CASE STUDY more holistic data center optimization approach that works to bring together the IT and M & E spaces .
How does the EkkoSense approach differ to traditional DCIM propositions ?
While most EkkoSense customers already have some form of traditional DCIM capability in place , EkkoSense complements these to provide essential thermal optimization capabilities . The company ’ s EkkoSoft Critical solution brings together a mix of patented technologies and capabilities – including an innovative SaaS platform , low-cost Internet of Things ( IoT ) sensors , Machine Learning , gaming-class 3D visualization and Digital Twin capabilities , AI analytics and embedded advisory support – all backed by EkkoSense ’ s PhD-level thermal and engineering skills .
New levels of granular real-time sensing support temperature and humidity monitoring , contributing directly to the effectiveness of the Machine Learning algorithms that support continuous improvements in optimization . The software also enables data center teams to visualize complex data quickly and easily via Digital Twins enabled by EkkoSoft Critical ’ s powerful 3D visualization capabilities . And the application of AI analytics provides the operational insights that help them to remove thermal and power risk , optimize cooling capacity and minimize energy waste . across key areas such as optimum set points , floor grille layouts , cooling unit operation and fan speed adjustments . Thermal analysis will also indicate optimum rack locations . And because AI enables real-time visualizations , data center teams can quickly gain immediate performance feedback on any actioned changes .
Our EkkoSoft Critical AI-powered optimization software not only shows what ’ s happening , but also why – allowing data center teams to make informed decisions on how to resolve issues . And , by introducing powerful algorithms that correlate the relationship between the critical infrastructure and IT load , they can materially reduce potential downtime events through continual optimization . The software observes changes in the environment in real-time and will often inform you that a failure is going to occur long before it materializes . With EkkoSense AI you can make the invisible , visible – effectively changing the game for data center operators .
How will the application of Machine Learning and AI help to redefine data center optimization ?
Instead of being swamped by too much performance data , operations teams can now take advantage of Machine Learning to gather data at a much more granular level – meaning they can start to access how their data center is performing in real-time . The key is to make this accessible , and using smart 3D visualizations is a great way of making it easy for data center teams to interpret performance data at a deeper level : for example , by showing changes and highlighting anomalies .
The next stage is to apply Machine Learning and AI analytics to provide insights . By augmenting measured datasets with Machine Learning algorithms , data center teams can immediately benefit from easy-tounderstand insights to help support their real-time optimization decisions . The combination of real-time granular data collection every five minutes and AI / Machine Learning analytics allows operations not just to see what is happening across their critical facilities but also find out why .
Where do concepts such as Digital Twins fit into redefined data center optimization ?
Digital Twins represent a powerful subset of the virtual space , providing operations teams with a digital replica of a key corporate asset such as a data center or other critical facility .
Taking advantage of the dramatically increasing volume of data points from Internet of Things based wireless sensors , Digital Twins have the potential to provide data center teams with a more accessible visualization of their operation ’ s real-time performance . That ’ s why at EkkoSense we were quick to adopt immersive , lightweight , gaming-engine based Digital Twin technology to make immersive real-time optimization of data center rooms a reality .
What role do you see in AI-enabled optimization in terms of helping to meet ESG requirements ?
AI and Machine Learning powered analytics can also uncover the insights required to recommend changes
At EkkoSense we recognize the increased pressure customers are under to reduce data center energy
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