Examples
Worked examples
- Is an instance
A cluster consolidates idle login nodes overnight to a single host, exploiting energy proportionality.
- Is an instance
A research group migrates from dedicated workstations to a shared compute service with higher average utilisation.
Counter-examples
Looks similar, but isn't
- Not an instance
A server that draws 200 W idle and 220 W under load is non-proportional.
- Not an instance
Always-on legacy hardware kept running just in case is the opposite.
Editorial commentary
Energy proportionality, a term introduced by Barroso and Holzle (IEEE Computer, 2007), describes a computing system whose power draw scales in proportion to its utilisation — an idle system should draw close to zero power, and a system at 50% load should draw roughly half the power it draws at 100%. Barroso and Holzle’s original observation was that most real servers fell well short of this ideal, commonly drawing 50-70% of peak power even near-idle, because components such as memory, disks and power supplies do not scale down the way CPUs increasingly do.
Why it matters for research computing
Most computing infrastructure spends much of its life below full utilisation — a dedicated workstation or a lightly used departmental server is often idle overnight and on weekends — so non-proportional hardware wastes energy precisely during the periods it is least needed. Modern CPUs with deep sleep and low-power states have improved substantially since 2007, but storage, memory and network equipment still lag behind, meaning proportionality remains an incomplete property of most real systems rather than a solved problem.
What it motivates in practice
The principle is the underlying justification for several common research-computing decisions: consolidating workloads from several lightly used dedicated machines onto fewer, more heavily utilised shared servers, rather than running many machines mostly idle; aggressive use of low-power and sleep states for genuinely idle hardware; and right-sizing cloud instance allocations to actual, measured usage rather than provisioning for a peak that is rarely reached. It is closely related to, but distinct from, carbon-aware computing, which is about scheduling relative to grid carbon intensity rather than hardware power-draw behaviour at a given load.
Recognising non-proportional hardware
A practical test: a server drawing, say, 200W idle and 220W under full load is close to non-proportional — nearly all of its energy cost is fixed regardless of whether it is doing useful work, which is the condition the principle is meant to identify and correct. This reasoning underpins procurement decisions covered under sustainable HPC.
References
- Barroso and Holzle, “The Case for Energy-Proportional Computing,” IEEE Computer, 2007.
Also known as
Power proportionality · Energy-proportional computing
Machine-readable encodings
Use in your systems
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