Monthly Archives: May 2012

A totally green-powered data centre in London?

Looks like Infinity DC are building a biomass-powered data centre near London.

This is great news: they’ll be the first one to do green-powered data centres in the UK. A couple of others are using offsetting but we’ve not heard yet of anyone else directly powering off a green source, which is much preferable.

Any current cloud providers fancy putting a public cloud resource in there? We’d love to add it to the menu for our clients – right now, Greenqloud in Iceland are the only other ones we know of, and we’ve got plenty of people who need to be UK-based but who would prefer to use fully green resources.

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Countries’ power grids differ wildly in their footprints

I showed in my post last week that the biggest determinant of good vs bad carbon footprint for a data centre is what power source it runs off.

If you want to pick a good data centre, therefore, you want to pick one that’s on a good power grid (most data centres run off their local public grid most of the time). Some high-level examples of power grid footprints are in the chart below, supplied by our friends at Amee. As you can see, the grids differ a *lot* in their footprints. The UK, where we’re located, is okayish but not great; in line with the US average, although within US states there’s a wide spread as well. Iceland, Switzerland, and Sweden are all very good, if you can manage to get servers there.

Of course, you can just run jobs through our services and we’ll take care of the detail of how to minimise the carbon footprint. But it’s still good to understand why we’re doing what we do.

80% of bad footprint is down to power source and climate

I’ve heard some very confident but completely conflicting claims lately about what is ‘the important thing’ in the carbon footprint of compute jobs. The dissonance was starting to get to me, so I’ve done some back-of-envelope calculations of different scenarios to figure out what the order of importance of the factors actually is. I soft-pedalled the impact of power source as much as possible, since a priori I thought that was the most important one and I didn’t want to be biased.

The results were really interesting, and not only because they show that I’m right 🙂

60% of a bad footprint is down to power source, and 20% is down to climate. The remainder is factors like physical design and occupancy; ironically, those smaller ones are the factors that people tend to highlight when talking about ‘green’ data centres. This waterfall chart breaks it down:

Here’s how I calculated the numbers shown in the chart – these are all approximations, but I’m confident they’re in the right ballpark. I would be particularly interested to hear any adjustments to the model that you’d make.

Start with a good footprint baselined at 1

PUE of different physical designs: Good case 1.2 (this is very good: the superstar PUE from OpenCompute is claimed as 1.08) bad case 1.9 (this is very bad since we’re assuming no climate or occupancy impact yet)

Physical design PUE impact 1.9/1.2 = 1.58 times impact 

Occupancy: very good case 100%, bad case 50% (assuming a fairly good worst case as a public cloud operator with very low occupancy is probably going to go out of business). Impact 1.22 times, read off from chart at the British Computer Society Data Science Simulator http://dcsg.bcs.org/welcome-dcsg-simulator

Cold and dry to hot and humid climate 2.0 times impact – assume massive extra air conditioning load

Power source assume bad case UK power grid, good case Sweden power grid. Deliberately understimated this one (e.g. many US regions are much worse than UK, and Iceland is much better than Sweden) to make sure I don’t overstate the case. 8.0 times impact according to Defra international energy figures.

Overall bad footprint 1 * 1.58 * 1.22 * 2.0 * 8.0 = 30 times worse than the starting ‘good’ case.

To convert multiplicative impacts into percentage shares, take logarithms.

End result:

61% is down to power source

20% climate

13% physical PUE factors

6% occupancy

That’s some pretty dramatic stuff – and it means, thankfully, that our model of estimating footprints based on external climate and power source data without always having access to physical PUE and occupancy data is heading the right way. Good news for us – we can indeed have a big impact with carbon ratings without the big providers playing ball!

Thoughts, comments, very welcome.

An open standard for cloud footprints?

Well, this has been an exciting week.

We had All The Meetups last week (thanks Carlos and Stewart for an excellent set of Big Data Week events). This week was going to be quieter, but turned into the week of All The Meetings; everyone we spoke to had someone else even more eminent that we needed to be introduced to.

Turns out that there’s tremendous appetite in parts of the industry for getting real, actionable data on cloud usage footprints into the hands of users. This has been a very happy surprise to us – we were especially excited that the founder of Joyent committed this week to getting data out into the world on this very thoughtful panel discussion which Tom Raftery at GreenMonk participated in and pointed us to. We think that maybe (just maybe) there is scope for putting together an open standard for ratings, forming a coalition of hackers, carbon specialists, and cloud providers, and getting it out as widely as humanly possible. Watch this space – and get in touch if that coalition is something that you’d like to engage with.

And the final win of the week – we have got an office! The novelty of working from home palled extremely quickly, and so we have taken some space from the lovely folks at Club Workspace, which I can highly recommend so far.