Sun’s PR team sent me some blog entries on their PUE efforts.
One is Sun discussing their achieving a 1.28 PUE.
I'll show you mine...
So how efficient is your datacenter?
Last month I received some pretty cool news. The Chill-Off we have been hosting for the Silicon Valley Leadership Group (SVLG) in their datacenter demonstration project, was completed. This was a head to head test against APC In-Row, Liebert XDV, Spraycool, Rittal Liquid Racks and IBM Rear Door Heat Exchanger. Sun was the host that provided the datacenter, plant, compute equipment, and support. Lawrence Berkeley National Labs (LBNL) was the group conducting the test on behalf of the California Energy Commission (CEC) The results from this test will be published in a report in June of this year and we will be hosting the event.
But one piece of information came out of this that I could not wait to share. As part of the chill-off, LBNL did a baseline of our plant in Santa Clara. Mike Ryan on my staff then captured the usage data for the remaining portions of the datacenter. This gave us our PUE or DCiE (pick you favorite) number.
I knew that our datacenter would be efficient because of the way we had designed it, but I did not have any data to back it up yet. We had been telling our customers, and others that toured the center, that we are pretty sure we would be under the industry targeted PUE of 2 (Created by Christian Belady from the Green Grid). That was a conservative number. But when I got the data back, even I was surprised at how efficient it was.
We achieved a PUE of 1.28!
Google says they eliminated data for facilities below 5MW.
Such a strong claim demands evidence, especially in light of recent criticism of companies "gaming the numbers." On this page we will explain our measurements in detail to ensure that they are realistic and accurate. It is worth noting that we only show data for facilities with an actual IT load above 5MW, to eliminate any inaccuracies that can occur when measuring small values. This section is aimed at data center experts, but we have tried to make it accessible to a general technical audience as well.
But given Google’s claim of measurement accuracy, it would seem like they should be able to measure numbers below 1MW like Sun has.
To ensure our PUE calculations are accurate, we performed an uncertainty analysis using the root sum of the squares (RSS) method. Our uncertainty analysis shows that the overall uncertainty in the PUE calculations is less than 2% (99.7% confidence interval). Our power meters are highly accurate (ANSI C12.20 0.2 compliant) so that measurement errors have a negligible impact on overall PUE uncertainty. The contribution to the overall uncertainty for each term described above is outlined in the table below.
Here are more technical details on Sun’s PUE calculations.
Sun exposes more details than Google as they want your business.
Better yet, would you like Sun to help design your datacenter to achieve the same efficiencies? Let us drive the next generations physical and technical solutions for you. Sun's Eco Virtualization practice can do just that. Email me at firstname.lastname@example.org and I'll tell you how.
What is Google’s motivation for its PUE disclosure?