4 minute read 1 Dec 2022
AI in data centers

How AI and automation make data centers greener and more sustainable

By Alexy Thomas

EY India Technology Consulting Partner

Technology enthusiast, Data-driven.

4 minute read 1 Dec 2022

Show resources

  • India smart datacenters and cloud infrastructure summit 2022

AI, IoT, and machine learning help modern data centers become green and sustainable.

In brief

  • Data centers currently account for 4% of the total greenhouse emissions worldwide.
  • Automating various processes using AI-based robots improves data center efficiency.  
  • Digital twins with AI and ML are helping reduce data centers’ carbon footprint.

In the first week of August 2022, an electrical explosion at a Google data center in the US injured three employees. An arc flash, which is an electrical explosion that generates intense heat, was the cause. The data center in Council Bluffs, Iowa, where this accident happened, is one of the world’s largest data center campuses.

Data centers consume large amounts of electricity and a safety hazard in them can cause significant harm. The International Energy Agency states that data centers account for around 1% of the global electricity demand. With digital transformation across sectors gaining momentum, the demand for data services is rising exponentially. This calls for bigger data centers. A rating agency predicts that the capacity of data centers in India will record a five-fold increase in the next five years. According to a recent Assocham-EY white paper, ‘India smart datacenters & cloud infrastructure summit 2022’, the Indian data center market is currently worth US$1.5 billion and may grow at a CAGR of 11.4%. 

Various digital initiatives of Union and state governments and the exponential growth in adoption of cloud services (public as well as hybrid) across sectors post-pandemic are also adding to the growth. The higher demand will mean more demand for power, space, and skilled workforce. At the same time, data center operators must meet sustainability requirements and reduce greenhouse emissions.

Global real estate consultant JLL’s recent study states that the energy that data centers consume doubles every four years and the sector now accounts for up to 4% of greenhouse gas emissions globally. The study adds that this market’s growth will be directly influenced by environmental, social, and governance (ESG) requirements. Therefore, becoming more sustainable and socially responsible will be a top priority in the next two years.   

Modern data centers are addressing these problems by deploying Artificial Intelligence (AI). Coupled with automation, IoT and machine learning, AI is helping many operators design and build lean and smart data centers. AI and robotics solutions not only help improve energy efficiency, reduce carbon emissions, provide predictive maintenance, and improve security, but also automate routine activities which reduces workforce requirements. AI can predict power outages, reduce maintenance costs, and achieve higher performance metrics. A Gartner report states that by 2025, half of cloud data centers will deploy advanced robots with AI and ML capabilities, resulting in 30% higher operating efficiency. 

Automating process 

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.  

Improved security

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.

Automating process 

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.  

Improved security

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.

Show resources

Summary

Data centers currently consume 1% of the global energy demand. With the specter of an energy-strapped future looming, green and sustainable data centers will be a top priority soon. Data center operators are adopting AI, IoT, and ML to build green, lean, and smart data centers. While AI-based robots help automate the functions and optimize efficiency, predictive analytics reduce energy consumption and total costs. AI tools can help companies save up to 40% of the power spent on cooling. 

About this article

By Alexy Thomas

EY India Technology Consulting Partner

Technology enthusiast, Data-driven.