We’ve come a long way from static web pages to Web 2.0 internet resources characterized by feature-rich website interfaces and the explosion of interactive and social media platforms.
And now, with the introduction of Web3 online infrastructures, providers are likely to need more data centres to power the apps, sites, and other online services that billions of people use daily.
Data centres are expensive to build, and they cost even more to run. It’s why tech titans are exploring using AI systems to run said data banks efficiently. And, of course, you can talk about AI and not mention the efforts of tech companies like Google (NASDAQ: GOOG), Microsoft Corporation (NASDAQ: MSFT), and Meta Platforms Inc. (NASDAQ: META).
Google is already on the frontlines of employing AI technology in its data centers. And Meta and Microsoft have lately been testing AI technology in their data centers by developing AI models with the help of vast operational data.
Use cases for AI in data centers
The tech firms above are using AI ingeniously to operate their data centers. Some of the use cases include:
Data centers can be unsafe due to exposure to harmful substances such as the chlorine used to sterilize liquid cooling systems for the servers and computers.
Infrastructure teams collect data from the mechanical and electrical devices and feed it to AI-powered monitoring and anomaly detection systems, which give timely alerts whenever an abnormal event occurs.
This setup makes for a better approach to handling abnormal events and an overall safer environment for data center staffers.
- Swift construction schedules and operations
An AI model keeps improving its output through reinforced learning as more data is fed into the model.
A great example is the use of AI models to predict the impact of construction and maintenance schedules in and around a particular data center and allocate ideal workloads for optimal power, network usage, and cooling efficiency.
Tech companies can also use AI systems to predict the likelihood of extreme weather conditions that could create an unsafe working environment or disrupt the day-to-day operations at a data center and make sensible power consumption, cooling system setups, and server adjustments.
- Cost savings
Power disruptions can cause millions in losses to data center operators, not to mention loss of business due to server and network downtime from power outages. AI monitoring systems help in identifying issues with power meters and fixing them timely.
Also, centers are already using AI to control mechanical and electrical components that significantly reduce wasted power by improving the air and water cooling capacity at data centers.
Will AI-run data centers be more efficient?
Definitely. Meta runs 20 centers, and Microsoft operates 200 plus centers, so you can already see the cost-saving implications of adopting AI models and systems. Plus, AI is a developing technology that keeps improving daily.
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