Improvements in aisle design, cooling systems and AI can make such power-hungry premises pay back greater dividends to improve the world.

Every interaction in the digital world we live in today generates data that powers a kaleidoscope of activities. However, it is data centers that form the backbone of this flow of information.

With digital transformation driving data-intensive applications like cloud computing, AI and Internet of Things (IoT), the role of data centers is ever more crucial. Yet, data centers are big consumers of power, with various estimates placing them as using 3% of the world’s electricity.

Data center operators recognize that the power required to sustain this ever-growing demand for data storage cannot be underestimated, and are working hard to drive down energy consumption to advance sustainability in their facilities.

Reducing data center carbon footprints

One way is modifying their physical design to include new innovations like aisle containment or cooling technology, which has led to industry average PUE values across both water- and air-cooled systems together to fall from 2.5 in 2007 to 1.67 in 2019.

Another way would be the use of AI in monitoring and managing data centers. The National Renewable Energy Laboratory (NREL) in the US has worked with HPE to explore how applying AI in its Energy Systems Integration Facility (ESIF) High-Performance Computing (HPC) Data Center could improve operations. Called Artificial Intelligence for IT operations (AIOps), this solution could eventually improve a data center’s sustainability without compromising system performance.

ABCs of AIOps

As its name suggests, AIOps utilizes AI and ML to simplify data center operations management and enhance efficiency.

When properly implemented, AIOps can ensure optimum performance by helping operators to identify, troubleshoot and resolve common issues within data center operations. AIOps also brings together data from diverse sources and performs real-time analyses at source, linking anomalies and observed patterns to relevant events.

The AIOps paradigm assumes that data collected from all available sources can be shared across all teams, simplifying data analytics in a hybrid cloud model with its power mainly in its ability to consume and analyze ever-increasing volume, variety and velocity of data generated by IT across various operation tools and devices.

This results in remediation of issues in real-time, which provides teams with traditional historical analytics.

Maximizing the benefits of AIOps

As more and more data gets generated, organizations will need to effectively monitor that production. Ineffective monitoring will result in missed anomalies in the data, which can in turn lead to increased downtime. This will also increase the mean time to respond (MTTR) to helpdesk events, inevitably reducing client satisfaction.

AIOps can help in this regard:

  • Reducing the number of failures, saving power to the number of notifications of anomalous data that operators must respond to—by saving the time dedicated to otherwise monotonous tasks. AIOps resolves these issues by converging all data points into clusters that represent correlated events. The IT system is often awash with data but very little is actionable. AIOps filters out this noise and only shows important notifications that the data center operation team can easily respond to, providing ample time for them to respond to the alert, hence reducing failure impact.
  • Boosting productivity: The deployment of AI and a unified data center management offers an efficient and effective alternative to the data center operations environment as it directly focuses on issues that need immediate attention and recording of the resolution method, automating the entire problem-solving process.
  • Reducing carbon footprints: Google has previously detailed how it leverages DeepMind AI for a data center facility’s cooling system, claiming a reduction of 40% off the total energy use. This represents a huge potential for reduction in carbon footprint in the data center industry.
  • Implementing AIOps solutions for simpler IT processes can reap almost immediate financial benefits by freeing up resources for revenue generation. Automating traditional processes such as proactively monitoring, predicting, and remediating incidents will not only reduce overall IT operational costs in the long run but also assure performance, making it a valuable addition to any organization.

Towards an AIOps future

The end-goal of AIOps is straightforward – to help IT teams prevent performance challenges from becoming system-wide issues. Along with the flexibility to rapidly find and fix issues, AIOps will eventually provide IT teams with predictive insights, precisely identifying potentially problematic nicks before they becomes problems. 

Moving forward, I hope to see more widespread use of the technology in areas such as security and power management. These use cases will be vital especially in a future where data centers are keeping a minimal number of staff on-site at a facility. 

Going forward, to reduce consumption and harmful emissions the data center industry as a whole needs to look ahead at innovative technologies like AIOps to achieve greater sustainability support long-term enterprise success.