Cloud Computing to Reduce Greenhouse Emissions

Cloud Computing could be used to reduce energy use and allow for more use of renewable energy, by shifting processing off desktop PCs and onto shared data centres.

At a new year's party I was asked how to reduce greenhouse emissions at home. This was by an engineer who works part time from their home office. One thing I suggested was a lower power computer. However, they explained that they need to perform complex engineering calculations which take several days on a desktop computer. A slow low power computer would result in the calculations taking weeks.

Instead I suggested using cloud computing, with the computations run not on the home computer but on one rented for the purpose, as required. An example of this is the Amazon Elastic Compute Cloud (Amazon EC2).

Amazon.com offer configurations (call "instances") of the services they provide optimised for data base ("High-Memory") or computations ("High-CPU"). Amazon offer a choice of Linux or Microsoft Windows operating systems, with Windows costing about 20% more. The number of the processors can also be selected. However, the engineering application is limited to running on Microsoft Windows and has not been optimised for multiprocessor machines.

The user can specify the number of "virtual cores" provided and the number of "EC2 Compute Units" for each. The compute units are measured relative to a 2007s era 1.0-1.2 GHz Intel Opteron processor. Offered are 1, 2, and 3.25 EC2 Compute Units. These appear to relate to the speed of the actual processors Amazon.com is using, rather than an arbitrary allocation by a virtual operating system.

One anomaly is that the High-CPU Instances have smaller EC2 Compute Units than the High-Memory Instances. The High-CPU Instances are much lower price than the High-Memory Instances.

Assuming that a computation takes two days on Amazon's standard instance (US$0.12 per hour), this would cost US$5.76. One such calculation per week, would cost about US$300 per year, a cost comparable to a desktop computer. Amazon.com also offers Spot Instances, where unused capacity is auctioned. This would suit engineering calculations which are not time critical.

Working out if using Amazon.com's service would actually reduce energy use would be a complex process. This would depend firstly on how the desktop alternative was used. If a computer was dedicated to computations and turned off when not needed, then the power use would be low (not including the embedded energy in making the computer). More likely the computer would be used for normal office applications. In hat instance the processor may lower its energy use for the less demanding application.

The energy management of Amazon.com's system is not well known publicly. Perhaps Amazon.com need to offer greenhouse gas emissions as one of the parameters for their system. The use could then select a processing site which might use renewable energy, for example, to power the processors.

Assuming that Amazon.com's processors are fully occupied, then they should use less energy and cause less greenhouse gas emissions than a desktop computer which is idle much of the time. Also assuming that Amazon.com's computers are in a well designed data centre building then the air conditioning cost of cooling the system should be lower than for an office building (if the desktop computer is at home then hopefully it is naturally cooled with no air-conditioning).

Perhaps this is something I need to set as an exercise for my Green Information Technology students.