Google, Intel, Netapp fund Wimpy Node/Server Research

News.com has an article on low power servers/nodes which is funded by Google, Intel, and Netapp.

Researchers tout 'wimpy nodes' for Net computing

by Stephen Shankland

Mainstream servers are growing increasingly brawny with multicore processors and tremendous memory capacity, but researchers at Carnegie Mellon University and Intel Labs Pittsburgh think 98-pound weaklings of the computing world might be better suited for many of the jobs on the Internet today.

This first-generation FAWN system has an array of boards, each with its own processor, flash memory card, and network connection.

This first-generation FAWN system has an array of boards, each with its own processor, flash memory card, and network connection.

(Credit: Carnegie Mellon University)

The alternative the researchers advocate is named FAWN, short for Fast Array of Wimpy Nodes. It's described in a paper just presented at the Symposium on Operating Systems Principles.

In short, the researchers believe some work can be managed with lower expense and lower power consumption using a cluster of servers built with lower-end processors and flash memory than with a general-purpose server. And these days, with green technology in vogue and power costs no longer an afterthought, efficient computing is a big deal.

"We were looking at efficiency at sub-maximum load. We realized the same techniques could serve high loads more efficiently as well," said David Andersen, the Carnegie Mellon assistant professor of computer science who helped lead the project.

It's not just academic work. Google, Intel, and NetApp are helping to fund the project, and the researchers are talking to Facebook, too. "We want to understand their challenges," Andersen said.

What scenarios are they looking at?

The FAWN approach can be adjusted with hard drives or conventional memory to match various sizes of datasets or rates or the queries retrieving that data.

The FAWN approach can be adjusted with hard drives or conventional memory to match various sizes of datasets or rates or the queries retrieving that data.

(Credit: Carnegie Mellon et al.)

The key value of FAWN
So where exactly is FAWN useful? Andersen makes no claims that it's good for everything--but the use cases are often central to companies at the center of the ongoing Internet revolution.

Specifically, it's good for situations where companies must store a lot of smaller tidbits of information that's read from the storage system much more often than it's written. Often this data is stored in a form called "key-value pairs." These consist of an indexing key and some associated data: "The key might be 'Dave Andersen update 10,579.' The update value might be 'Back in Pittsburgh.'"

How much power can they save?  52 queries per joule for typical server vs. 346 queris per joule for FAWN.

The researchers compared how many datastore queries could be accomplished per unit of energy and found FAWN compelling: a conventional server with a quad-core Intel Q6700 processor, 2GB of memory, and an Mtron Mobi solid-state drive measured 52 queries per joule of energy compared to 346 for a FAWN cluster. And tests of a newer design show even more promise: "Our preliminary experience using Intel Atom-based systems paired with SATA-based Flash drives shows that they can provide over 1,000 queries per Joule," the paper said.