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Blade computing is sort of the complement to the grid. Computers and storage are getting physically smaller and smaller, to the point where a properly loaded data center rack could hold hundreds of small, totally self-contained computers. These small computers, called blades, are already available from many system vendors. The thinking is that running hundreds of small computers results in more computing power for less money than running a single large system. Of course, running hundreds or thousands of computer systems of any size introduces all sorts of new problems. For blades to be truly practical, a technology that can centrally manage large quantities of servers must be brought to market. That solution will need to manage servers from different vendors, that sit in separate racks, even in different data centers or buildings. The application that comes closest to delivering this functionality today is clustering software. With clustering software, an administrator can manage several nodes at the same time, although not as many as a large blade implementation will require; today s clustering software can generally only manage small to medium-sized clusters, containing up to 32 nodes. That is inadequate in an environment that may have thousands of blades. After all, a single data center rack can hold more than 200 blades. What you can expect to see is a model where each blade runs a single application. The distribution of these applications is controlled by a piece of software that runs on a couple of blades, but not all of them. If a blade fails, its application is failed over to another blade that was sitting idle, waiting to take over. What s even more interesting is the prospect of instant blade provisioning, where the blade is kept totally blank. When circumstances dictate that a blank blade must be allocated, a copy of the appropriate OS is installed on it (Windows NT, Windows 2000, Windows XP, Linux, Solaris on Intel, and so on). With the operating system image provisioned to the blade, you ll layer middleware or applications images, and finally point the blade at a networked data repository. Once the data is in place, the blade can join the network and begin serving the application and data. In this view, the computer has become a complete commodity. The pricing model for blades will likely be such that if one fails and cannot be easily repaired, the administrator will remove the system from the rack, throw it away, and replace it with another blade held in reserve. The cost of these blades is expected to fall to under $500 over time, and probably much less than that. The cost to repair one of these systems will be greater than cost to replace it. So, the commodity system becomes a disposable commodity. The cost of a blade can be kept low by building it on commodity processor hardware (Intel processors are the most common), running a free or nearly free operating system such as Linux, and connecting the system to low-cost commodity storage.
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There are still some technological issues that work to keep blades from achieving wider acceptance. It is impractical to connect blades together in a SAN when an HBA (the adapter board that connects a computer to a SAN) costs more than the blade itself. And until solid management tools can be developed, it will remain very difficult for system administrators to trade a few large systems in for hundreds or thousands of smaller ones. Designing applications that can be run in multiple, separate operating system instances remains an issue; there are obvious choices like web and application servers, and emerging models such as finely grained clusters of database servers. Blade and grid computing are likely to drive acceptance jointly, as application development and application assembly models such as web services are mapped into fine-grain compute elements.
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