Turning data center machine learning insights into AI-powered actions with Cooling Advisor
We’ve seen how comprehensive monitoring combined with machine learning algorithms can uncover and continually track the cooling zones within a data center – presenting a core foundation for effective automated cooling within critical facilities. However, there’s often still considerable concern and reluctance when it comes to handing over control to what is essentially a machine learning powered black box.
To avoid this concern, at EkkoSense we believe it’s important to concentrate on making the expertise behind our machine learning algorithms as intuitive and accessible as possible. Clear 3D visualizations and digital twin representations play a key role here, but it’s also necessary to provide data center teams with the human auditability that’s so important to them.
That’s where Cooling Advisor – the first embedded advisory solution powered by AI and machine learning analytics – fits in. Cooling Advisor is a machine learning powered tool that helps data center teams to keep their critical facilities thermally-optimized, and ready to deliver the next best optimization outcome.
Cooling Advisor draws on machine learning insights from over 50 million data points, with actionable changes including optimum cooling unit setpoint adjustments, fan speed points and standby settings alongside recommended changes to floor grille layouts. Changes and recommendations are based on our software’s powerful AI and machine learning analytics, and all recommendations are presented each time for human auditability before data center operations team members make the suggested changes. Our EkkoSoft Critical software can then loop back to confirm that Cooling Advisor recommendations are delivering the expected results.
Cooling Advisor operation is particularly intuitive, with potential risk mitigated by the defining of clear action steps, the provision of obvious back-out mechanisms, as well as the ability to flag and unflag items so that optimization suggestions aren’t repeatedly given for changes that cannot be implemented.
Our latest EkkoSoft Critical software-based data center optimization solution already helps to unlock data center cooling energy savings of up to 30% per annum. With Cooling Advisor, we can help you to go one step further, with clear recommended actions that take advantage of EkkoSense’s embedded PhD-level optimization expertise in the form of cooling unit algorithms updated by learnings to equip data center teams with a powerful self-optimization capability.
We also recognize that data centers never stay the same. By adopting analytics and machine learning, we can ensure that Cooling Advisor delivers advice that is specific to each of your data center rooms, while using Cooling Advisor and following its recommendations will allow your operations teams to keep on unlocking savings on their cooling costs.
If you’re interested in how our machine learning powered Cooling Advisor tool could bring the power of AI to your critical facilities, get in touch with me directly, or book a demo here.
What are your views? Get in touch with me to discuss more [email protected] or watch our video here https://youtu.be/oYABvv9Cm4Y
This is the fourth in a series of articles from me. To read the previous three pieces, please see below..
- Traditional data center software toolsets simply can’t balance escalating IT workloads with the need to cut energy consumption
- How machine learning and AI can make all the difference when it comes to data center optimization
- Five ways that machine learning algorithms can help you drive software-based data center optimization
