Tracy Collins, VP Sales – Ekkosense, discusses the ways automation tools like Artificial Intelligence and Machine Learning can be used to enable organisations to act quickly in addressing challenges and deliver the results their customers and business require.
Before joining EkkoSense, I led a regional colocation provider and prior to that spent over 21 years with Vertiv – one of the largest suppliers of critical infrastructure for the data centre industry. I’ve spent my entire career in data centres and have lived through all the challenges and limitations our sector faces around managing the issues balancing critical infrastructure and IT loads. So, I’m fully aware of the complexities involved in striking the right balance between efficiency, performance, risk and costs across all your facilities.
In reality, most data centres continue to follow traditional operating models that have been in place for decades. The environment is built to support the assumed and anticipated needs associated with the IT workload. Flexibility and future capacity are typically designed in and, in many cases, built out. But, practically speaking, monitoring is focused on failures and alerts – measuring performance and understanding the relationship between the IT load and the critical infrastructure on a continuous basis rarely occurs.
The primary reason for this is the sheer volume of data involved and the fact that most of the tools required to interrogate and interpret this data still rely on human capital. The application of Machine Learning (ML) and Artificial Intelligence (AI) capabilities creates a new paradigm in how to approach data centre operations. Now, data centres leveraging Machine Learning to monitor their critical infrastructure and IT load benefit by having better real time visibility of the operation, any potential risks and how to address them – while also reducing energy consumption and lowering operating costs significantly.
We’re already seeing hyperscale data centre operators achieving unprecedented efficiency and environmental performance levels, and they remain at the forefront of leveraging AI/ML to operate their facilities more efficiently than ever. Google, for example, has been working for over a decade to make its data centres as efficient as possible and says that they are now twice as energy efficient as the standard enterprise data centre. Indeed, by using AI, the company has been able to consistently deliver energy savings of around 30% on average.
This is an impressive performance and perhaps one that can seem daunting to those enterprise operations and colocation sites that have been slower to adopt AI/ML. Ironically, it’s these sites that face greater diversity across their operations than their hyperscale counterparts – and this complexity makes it more challenging for them to find and maintain the correct balance between risk, efficiency and cost. However, by implementing an AI/ML-based solution, they can move quickly to address these challenges and deliver the results their customers and business require, while also lowering risk and costs. That’s where EkkoSense and its distinctive EkkoSoft Critical AI-powered software optimisation approach for critical data centres can help.
EkkoSense is the only organisation to directly address the fundamental challenge of allowing operations teams to gather and visualise data centre cooling, power and space data at a granular level, while incorporating Machine Learning. We do this by bringing together an exclusive mix of technology and capabilities – including an innovative SaaS platform, low-cost Internet of Things (IoT) sensors, Machine Learning, AI analytics and PhD-level thermal skills. The result is a 3D visualisation and analytics platform that’s particularly easy for operations teams to implement, use and understand. This lets you visualise airflow management improvements, manage complex capacity decisions and quickly highlight any worrying trends in cooling performance.
Already we’re seeing customers using EkkoSoft Critical securing average data centre cooling energy savings of 30% – equivalent to the cooling energy savings achieved by hyperscale operators such as Google – by applying our Machine Learning and AI-enabled approach to data centre optimisation. So how can organisations put these technologies to work across their own operations?
Ekkosense is also looking into other specific challenges that operations teams face – covering:
- The current state of data centre optimisation – why most operators are still in the dark
- Why capturing critical data at a more granular level opens up the potential for AI-enabled data centre optimisation
- How 3D visualisation and AI-powered analytics makes it so much easier for data centre teams to support instant optimisation decisions
- The five key steps underpinning AI and Machine Learning powered data centre operations – and the specific benefits that you can expect to unlock by following this approach
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