I was recently quoted in an article by Randall Stross of the New York Times, as part of my role in the Green Grid, regarding how the conceptual “Data Furnace” might improve the energy efficiency of my vacation home in Central Oregon. In winter, my electric bills are quite high; I need to leave some electric heat running all the time to keep the pipes from freezing. When I arrive for a weekend of skiing, I turn up the electric heat until the pellet stove warms up. It costs me a small fortume.


How would a “data furnace” improve the efficiency of my home? Well, it wouldn’t in the sense that physics thinks about efficiency. But from an economic perspective, it could. Computers fundamentally turn electrical energy into heat. The difference is that computers provide a computational resource while doing so, which might be solving protein structures or even be billed on a compute trading scheme as a cloud resource. That’s energy that doesn’t need to be spent elsewhere, all while providing exactly the same heat to my home.


Now, although with wide variance, it’s generally estimated that about 2% of the world’s energy is spent on computing. I spend essentially all of my professional life making that energy use more efficient.


This morning I asked an interesting “out of the box” question: “ what if the other 98% computed?” Of course it’s impractical to think of all that energy computing but the scale of 50:1 gives you some pause. What if?


What about water heating? According to the US Department of Energy I can expect to spend about $300 per year on electrical energy for water heating (about 5000 kWh). This is more than enough energy to run two highly efficient servers at full load continuously for an entire year.


Clothes dryers consume up to 12% of household electricity. What about the heater in your dishwasher? Your waterbed? Your aquarium? Your coffee maker?


It’s not too out of the blue to imagine that all of these resources could, in some not too distant future, provide useful computational work. While a detailed business model would present some unique challenges, it is certainly an intriguing idea to think that not only should all energy that computes be as efficient (i.e. heat as little) as possible, but indeed, that all energy heats should also compute as much as possible..


How would this solve my particular problem? Well, imagine if I could offset the cost of the electricity I use with a higher value-add business service. This can be seen in the picture below. When I need to generate heat, an intermeidiate service could auction that resource to a bidder. In the right circumstances, it could be a win-win. Someone gets a low cost compute resource. I get help with my electricty bill.


Compute-regulated energy delivery2.jpg


Click on the image for a larger view


What an interesting challenge! Think of the benefit to society that opportunity could deliver! How much faster could we decode everyone's genome? How much faster could we advance our understanding of fundamental matter and black holes? How much faster and more efficienctly could we render movies? What about digesting ever-larger data-sets?


So, what are the biggest challenges with this, and how would you solve them? Software architecture? Security? Reliability? Market models? Your comments are welcome.

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