Cluster introduction

A cluster is two or more computers (called nodes or members) that work together to perform a task. There are four major types of clusters:

Storage clusters provide a consistent file system image across servers in a cluster, allowing the servers to simultaneously read and write to a single shared file system. A storage cluster simplifies storage administration by limiting the installation and patching of applications to one file system. Also, with a cluster-wide file system, a storage cluster eliminates the need for redundant copies of application data and simplifies backup and disaster recovery.

High-availability clusters provide continuous availability of services by eliminating single points of failure and by failing over services from one cluster node to another in case a node becomes inoperative. Typically, services in a high-availability cluster read and write data (via read-write mounted file systems). Therefore, a high-availability cluster must maintain data integrity as one cluster node takes over control of a service from another cluster node. Node failures in a high-availability cluster are not visible from clients outside the cluster. (High-availability clusters are sometimes referred to as failover clusters.)

Load-balancing clusters dispatch network service requests to multiple cluster nodes to balance the request load among the cluster nodes. Load balancing provides cost-effective scalability because you can match the number of nodes according to load requirements. If a node in a load-balancing cluster becomes inoperative, the load-balancing software detects the failure and redirects requests to other cluster nodes. Node failures in a load-balancing cluster are not visible from clients outside the cluster.  LVS (Linux Virtual Server).

High-performance clusters use cluster nodes to perform concurrent calculations. A high-performance cluster allows applications to work in parallel, therefore enhancing the performance of the applications. (High performance clusters are also referred to as computational clusters or grid computing.)

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