Data Centre Energy Optimisation at Scale: Why Manual Tuning Can't Keep Up
Modern data centres are drowning in energy data but starving for decisions. When telemetry updates every few seconds and conditions shift minute by minute, dashboards and recommendations can't keep up across a multi-site estate. For sustained cost and carbon reductions without gambling on reliability, you need to shift from visibility to governed, closed-loop action.
The challenge facing data centres isn't gathering energy data anymore. It's making decisions at the speed the data demands.
Telemetry arrives every few seconds: server power draw, utilisation, temperatures, tariff shifts, grid carbon intensity, maintenance states. When you're sampling that frequently, you're dealing with control, not reporting. If humans are still the control loop, the data's arriving faster than we can safely act on it.
When telemetry updates every 10 seconds, humans become the bottleneck
This is why so many energy programmes hit a wall at visibility. Dashboards, alerts, recommendations are useful, but they're open-loop by design. They surface insight, then wait for a person to execute. Insights don't drive change. Action does. At estate scale, that execution doesn't happen at the pace the environment changes.
Workloads are bursty. A host can jump from 40% to 60% utilisation as multiple services shift demand. Meanwhile, thermal headroom, redundancy states, maintenance windows, pricing signals and carbon intensity all change around it. By the time someone reacts, the state's already moved on.
Add in the operational reality of understaffed teams, shift handovers, and sensible caution about changes that might trigger an incident, and manual tuning becomes lots of small adjustments, lots of checking, but not much sustained improvement.
This breaks down fastest in multi-site estates and colocation environments. Each site has its own tooling, procedures, and tolerance for change. Outcomes vary, and the estate never converges on best practice.
Open-loop vs closed-loop: advice vs outcomes
The real question isn't whether you can see what's happening. It's whether you can act on it safely, consistently and at scale.
Autonomous optimisation doesn't mean hands-off. It means governed automation where systems sense, decide and act within explicit policies that protect reliability. In control engineering terms, open-loop systems inform; closed-loop systems correct continuously based on feedback. In data centres, where disturbances are the norm, open-loop advice can't compensate fast enough. Closed-loop control can, provided it's properly bounded.[3]
Evidence that action beats recommendation
In a lab evaluation by World Wide Technology, closed-loop optimisation reduced average server power draw by 19 to 23% under steady loads and 27 to 29% under varying loads, with immediate effect once activated.[1] WWT validated power readings against PDU data, not just onboard telemetry, separating measurement from vendor claims.
An Intel solution brief showed similar patterns: up to 25% lower power under representative workload and roughly 53% lower power at idle with closed-loop control enabled.[2]
These are controlled tests, not fleet-wide production benchmarks. But they reinforce a simple point: when optimisation executes automatically within guardrails, savings show up quickly and repeatably, especially when conditions keep changing.
Energy savings land across the organisation. Finance sees cost reduction. Sustainability sees carbon reduction. Operations and SRE teams see power headroom, meaning capacity freed within the existing envelope, which can defer upgrades and reduce risk.
If your current approach ends at visibility, you've optimised reporting, not outcomes. The shift to make now is treating energy optimisation as a governed, closed-loop control problem. Ask yourself one practical question, where are humans still expected to make sub-minute optimisation decisions in an environment that changes faster than they can safely respond? That's where autonomy stops being optional.
References
[1] (2023) Using AI to Reduce Energy Consumption, Cost and Carbon Emissions in Data Centres, World Wide Technology.
[2] (2022) Power Management: Leveraging AI for Smarter Data Centre Power Efficiency (Solution Brief), Intel (Network Builders).
[3] (2017) Know when to use open- or closed-loop control, Control Engineering.
