Artificial Intelligence (AI) and Machine Learning (ML) are two terms often used interchangeably or considered to be synonyms. AI refers generally to the concept that a machine or system can be “smart” in carrying out tasks and operations based on programming and the input of data about itself or its environment. ML, on the other hand, is an approach or method for making a machine or system more intelligent…to enable it to be more autonomous and self-adjusting as conditions change. ML is fundamentally the ability of a machine or system to automatically learn and improve its operation or functions without human input. ML could be thought of as being the current state of the art form of imbuing a machine with AI.
Data Center Physical Infrastructure systems have some form of AI today. UPSs, cooling units, etc. have programmed firmware and algorithms that dictate how the equipment operates and behaves as conditions change. For example, cooling control systems actuate valves, fans, and pumps in a coordinated, logical way to achieve user-defined set points as environmental conditions change over time. Power and Cooling equipment have sensors that collect a large amount of useful information about themselves and their environment. This data is used by the machine to determine how it operates and responds (e.g., switch to bypass, turn off battery charging, send warning message, etc).This data is further used by “smart” Building Management Systems, Electrical Power Monitoring Systems, and/or Data Center Infrastructure Management Systems to extract useful insights about the data center’s status and trending in terms of capacity, reliability, efficiency, and so on.
Using Machine Learning in data centers, however, is still a new and developing concept. Schneider Electric, however, believes that increasing the intelligence and automation of physical infrastructure equipment and management systems will make data centers more reliable and efficient both in terms of energy use and operations. Schneider Electric is leading the way in this and laying the foundation for this trend with our new EcoStruxure for Data Centers System Architecture. EcoStruxure is our IoT-enabled, Open, and Interoperable architecture and platform. It is our approach for taking advantage of cloud computing, IoT, and “Big Data” analytics.Combined with developments in Artificial Intelligence and eventually Machine Learning, these technology trends have the potential to revolutionize our lives in many positive ways. For those tasked with managing buildings or critical facilities, these technologies offer the promise of much greater operational insight, automation, and unlimited, easy scalability of the management system.This will improve system reliability and energy efficiency while simplifying and reducing the cost of operating and maintaining the facility. The future looks bright!
It is important to say, however, that AI is not going to solve all challenges. It will not magically make an old traditional data center into a cutting-edge site with a perfect PUE and availability record. The fundamentals and best practices of data center design and operation will still be crucial to success, of course. My team, the Data Center Science Center, recently calculated that a typical data center’s physical infrastructure energy losses have been cut by 80% over the last 10 years or so thanks to improvements in UPS efficiencies, cooling technologies (e.g., economization), and cooling practices (e.g., air containment). Data Centers are cheaper now, too, on a $/watt basis. These gains are a starting point for a good data center today. So, we expect future AI and ML developments that are applied in the data center will build on or provide incremental value to these major performance improvements that were gained over the last decade. AI won’t be a cure all.
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