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.

Critical Thinking is a weekly column on innovation in data center infrastructure design and management. The end game for data center infrastructure management (DCIM) software is that it eventually enables self-managing, or fully autonomic, data centers. The hope is that AI-driven management software (likely cloud-based) will monitor and control IT and facilities infrastructure, as well as applications, seamlessly and holistically – potentially across multiple sites. Cooling, power, compute, workloads, storage, and networking will flex dynamically to achieve maximum efficiency, productivity, and availability. Facilities equipment and IT will also be self-healing to some degree by applying cloud-based analytics to sensor data harvested from thousands of sites to guide and enact targeted predictive and preventive maintenance programs. Spare parts will be ordered, tested, and installed (perhaps by dexterous robots) to exactly align with when they are required to avoid failures but also to avoid unnecessary maintenance and testing.