Using a cluster of the same processors that normally show up in netbooks and similar mobile devices, researchers have created a powerful server architecture that draws less power than a lightbulb.
The architecture, dubbed a “fast array of wimpy nodes,” or FAWN, offers a way to decrease by an order of magnitude the amount of power used by the computational infrastructure of Internet giants like Google, Microsoft, Amazon, eBay, Facebook, and others. If the predictions of its inventors are borne out, it could have a significant impact on both the bottom line and the environmental impact of cloud computing.
Power now accounts for up to 50 percent of the cost of operating data centers, and in the United States, its cost per kilowatt-hour is increasing. Even relative newcomers like Facebook use up to $1 million a month in electricity, and the Environmental Protection Agency (EPA) projects that by 2011, data centers in the United States could use up to 100 billion kilowatt-hours of electricity, for a total annual cost of $7.4 billion, with an estimated emissions impact of 59 million metric tons of CO².
FAWN, which is described in an as-yet-unpublished paper by David Andersen and his team at Carnegie Mellon University, tackles this problem with a combination of relatively slow processors (the kind used in netbooks and other mobile devices) and flash memory (the kind that stores data in digital cameras and USB drives). The somewhat counterintuitive result is an architecture whose performance per watt of energy is a hundred times better than that of traditional servers, which use faster (but much more energy-hungry) processors and disk-based storage.
The exceptional performance of FAWN is limited to certain kinds of problems–random access of small bits of information–but this kind of input/output-intensive task is exactly what strains the existing infrastructure of Web companies like Facebook.
“When you go to Facebook.com, the home page has hundreds of individual data elements on it, which get translated into hundreds of internal lookups,” says Andersen. Requests for those hundreds of elements, which include friends’ updates, the number of messages in an inbox, and more, are handed off to a specialized piece of software, called memcached, that stores relevant data in RAM. Memcached prevents Facebook’s disk-based databases from being overwhelmed by a fire hose of millions of simultaneous requests for small chunks of information. Amazon, which has more or less the same problem as Facebook with its shopping cart and custom recommendations, uses a similar piece of custom-built software, called Dynamo, to perform nearly the same function.
One way that FAWN replaces software like memcached and Dynamo is by conquering what computer scientists call the memory wall, which is the huge disparity between the rate at which disk-based storage can feed data to a CPU and the rate at which a CPU, which is much faster, can chew through that data. (Andersen points out that modern CPUs use an enormous number of transistors trying to guess what data to expect, fetching data in advance or caching it in memory to make sure that the chip always has a steady supply of bits to process.)