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OpenSSI is an open-source single-system image clustering system. It allows a collection of computers to be treated as one large system, allowing applications running on any one machine access to the resources of all the machines in the cluster.
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters).
BIRCH (balanced iterative reducing and clustering using hierarchies) is an algorithm used to perform connectivity-based clustering for large data-sets. [7] It is regarded as one of the fastest clustering algorithms, but it is limited because it requires the number of clusters as an input.
High-availability cluster. Apache Mesos, from the Apache Software Foundation; Kubernetes, founded by Google Inc, from the Cloud Native Computing Foundation; Heartbeat, from Linux-HA
Kerrighed is an open source single-system image (SSI) cluster software project. The project started in October 1998 at the Paris research group The French National Institute for Research in Computer Science and Control. From 2006 to 2011, the project was mainly developed by Kerlabs. In January, 2012 the Linux clustering mission of Kerlabs was ...
Clustered file systems can provide features like location-independent addressing and redundancy which improve reliability or reduce the complexity of the other parts of the cluster. Parallel file systems are a type of clustered file system that spread data across multiple storage nodes, usually for redundancy or performance.
The history of early computer clusters is more or less directly tied to the history of early networks, as one of the primary motivations for the development of a network was to link computing resources, creating a de facto computer cluster. The first production system designed as a cluster was the Burroughs B5700 in the mid-1960s. This allowed ...
In computer science, constrained clustering is a class of semi-supervised learning algorithms. Typically, constrained clustering incorporates either a set of must-link constraints, cannot-link constraints, or both, with a data clustering algorithm. A cluster in which the members conform to all must-link and cannot-link constraints is called a ...