a given piece of data and is the only node allowed to modify that data item. Other members wishing to modify the data item must first contact the master node.
Within cluster and parallel computing, a cluster manager is usually backend graphical user interface (GUI) or command-line interface (CLI) software that runs on a set of cluster nodes that it manages (in some cases it runs on a different server or cluster of management servers). The cluster manager works together with a cluster management agent.
Consensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms.Also called cluster ensembles [1] or aggregation of clustering (or partitions), it refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better ...
Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other cluster analysis techniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outlier points.
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.