Search results
Results from the WOW.Com Content Network
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] Kubernetes provides two modes of service discovery, using environment ...
In either case, the cluster may use a high-availability approach. Note that the attributes described below are not exclusive and a "computer cluster" may also use a high-availability approach, etc. "Load-balancing" clusters are configurations in which cluster-nodes share computational workload to provide better overall performance.
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.
MySQL Cluster, also known as MySQL Ndb Cluster is a technology providing shared-nothing clustering and auto-sharding for the MySQL database management system.It is designed to provide high availability and high throughput with low latency, while allowing for near linear scalability. [3]
The appropriate clustering algorithm and parameter settings (including parameters such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or ...
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster.