Certified Datacenter Support Provider for Clustering Exchange

Certified Datacenter Support Provider for Clustering Exchange Consulting Services

Datacenter is the first Microsoft product that is available solely through OEMs. Vendors such as Compaq, Hewlett-Packard (HP), and Dell offer specific Datacenter product configurations that have been tested on a higher level of compatibility and reliability than the traditional Windows testing process. Each OEM's product must pass rigorous 14-day load and compatibility testing that pushes each hardware configuration to its limit.

Datacenter also offers improved support services. Microsoft requires each OEM to host a support team in which Microsoft and the OEM's engineers work together to provide support. This setup delivers an OS and hardware bundle that's closely tied to joint support programs between Microsoft and the OEM.

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Exchange and the Datacenter 

One of Microsoft's key goals for Exchange and Datacenter is to promote server consolidation. Although Exchange doesn't directly support many Datacenter features, its support for 4-node clustering is very important to Microsoft's and OEMs' shared vision for Exchange server consolidation.

As organizations work to achieve the reduced TCO, streamlined management, improved security, and improved real estate requirements of server consolidation, Exchange on Datacenter presents an option. My company is planning server consolidations in large sites in which 30 or more servers support 10,000 to 15,000 users. The plan calls for these servers to be consolidated into one or two 4-node Exchange Enterprise Server on Datacenter clusters in each site. This setup is in line with Microsoft's server consolidation goals for Exchange. The time for long-awaited server consolidation projects might be now. Spend some time evaluating whether your Exchange deployment can benefit from Exchange on Datacenter.

  

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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.

 

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