Posted on February 7, 2019 by Gento
It's no secret that machine learning has infiltrated everything from the technology sector to the consumer market. From smartphone assistants to Google's Artificial Intelligence (AI) advancements, machine learning continues to help tackle the most challenging problems in the world. When it comes to data centers, the use of machine learning has led to incredible advances in efficient and reduced energy consumption.
Google's AI Leads To A Breakthrough In Energy Consumption
Google's DeepMind AI program has been used in their data centers to reduce the amount of energy used on a daily basis. The implications of this type of machine learning are potentially industry-changing. Take for example the amount of energy that is used to cool a data center. From cooling the servers to maintaining the optimal temperatures in a rapidly warming world, data centers require an astronomical level of energy to cool. Fortunately, AI programs, like DeepMind, are designed to not only reduce the amount of energy needed to cool a data center, but to also provide actionable insights into a global issue: climate changes.
In a recent study, Google revealed that DeepMind led to a 40 percent reduction in the amount of energy used to cool their data centers. By reducing the amount of energy that is consumed by individual data centers, Google's AI has also helped to effectively:
• Lower the total level of emissions produced by Google data centers.
• Increased the data center's ability to adapt quickly to internal and external climate changes.
• Created an adaptive and efficient framework that can be used to optimize efficiencies for other data centers.
The Bottom Line: The Future Of Data Centers Is Here And It Leverages AI
Data centers are notorious for housing vital data; however, data centers also produce a large amount of their own data. As seen through DeepMind's applications, the historical data produced by an intelligent data center infrastructure is vital to analyzing everything from temperatures to pump speeds, and power consumption. As part of an extensive study, DeepMind was used to analyze the historical data, some of which could have been collected by Intelligent PDUs. This data was then used to build a data center neural network. The network was designed around the optimal Power Usage Effectiveness (PUE) level.
Next, DeepMind was once again used to predict (based off the analyzed historical data) when the future temperature and pressure of the data center would fluctuate. The latter knowledge was added to the neural network to ensure that the recommended actions could be automatically taken to accurately maintain the correct PUE level. The result was a self-automated data center management system that consistently achieved a 40 percent reduction in the amount of energy needed to cool the data center. Not only is the latter result better for the environment, but it effectively equates to a 15 percent reduction in the overhead associated with a high PUE level.
The moral of the story is simple, as seen through Google's DeepMind program, AI can be leveraged to effectively reduce energy consumption, improve the environmental impact, and optimize data centers across the globe. In this vein, the success of Google's DeepMind program is built upon the data that is gathered, which is first collected by an intelligent PDU. In short, if operators want to create the most operationally effective and energy efficient data centers, then they need to not only leverage AI, but they also need to use the right data gathering tools, such as Intelligent PDUs.
Learn more about Raritan's Intelligent PDUs here.