Advanced Server Clustering

Advanced Server Clustering Consulting Services

Advanced Server Clustering is a scalable technology that doesn't require specialized hardware or “Clusterware” software, and is easier and cheaper to configure than competing alternatives. Microsoft Cluster Server (MSCS), first introduced in the Windows NT Server 4.0 Enterprise Edition operating system, is now called Cluster service in Windows 2000. Cluster service in Windows 2000 Advanced Server and Windows 2000 Data Center Server provides high availability by allowing a server in a cluster to take over and run a service or application that was running on another server that has failed, a process referred to as failover. These services or applications are provided by means of "virtual servers".

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The Goals of Clustering

The goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. But how to decide what constitutes a good clustering? It can be shown that there is no absolute “best” criterion which would be independent of the final aim of the clustering. Consequently, it is the user which must supply this criterion, in such a way that the result of the clustering will suit their needs.
For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection).

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Who uses clustering?

Many different types of organizations use clustering as a vital part of the work. A sampling of these include:

Marketing: finding groups of customers with similar behavior given a large database of customer data containing their properties and past buying records;
Biology: classification of plants and animals given their features;
Libraries: book ordering;
Insurance: identifying groups of motor insurance policy holders with a high average claim cost; identifying frauds;
City-planning: identifying groups of houses according to their house type, value and geographical location;
Earthquake studies: clustering observed earthquake epicenters to identify dangerous zones;
WWW: document classification; clustering weblog data to discover groups of similar access patterns.