The saying goes: "If you're not on the edge, you're taking up too much space". And compute itself is now moving to the edge, forcing datacentre operators to wring the last drops of productivity from their infrastructure, ahead of a future supporting multi-sensor internet of things (IoT) devices over 5G for machine learning, and even artificial intelligence (AI). Jennifer Cooke, research director of cloud-to-edge datacentre trends at IDC, says datacentre operators need to start thinking about how many systems they will need to roll out, and the people they will need to support them. "Cost becomes the prohibitive factor," she says. Edge will take different forms.

Data center operators deploying tools that rely on machine learning today are benefiting from initial gains in efficiency and reliability, but they've only started to scratch the surface of the full impact machine learning will have on data center management. Machine learning, a subset of Artificial Intelligence, is expected to optimize every facet of future data center operations, including planning and design, managing IT workloads, ensuring uptime, and controlling costs. By 2022, IDC predicts that 50 percent of IT assets in data centers will be able to run autonomously because of embedded AI functionality. "This is the future of data center management, but we are still in the early stages," Rhonda Ascierto, VP of research at Uptime Institute, said. Creating smarter data centers becomes increasingly important as more companies adopt a hybrid environment that includes the cloud, colocation facilities, and in-house data centers and will increasingly include edge sites, Jennifer Cooke, research director of IDC's Cloud to Edge Datacenter Trends service, said. "Moving forward, relying on human decisions and intuition is not going to approach the level of accuracy and efficiency that's needed," Cooke said.

Not only is this blunt-force approach extremely inefficient, it doesn't guarantee that none of the IT equipment will overheat. "You encounter hot spots even in an over-cooled data center," Rajat Gosh, CEO of AdeptDC, a startup whose software use machine learning to manage data center infrastructure, told Data Center Knowledge in an interview. One of the hardest problems to solve in data center cooling is pressure distribution, he said, and machine learning can be especially effective at solving it. Earlier this year, Joe Kava, the man in charge of data centers for Alphabet's Google, revealed to us that the company had been using machine learning algorithms to automatically tune its data center cooling systems, which enabled cooling energy savings of up to 30 percent. Google has considered turning the technology into a solution it can offer to other companies managing industrial facilities, and it may that sometime in the future, but you don't need to wait.

Artificial intelligence is set to play a bigger role in data-center operations as enterprises begin to adopt machine-learning technologies that have been tried and tested by larger data-center operators and colocation providers. Today's hybrid computing environments often span on-premise data centers, cloud and collocation sites, and edge computing deployments. And enterprises are finding that a traditional approach to managing data centers isn't optimal. By using artificial intelligence, as played out through machine learning, there's enormous potential to streamline the management of complex computing facilities. AI in the data center, for now, revolves around using machine learning to monitor and automate the management of facility components such as power and power-distribution elements, cooling infrastructure, rack systems and physical security.

Back at the dawn of the internet, data centers could be small and simple. A large ecommerce service could do with a couple of 19-inch racks with all the necessary servers, storage, and networking. Today's hyper-scale data centers cover acres, with tens of thousands of hardware boxes sitting in thousands of racks. Along with the design changes, these mega-server farms have been built in new, remote locations, trading proximity to large population centers for cheap power. As they automate data center operations, public clouds like Amazon Web Services or Microsoft Azure hire fewer and fewer highly skilled data center engineers, who are usually outnumbered by security staff and relatively low-skilled workers who do manual labor, such as handling hardware deliveries.