Most of the operational processes in traditional enterprise data centers like server upgrades, scheduling, monitoring, maintenance, patching, updating, reporting, application delivery capacity planning, etc. are repetitive and error prone. Over the next five years, these tasks may automate using AI-powered robots that deliver accurate results.
Industrial robots can efficiently expedite tasks like disposing, decommissioning, and destruction of outdated servers and other infrastructure. Robots used for remote monitoring can collect data on sound and images to detect irregularities and security risks. Hyperscale data centers are now automating their systems with software-based management tools and ML. Benefits are aplenty. Automation not only frees up human interference but also provides valuable inputs on server nodes and configurations and boosts speed. So, it improves the overall efficiency and increases the ROI.
Greener and sustainable
Digital twins (real-time virtual representations) are now becoming key to data center efficiency improvement. They allow collection of data from all sources and help data centers to operate more sustainably, not only from a cost perspective but also from an environmental standpoint. From facility design to space utilization, digital twin technology reduces carbon footprint. As a data center becomes bigger and handles more data, the operations become more complex. Digital twins with AI and ML platforms analyze silos of data generated and track all the components within the facility to make real-time adjustments. This can also mean predicting behaviors, which in turn helps in predictive maintenance, cutting energy, time and costs.
Perhaps the most pressing issue for a data center is the power it consumes. The more powerful the data center is, the more heat it generates thus consuming more energy for its cooling systems. Real-time control of cooling equipment with sensors and ML reduces the amount of energy spent on cooling, shrinking energy bills and carbon footprint. This also minimizes the need for human supervision. The software learns by continuously analyzing sensor data and adjusts according to the environmental change. With pragmatic usage of AI, companies can save up to 40% of the power spent on data center cooling.
Data outages in data centers are common, but costly. Traditional data centers monitor and report data outages manually. AI can monitor server performance, network congestions, and disk utilization and predict data outages in data centers and thus minimize downtimes.
Data centers are prone to different kinds of security risks — physical as well as digital — which are major concerns for service providers. AI/ML-powered smart cameras, intrusion detection systems, and robots ensure the physical protection of a data center. AI is also effective in preventing cyber security risks as AI systems learn normal network behavior and spot deviation, if any. Additionally, AI can also detect malware and identify security loopholes in data center systems while thoroughly analyzing incoming and outgoing data for security threats.
As firms gear up for digital transformation, data center operations are going to become more complex. Powering them with AI and automation will not only make them sustainable but also help companies be competitive.