- Published 15 Dec 2025
- Last Modified 15 Dec 2025
6 min

Optimising Data Centre Cooling for Efficiency

Optimising data centre cooling is crucial for efficiency, cutting energy use, and boosting uptime. Implement smart airflow, sensors, and real-time data for better performance.

A server rack in a data centre, with numbers and code surrounding them.

Data centres are crucial to supporting business operations. Any business that requires data storage, email applications, or the ability to make e-commerce transactions needs access to a data centre. Increasing adoption of AI and machine learning and its exponentially heavier data requirements make absolute efficiency essential in the daily running of data centres.

Powering these processes generates vast amounts of heat, making data centre cooling a vital activity now in focus.

Why Data Centre Cooling Matters

If a data centre fails, business operations such as online transactions, data access, and communications fail too.

As of 2024, cooling failures are some of the most impactful scenarios data centres experience, after power outages.

This is because the equipment in a data centre has a temperature threshold that, if exceeded, can trigger the system to throttle performance, cause damage to hardware (component failures can be as much as 50-70%), and potentially result in total system shutdown.

If the necessary measures aren’t taken to protect data centres from overheating, warranties can be voided, and the vast expense of repair and replacement can fall to the business.

A pie chart breaking down the energy usage in data centres, highlighting cooling taking up 40% energy usage and 60% used elsewhere.

There is also the matter of climate compliance. Data centres are energy guzzlers, and 40% of their energy consumption is attributed to cooling. Ensuring this process is as efficient as possible not only helps businesses meet evolving regulatory requirements but also improves and safeguards the future financial viability of the business.

Common Cooling Challenges in High-Density Environments

Technological advancements combined with the effects of climate change have created the perfect conditions for data centre disruption.

This comes in the form of vast, rapid adoption of energy-intensive AI processes, which require additional hardware and greater cooling power.

Hardware density, together with the rise in global temperatures as a result of climate change, poses an even more significant combined threat to the thermal stability of data centres.

Data centre design plays a crucial role in mitigating these risks.

Strategies to Improve Cooling Efficiency

Several factors in data centre design help to manage heat intensity and distribution and maximise the potential of cooling technology. These include:

Airflow

Optimising airflow involves several factors. It considers not only vent, cooling fan, or blower positioning (in terms of which equipment is closer to the vent and thus receives more cooling airflow) as well as two opposing issues - obstructions to airflow and empty spaces which cause airflow to bypass equipment in need of cooling.

Optimising for airflow is a proven technique for driving data centre efficiency, improving energy use in businesses that implement it by 15-20%.

Rack Spacing

When hardware is packed too tightly together, it can obstruct and/or intensify the flow of heat from exhaust fans. Ensuring enough distance between devices for airflow to permeate is key to preventing overheating, ultimately prolonging their lifespan.

A bar chart comparing the different methods for potential energy efficiency gains for data centres. Hot/Cold Containment at 20-50%, Machine Learning at 14-21% and Airflow Optimisation at 15-20%.

Hot and Cold Air Direction and Containment

Separating the hot air generated from servers and the airflow supplied to cool them is crucial to cooling efficiency. It involves positioning servers so that their exhaust fans emit hot air in one direction, while cool air feeds in from another.

These hot and cold containment corridors need to be properly sealed to prevent air from mixing between the two or escaping along the wrong pathway. This can improve cooling efficiency by 20-50%.

These static data centre cooling methods are important, and go a long way to improve their functionality, but more is needed to monitor and preserve optimal ongoing performance.

The Role of Sensors and Real-Time Monitoring

Weather patterns and surges in data requirements can be unpredictable, affecting data centre temperatures suddenly and significantly. Preparing for both requires the real-time monitoring capabilities of sensors.

Sensors give data centre managers the ability to identify hotspots in their layout - vulnerabilities which could lead to equipment damage.

Constant sensor monitoring of humidity, airflow, energy consumption, and waste also allows for adaptive cooling measures. Fan speeds can be reduced when servers are less active or ambient temperatures are lower, while advanced data modelling using machine learning can predict the occurrence of business-critical faults such as overheating.

According to a 2025 paper by Cornell University, incorporating machine learning to diagnose and optimise cooling responses can result in energy savings of 14-21%.

Balancing Energy Use with Uptime

Given how critical they are to continued business operations, there is a tendency to over-provision data centres without considering and adapting to the patterns and fluctuations in how they’re actually used. This leads to inefficient data centre energy consumption.

Ways to balance energy consumption according to usage involve responding dynamically to sensor data readings, for example:

Using sufficient, not excessive, power by selecting the right-sized infrastructure for your business activities
Not overcooling. Reduce energy consumption by figuring out an optimal running temperature. This will save on energy without increasing the risk of damage
Organising and consolidating workflows to run fewer servers and run them more efficiently. For example, you could run energy-intensive operations, such as a website crawl, overnight while fewer people are using the system. Running these kinds of business activities outside of peak operating hours can have side benefits too, such as (in this instance) reducing the impact on site performance during peak times, as well as lowering the chances of discovery

Steps to Start Optimising Your Cooling System

There are several immediate to mid-term activities that can drive efficiencies in data centre cooling systems.

Firstly, there are obvious energy-sapping design imperfections such as:

Suboptimal vent positioning in relation to equipment
Gaps in air containment corridors
Incorrect positioning of servers which mix hot and cold airflow

These are fairly straightforward, surface-level fixes that can deliver immediate efficiencies.

Similarly, identifying and scheduling data-heavy activities during operational downtimes is a task that relies more on the organisation of business processes than technical adaptation, yet can reduce the strain on servers and contribute to more efficient energy consumption.

Sensor implementation is also a crucial move and will supply critical performance data that highlights existing vulnerabilities within the landscape and functioning of your data centre.

Importantly, it also provides data-backed evidence for securing greater investment in upgraded systems, and will definitely show where you may be overpowering or overcooling your data centre, leading to cost savings.

A table with the metrics to detect inefficiency. Power Usage Effectiveness - High ratio = Oversupply of Power, CPU Utilisation - <20% = Over-provisioning, Cold Aisle Temperature <18c = Overcooling.

Important metrics for determining data centre efficiency are:

PUE (Power Usage Effectiveness) - this metric is delivered as a ratio of total data centre power to equipment power. A high ratio indicates an oversupply of power, including cooling, for your needs
CPU Utilisation - this will tell you how much computing power you’re using relative to the processing power available. A low CPU Utilisation percentage, usually 20% or lower, suggests you’re powering greater computing resources than you actually need
Hot corridor versus cold corridor temperatures - temperatures lower the 18°C in the cold aisle are usually a sign of overcooling

Continued monitoring using real-time data from sensors is essential to detecting and predicting suboptimal operations as well as potential internal and external thermal hazards that could damage equipment or halt business operations entirely.

While the investment in physical design, sensors, more intelligent analysis and system compatibility may be costly at first, a well-optimised system will provide greater long-term efficiency and security.

A data centre designed in this way will experience fewer failures, save on energy costs and offer data-backed reasons and optimal solutions for centre modification and expansion.

If you’re redesigning the layout of your data centre for improved performance, take a look at our selection of rack cooling systems and other thermal management devices.