Artificial Intelligence In Data Centers

Today, however, AI is mostly being used in data centers to improve existing functions and processes. Use cases are focused on delivering tangible operational savings, such as cooling efficiency and alarm suppression/rationalization, as well as predicting known risks with greater accuracy than other technologies can offer.

AI is currently being applied to perform functions and processes faster and more accurately. In other words, not much new, just better. The table is taken from an Uptime Intelligence report Very smart data centers: How artificial intelligence will power operational decisions and shows AI functions/services that are being offered or that are in development; with a few exceptions, they are likely to be familiar to data center managers, particularly those that have already deployed data center infrastructure management (DCIM) software.

So where might AI be applied beyond these examples? We think it is likely that AI will be used to anticipate failure rates, as well as to model costs, budgetary impacts, supply-chain needs and the impact of design changes and configurations. Data centers not yet built could be modeled and simulated in advance, for example, to compare the operational and/or performance profile and total cost of ownership of a Tier II design data center versus a Tier III design.

Meanwhile, we can expect more marketing hype and misinformation, fueled by a combination of AI’s dazzling complexity, which only specialists can deeply understand, and by its novelty in most data centers. For example:

Myth #1: There is a best type of AI for data centers

The best type of AI will depend on the specific task at hand. Simpler big-data approaches (i.e., not AI) can be more suitable in certain situations. For this reason, new “AI-driven” products such as data center management as a service (DMaaS) often use a mix of AI and non-AI techniques.

Myth #2: AI replaces the need for human knowledge

Domain expertise is critical to the usefulness of any big-data approach, including AI. Human data center knowledge is needed to train AI to make reasonable decisions/recommendations and, especially in the early stages of a deployment, to ensure that any AI outcome is appropriate for a particular data center.

Myth #3: Data centers need a lot of data to implement AI

While this is true for those developing AI, it is not the case for those looking to buy the technology. DMaaS and some DCIM systems use prebuilt AI models that can provide limited but potentially useful insights within days.

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