According to Goldman Saches, AI investment is expected to reach $200 billion globally by 2025.
The vast potential of these rapidly advancing technologies has spurred a significant increase in their use cases, ranging from transformation in healthcare to enhancing customer experiences. And while much has been said about the transformative power of AI and ML in various industries, one area that remains relatively lesser known and discussed is their role in the data centre.
The data centre serves as the backbone of the digital age, housing the critical infrastructure that stores and processes vast amounts of data. In this data-driven world, having the right data is crucial, and businesses across the board are always on the lookout for better ways to make informed decisions that can improve productivity and energy efficiency. Here lies the potential of AI and ML in the data centre.
AI uses data to perform tasks that usually require human intelligence. ML on the other hand is a subset of AI and uses algorithms to learn from data to improve its performance, gradually improving its accuracy. Together, these technologies can automate tasks and make predictions to support with decision making and reducing human error, among a range of other benefits.
One of the primary challenges in data centre operations where AI and ML could lend a hand is energy consumption. Data centres consume enormous amounts of electricity to keep the servers running and the data flowing. But while data centre decarbonisation offers a key opportunity for corporate sustainability efforts, a recent Hitachi Vantara survey has found that progress so far has been slow. Despite global pressures to address carbon emissions, almost half (49%) of respondents expect the carbon footprint of their data centre will either stay the same or even increase.
It could be said that organisations missing a crucial opportunity to leverage the right technologies in their effort to meet net zero targets. Here, AI and ML solutions could be deployed in several ways. For example, to analyse vast amounts of data to identify areas of energy and operational inefficiency, while simultaneously making recommendations for better power distribution to prevent energy over-consumption and reduce total energy use.
By streamlining processes, automating routine tasks, and identifying bottlenecks, AI and ML can help address unnecessary energy consumption, as well as free up valuable human resources to allow data centre personnel to focus on more strategic and value-added tasks.
Beyond the environmental benefits, these technologies can be used to predict and troubleshoot operational problems before they escalate into critical issues. By analysing historical data and real-time metrics, AI algorithms can detect anomalies, anticipate potential failures, and provide actionable insights to data centre operators, enabling them to address potential issues proactively. By detecting these issues early, operators can avoid costly downtime, as well as any associated reputational risk.
AI and ML can also increase the robustness and resilience of data centre operations more generally. Through continuous monitoring and learning from patterns, these technologies can automatically optimise workloads, allocate resources more efficiently, and dynamically adapt to changing demands. This results in a more agile and adaptable data centre infrastructure that can handle fluctuations in traffic and workloads without manual intervention, ensuring seamless operations and better user experiences.
In order to enable AI solutions to manage and optimise datacentres, they need real-time access to data and metadata, including resource consumptions of critical services and configuration information. This can be accomplished by implementing a decentralized data and metadata fabric that provides standardised access to data and distributed query processing across disparate data sources. Moreover, AI models need to be provided with tools to access the right kind of information as needed. Those so-called agents (i.e., ML/AI models with access to tools) are fine-tuned to perform the tasks needed to optimally manage data centres.
While the potential benefits of AI and ML in the data centre are undeniable, it is essential to consider their own potential environmental impact. As the AI boom continues, data centres may witness a surge in their carbon footprint due to increased energy consumption and hardware requirements. This emphasises the need for responsible and sustainable AI implementation.
Data centre operators must use these powerful technologies judiciously, focusing on energy-efficient hardware and optimised algorithms. AI and ML can also be utilised to develop smart cooling systems, intelligently adjusting cooling based on real-time data, thereby reducing energy wastage.
To lower the carbon footprint even further (and improve security and performance at the same time), we recommend to reimplement JAVA services in Rust. Moreover, while the transition from VMs to Linux containers may still be ongoing, we expect more and more services to be implemented as WASM modules which also help improve both efficiency and security.
Overall, the rise of AI and ML has opened up a new frontier of possibilities for the data centre industry. From energy savings and enhanced troubleshooting to increased robustness to improved operational efficiency, these technologies hold the potential to revolutionise data centre operations and drive the industry towards a more sustainable future. However, it is crucial to approach AI and ML implementation with responsibility and mindfulness, taking into consideration their environmental impact and using them as tools to address sustainability challenges rather than exacerbating them. With the right approach, AI and ML can truly transform the data centre industry and pave the way for a data-driven future.
Author details: Bharti Patel, Senior Vice President, Head of Engineering at Hitachi Vantara
