A couple of years ago the US Environmental Protection Agency
reported that the energy consumption associated with data centers had
doubled between 2000 and 2006, reaching some 60 billion kWh in 2006,
roughly 1.5 per cent of the entire US energy use. The EPA says
this is expected to double again by 2010.
The report triggered a flurry of interest in ways to reduce
consumption. However, Stavros Harizopoulos from HP Labs in Palo Alto
and buddies say that almost all the attention has focused on
hardware fixes. At the chip level, this means things like dynamic
voltage and frequency scaling (DVFS), clock routing optimizations,
low-power logic and asymmetric multi-cores. At the platform level
they’ve suggested things like dynamically turning off DRAM, disk speed
control and disk spin down.
Advertisement
But what of software fixes? Harizopoulos and co say far less work
has been done in this area, partly because there are limited ways in
which programmers can control the power hungry process that go on in
silico.
This story is only available to subscribers.
Don’t settle for half the story.
Get paywall-free access to technology news for the here and now.
But the team says there are still many was that database managers can
optimise their energy use and give several examples, such as
designing algorithms for energy performance. That might mean carrying
out scans on uncompressed data rather than compressed data, which
Harizopoulos and co have calculated is more energy efficient.
In fact the whole issue of data compression will need
re-examining, they say. Data compression trades CPU cycles for lower
bandwidth, which has always seemed a bargain. But if you add energy
use into the mix, the reasoning changes since CPU cycles can be more
power hungry.
It’s this kind of green thinking that Harizopoulos and co want to
promote with their paper, which has lots of other ideas.
That could make for some fairly intensive work for managers of
data centres but it could lead to substantial savings. Better get
working
Ref: arxiv.org/abs/0909.1784: Energy Efficiency: The New Holy Grail of Data Management Systems Research
