Here's a new approach from researchers at MIT and Carnegie Mellon that takes Green IT a step further - into the the router and into the cloud. Energy-Aware Internet Routing
An Internet-routing algorithm that tracks electricity price fluctuations could save data-hungry companies such as Google, Microsoft, and Amazon millions of dollars each year in electricity costs. A study from researchers at MIT, Carnegie Mellon University, and the networking company Akamai suggests that such Internet businesses could reduce their energy use by as much as 40 percent by rerouting data to locations where electricity prices are lowest on a particular day.
Modern datacenters gobble up huge amounts of electricity and usage is increasing at a rapid pace. Energy consumption has accelerated as applications move from desktop computers to the Internet and as information gets transferred from ordinary computers to distributed 'cloud' computing services. For the world's biggest information-technology firms, this means spending upwards of $30 million on electricity every year, by modest estimates.
Asfandyar Qureshi, a PhD student at MIT, first outlined the idea of a smart routing algorithm that would track electricity prices to reduce costs in a paper presented in October 2008.
The researchers first analyzed 39 months of electricity price data collected for 29 major US cities. Energy prices fluctuate for a variety of reasons, including seasonal changes in supply, fuel price hikes, and changes in consumer demand, and the researchers saw a surprising amount of volatility, even among geographically close locations.
The team then devised a routing scheme designed to take advantage of daily and hourly fluctuations in electricity costs across the country. The resulting algorithm weighs up the physical distance needed to route information--because it's more expensive to move data further--against the likely cost savings from reduced energy use. Data collected from nine Akamai servers, covering 24 days of activity, provided a way to test the routing scheme using real-world data. The team found that, in the best scenario--one in which energy use is proportional to computing--a company could slash its energy consumption by 40 percent.