Scale or die

Hypothetical energy cost extrapolations, 5 years from now, hardware could be only 20-50% of the total energy costs.

Efficiency defined as computing speed divided by power. Can be broken down further (computing speed / power provided to chip x power provided to chip / power provided to server x power provided to server / power provided to data center).

• Data center efficiency, PUE around 1.83, worse if data center is underutilized
• Server energy efficiency, 25% dissipated by power supply

From uptime institute, 10-year energy costs, $9/W for consumption, $10-22/W for data center build out.

Rough cost breakdown: 50% on hardware, 22% on energy, 28% on  data center (assumptions, dual socket x86, 4 year depreciation, 70% load at peak).

How to be more efficient:

1. consolidate workloads
2. measure actual power usage rather than rely on nameplates
3. investigate oversubscription

Oversubscription potential rises as the number of machines grows so oversubscribe at the data center level. Also mix workloads and be ready to kill instances if you get close to the limit.

Source: Energy-proportional computing

Consider a data center as a device (5,000 machines), distribution with 2 peaks, one at 5% utilization, another around 30%.

Typical power efficiency of a typical server, a machine running at a load of 0.3 is at 60% power efficiency, while a fully loaded machine is at 100% power efficency, and sadly data center are very rarely at 100% as seen before.

The idea behind energy-proportional computing: a generally proportional relation between work and power. Idleness in a server is scarce. It should happen at the electronics because in software it’s much harder (think of kernel getting interrupts all the time).

If you breakdown power by component, you find out that the CPU is much-more proportional than the rest of the components so even powering down the cpu the total savings are still between 10% and 20% of power gains.

Still CPUs have 2 important power-usage features:

1. wide dynamic power range (ram, disks and network devices remain in a much closer power range)
2. active low-power modes, where the cpu can do things

People, which average around 120W, have a 20x dynamic power range, compared to a 2x of a PC.

In conclusion, write fast code (biggest contribution to energy efficiency), consider reduction of all energy-related costs (provisioning), and demand energy-proportionality from equipment manufacturers.

Plug: http://climatesaverscomputing.org