Vendor Landscape Plus: Blade Server Platforms
Investing in blade servers is not just about buying a server; it is about buying into a server platform.
- The origins of blades are in high performance computing clusters, and limitations on processing, memory, and I/O made them less than ideal for consolidation and virtualization. More recent developments in these areas have enabled blades to be a solid virtualization and consolidation platform with strong market growth.
- Improvements in server capacity have created the tide that raises all blade boats. Beyond features common to all x86/x64 blades, consider how the servers fit into a consolidated infrastructure that includes the blade chassis as well as networking, storage, and virtualization.
- Use Info-Tech’s blade server RFP template to document your requirements and score the responses. Use the blade vendor landscape to develop a shortlist. Modify the shortlist to your own context. HP, IBM, and Dell are the leaders overall, but there are lower cost options for more modest deployments.
- Make a careful study of compatibility and connectivity with the rest of the infrastructure and be sure to freeze configurations to avoid surprises in a rapidly developing product market. These are just two tips from the trenches of blade deployment.
Our Advice
Critical Insight
- Blades make effective building blocks for a consolidated utility infrastructure. They are increasingly used for server consolidation, particularly where space, power consumption, and management resources are at a premium.
- However, blades are more proprietary – particularly at the chassis, network connectivity, and management level.
- First, be sure that blades are really appropriate for enterprise server goals. Then decide which blade is best and which vendor’s approach to consolidation and management is optimal.
Impact and Result
- While each blade is a full server, blades require a hosting enclosure or chassis to be connected to power, cooling, and networked I/O.
- The space efficiency of blades means more processing can be concentrated in a smaller area. Shared resources across the chassis improve efficiency and lower physical complexity.
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