what is clustering. What are the different algorithms used for clustering?

Question

what is clustering. What are the different algorithms used for clustering?

Answer 1

Clustering is grouping machines together to transparantly provide enterprise services.

The client does not now the difference between approaching one server or approaching a cluster of servers.

Clusters provide two benefits: scalability and high availability



J2EE application server vendors define a cluster as a group of machines working together to transparently provide enterprise services (support for JNDI, EJB, JSP, HttpSession and component failover, and so on). They leave the definition purposely vague because each vendor implements clustering differently. At one end of the spectrum rest vendors who put a dispatcher in front of a group of independent machines, none of which has knowledge of the other machines in the cluster. In this scheme, the dispatcher receives an initial request from a user and replies with an HTTP redirect header to pin the client to a particular member server of the cluster. At the other end of the spectrum reside vendors who implement a federation of tightly integrated machines, with each machine totally aware of the other machines around it along with the objects on those machines.



In addition to machines, clusters can comprise redundant and failover-capable:



* Load balancers: Single points of entry into the cluster and traffic directors to individual Web or application servers

* Web servers

* Gateway routers: Exit points out of an internal network

* Multilayer switches: Packet and frame filters to ensure that each machine in the cluster receives only information pertinent to that machine

* Firewalls: Cluster protectors from hackers by filtering port-level access to the cluster and internal network

* SAN (Storage Area Networking) switches: Connect the application servers, Web servers, and databases to a backend storage medium; manage which physical disk to write data to; and failover

* Databases



Regardless of how they are implemented, all clusters provide two main benefits: scalability and high availability (HA).



Scalability

Scalability refers to an application's ability to support increasing numbers of users. Clusters allow you to provide extra capacity by adding extra servers, thus ensuring scalability.



High availability

HA can be summed up in one word: redundancy. A cluster uses many machines to service requests. Therefore, if any machine in a cluster fails, another machine can transparently take over.



A cluster only provides HA at the application server tier. For a Web system to exhibit true HA, it must be like Noah's ark in containing at least two of everything, including Web servers, gateway routers, switching infrastructures, and so on. (For more on HA, see the HA Checklist.)



Cluster types

J2EE clusters usually come in two flavors: shared nothing and shared disk. In a shared-nothing cluster, each application server has its own filesystems with its own copy of applications running in the cluster. Application updates and enhancements require updates in every node in the cluster. With this setup, large clusters become maintenance nightmares when code pushes and updates are released.



In contrast, a shared-disk cluster employs a single storage device that all application servers use to obtain the applications running in the cluster. Updates and enhancements occur in a single filesystem and all machines in the cluster can access the changes. Until recently, a downside to this approach was its single point of failure. However, SAN gives a single logical interface into a redundant storage medium to provide failover, failback, and scalability. (For more on SAN, see the Storage Infrastructure sidebar.)



When comparing J2EE application servers' cluster implementations, it's important to consider:



* Cluster implementation

* Cluster and component failover services

* HttpSession failover

* Single points of failure in a cluster topology

* Flexible topology layout

* Maintenance

Answer 2

Data clustering - a common technique for data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis, bioinformatics, and search engines. Clustering consists of partitioning a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often similarity or proximity for some defined distance measure. When the data is human language text, rather than numbers or symbols, different methods are employed.



Data clustering algorithms can be hierarchical or partitional. With hierarchical algorithms, successive clusters are found using previously established clusters, whereas partitional algorithms determine all clusters at once. Hierarchical algorithms can be agglomerative (bottom-up) or divisive (top-down). Agglomerative algorithms begin with each element as a separate cluster and merge them in successively larger clusters. The divisive algorithms begin with the whole set and proceed to divide it into successively smaller clusters.



There are other type of clustering as well; for more info refer to the sites-

Answer 3

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