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Modular Computing
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IT's Challenge

In the past three tears, the world has changed for information technology groups. In the late 1990s, the predominant problem was deploying equipment and software quickly enough to keep up with demand for computing. While the tech sector boomed on Wall Street, money was no object. IT budgets swelled and the numbers of computers in data centers grew exponentially.

Now, in the early 2000s, the picture is very different. IT budgets are flat down, yet business demand for IT services continues to escalate. This combination of more demand and constrained budgets has compelled IT groups to consider new approaches to IT infrastructure, approaches that offer more flexibility and lower cost of ownership.
The common theme is cost cutting. In today's world, profits come less easily than in 1990s. Competitors are more experienced, and competition is more intense. Corporations that trim costs while providing great service will prevail over those that can't.

IT plays a major role in this competitive situation. As competition becomes more intense, so does the pressure on IT to cut costs and boost contribution. Now more than ever, large corporations are using their computing assets as tools to pull ahead of the competition.

Winning through Modularity

As Janet Matsuda, SGI's director of Graphics Product Marketing, says: "Modularity offers both savings and scalability so that customers don't waste their money on what they don't want and can spend it on what they do want."
Debra Goldfarb, group vice president at analyst firm IDC, agrees: "Modular computing empowers end users to build the kind of environment that they need not only today but over time.

Doing More With Less

To keep up with computing demand while operating within restricted budgets, IT must find ways to optimally use computing resources and reduce people costs. There are many areas of improvement.

Cost of Over-Provisioning

As data centers have moved toward servers and away mainframes, IT has found that some mainframe capabilities weren't available on servers. A glaring example is that smaller servers were unable to rapidly obtain more processing power to accommodate peaks in computing demand.As applications became more transactional, for example with customers entering information via the Web, these peaks in computing demand became more visible.

During peak demand, customers saw their transactions slow down. In situations where these transactions affect the bottom line, as when customers enter purchases, prompt processing becomes vital to the business.As the number of customers using Web services has increased, the peaks in computing demand became more intense and more frequent. Consequently, customers more frequently saw declines in performance.

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