Artificial Intelligence to Create Self-driving Data Centers

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The news revolving around Artificial Intelligence centers on autonomous vehicles, chatbots, digital twin technology, robotics, and the use of AI-based ‘smart’ systems to extract business perceptions out of large data sets. AI’s capability to elevate data-center efficiency and by expansion enhance the business is divided into four main categories:

  • Power Management: AI-based power management can aid to optimize heating and cooling systems, which will reduce the electricity costs, decrease headcount, and increase efficiency. The top vendors in the area are Siemens, Eaton Corp, Schneider Electric, and Vertiv.
  • Equipment Management: AI  systems can scan the health of servers, storage, and networking gear, examine to see that systems remain properly configured, foresee when the equipment is about to fail. Top vendors in the infrastructure management group are OpsRamp, Datadog, Virtana, ScienceLogic, and Zenos.
  • Security: AI tools can ‘learn’ what basic network traffic looks like, detect anomalies, prioritize which alerts need the attention of security practitioners, aid with post-incident analysis of problems and provide suggestions for plugging holes in enterprise security defences. Major vendors in the area are Cisco, VectraAl, Darktrace, and ExtraHop.

Putting it all together and the vision is that AI can help businesses create highly automated, secure, self-healing data centers that require little human interference and works at high levels of efficiency and resiliency. AI automation can scale to analyze data at levels beyond human capacity, gleaning vital perceptions needed for optimizing energy use, distributing workloads, and maximizing efficiency to achieve higher data- center asset utilization.

Power Management taps into server workload management.    

Data centers are calculated to use 3% of the global electricity supply and make about 2% of greenhouse gas emissions, so there is no doubt that so many businesses are taking data-center power management carefully, both to save money and to environmentally accountable. AI can also learn a facility by correlating HVAC systems data and environmental sensory readings on the data center floor.

AI-driven health monitoring, configuration management oversight.

AI systems can go beyond scheduled maintenance and help with the collection and analysis of telemetry data that can spot specific areas that require immediate attention. Health monitoring begins with examining if the equipment is configured correctly and performing to expectations.  Predictive equipment failure modeling based on vast amounts of sensory data logs can spot looming components or equipment failure and analyze whether it needs immediate maintenance to avoid any loss of capacity that would lead to a service outage.

Some of the barriers to implementing AI are that the right people need to either be hired or trained to manage the system. Enterprises should take small steps towards AI and not caught up in the hype that so often surrounds the technology.

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