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Computer researchers at Stanford want to throw away the hard disk and store information in data centers in random access memory, the more expensive temporary storage that makes programs run faster.     

Today’s hard disks can hold roughly 10,000 times as much information as they did in the mid-1980s, but they can only transfer large amounts of data about 50 times as fast as they could back then. This is a significant bottleneck for data stored on a server in a data center—the kind becoming increasingly common as businesses push their data into cloud computing.

For applications that need to manipulate a lot of data very quickly, like high-frequency stock trading, or translating Web pages from one language to another, the delay is a problem, says John Ousterhout, research professor of computer science at Stanford and head of a new project based on the idea, dubbed RAMCloud. “We’re seeing more and more interesting applications that have huge data sets and access that data very intensively,” he says.

Ousterhout’s proposed RAMCloud is based on dynamic random access memory (DRAM). In personal computers, after data is fetched from a disk or flash drive, it is temporarily stored in DRAM, which provides a program with very fast access. Data is stored as an electrical charge on a capacitor. In a data center, fetching bits from DRAM and sending them over the center’s internal network should be 100 to 1,000 times faster than getting it from a disk.

“You’ll be able to build new kinds of applications that just weren’t possible before,” says Ousterhout. “Can you ever think of a time in the history of technology that improving speed by a thousandfold happened and nothing changed?”

Some other computer scientists are more skeptical. “I was hoping to hear a more convincing argument,” wrote Murat Demirbas, associate professor of computer science and engineering at the State University of New York, Buffalo, in a blog post reviewing Ousterhout’s RAMCloud paper. Demirbas also writes that using many disks in parallel might be another approach to cutting down retrieval times.

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