By: Sachin Bhalla, Vice President-Secure Power Division, India and SAARC, Schneider Electric India
Data centers are the backbone of modern companies in the ever-evolving technological world, enabling a wide range of digital services and applications. It is impossible to stress how important it is to maintain flawless operations and optimise resource usage within these data centers. With the global data volume expected to surpass 180 zettabytes by 2025, data has become incredibly valuable, necessitating AI-driven resource optimisation. The global enterprise data management sector is expected to experience a Compound Annual Growth Rate (CAGR) of 12.1 percent from 2023 to 2030, and this growth is likely to result in a rise in the integration of artificial intelligence (AI) within data management processes.
A recent study by JLL established that data centers now account for up to four percent of global greenhouse gas emissions and that their energy consumption rises every four years. The study also asserts that the development of this industry would be directly impacted by environmental, social, and governance (ESG) norms. Consequently, improving our sustainability and social responsibility will be one of our major priorities during the next two years.
Enter Artificial Intelligence (AI) is used in modern data centers to solve these issues. Automation of normal tasks lowers the need for labour through increasing energy efficiency, lowering carbon emissions, providing predictive maintenance, enhancing security, and all of the aforementioned factors. Artificial Intelligence (AI), a game-changing force is revolutionising the management of data center infrastructure, boosting productivity, and raising uptime to previously unprecedented proportions. AI can optimise, automate, and simplify data-related operations including quality, governance, metadata management, and analytics. It might also utilise machine learning to distribute computer resources depending on current user demand, achieving performance balance and cost savings.
AI-driven DCIM: An evolution in management
This fusion between AI and Machine Learning (ML) technologies into Data Center Infrastructure Management (DCIM) creates autonomous data centers that can carry out general data engineering activities without the need for human involvement. Businesses can unlock the potential for optimised operations and resource allocation by handing routine jobs over to AI. This results in a well-functioning ecosystem where corporate objectives are ideally aligned with data center operations. The Indian IT sector currently demands more resilient, secure, and sustainable IT infrastructure due to the sector’s significant 7.4 percent GDP contribution. Here, DCIM offers the IT sector a highly reliable and secure energy solution for all IT spaces, from core to cloud to edge. Sustainable energy usage has been improved, as a result of the widespread adoption of DCIM in IT infrastructure operations.
AI-powered cooling and autonomous monitoring
Other key issues in data center operations are energy use and cooling effectiveness. These difficulties may be changed into opportunities by using AI-powered solutions. These systems optimise cooling procedures and control energy usage by absorbing real-time data and environmental inputs, such as temperature and humidity. The results include increased energy efficiency and lower operational expenses. The cohabitation of AI and environmental data orchestrates resource usage and operational excellence in a challenging way. The next game-changer in the pursuit of operational excellence is autonomous monitoring. AI eliminates data centers from the constraints of manual intervention through automation, enabling them to operate more effectively. The system continually monitors the functioning of the equipment, forecasting when replacements are required, and adjusting operations in response to current efficiency indicators. Thus, businesses can concentrate on strategic initiatives owing to this seamless orchestration, secure in the reliable AI-driven operational foundation.
Predictive analytics: proactive downtime mitigation
Another aspect of AI called predictive analytics keeps a close check on the functionality of the equipment in data centers. It detects irregularities that might signal upcoming problems through ongoing monitoring and sophisticated pattern recognition. The outcome: For businesses avert downtime risks and catch issues early. In addition, predictive analytics helps in predicting the need for equipment replacement, guaranteeing smooth operations, and avoiding expensive disruptions. A potent weapon in the data center management toolbox is the synthesis of real-time data and predicted insights.
Although the benefits of AI in data center management cannot be disputed, there are a few factors that influence its efficiency. Important considerations include the dependability of AI algorithms, the volume and caliber of data collected, and the adaptability of the present infrastructure. As AI technologies advance, the industry could anticipate more accuracy, adaptability, and integration skills.