Google Gives More Power to AI Within Its Data Centers, but the Biggest Opportunities Lie Ahead

C. Drake

Summary Bullets:

  • A newly autonomous AI algorithm operating Google’s data center cooling systems will be scrutinized to learn how AI can be applied to other areas of data center operations.
  • There are opportunities for Google to leverage its growing expertise in applying AI to internal operations by expanding the range of AI solutions it offers enterprise customers.

Google recently announced plans to give operational control over the cooling systems in its data centers to an artificial intelligence (AI) algorithm. Using this algorithm, Google has already achieved a considerable reduction in the energy consumed by its 15 globally distributed data centers, with both cost and environmental implications. However, this latest move is significant because it represents a major first application of AI to data center operational control systems on a large scale. The initiative also provides another example of how AI can be deployed in data centers in ways that improve their operational efficiency, including their consumption of energy resources. Initiatives like this are becoming more common and are often included under the ‘AIOps’ (artificial intelligence for IT operations) banner, a term that refers to the use of big data analytics, machine learning (ML), and other AI technologies to automate the management of IT systems and processes.

Google’s use of AI to improve the operational efficiency of its data centers is part of an ongoing process that began with its acquisition of DeepMind, a UK-based AI company, in 2014. Following that acquisition, Google began testing an algorithm that could learn how to best operate data center cooling systems, including their fans and ventilation equipment, with the objective of reducing their power consumption. The AI algorithm collects data from thousands of sensors and feeds that data into an AI system that is modeled on neurons found in the human brain. The AI system then considers a broad swath of indicators, from energy consumption levels to safety constraints, in order to identify the best course of action.

Previously, the AI system sent recommendations to data center managers who decided whether to implement them. This helped Google achieve a 40% reduction in the amount of energy consumed by its data center cooling equipment. However, Google has now relinquished control over the decision-making process to the AI algorithm itself, which is now managing the cooling systems at several of its data centers.

Innovations like these offer potential to reduce the costs associated with employing large numbers of data center personnel. However, they may also fuel concerns among those who fear the impact of AI technologies on the labor market.

Nevertheless, Google’s AI algorithm won’t completely remove the need for human participation in managing a data center’s cooling operations. Managers will still be required to supervise the algorithm’s confidence level, as well as any changes it wants to implement. Managers can intervene if they have doubts or concerns about a proposed course of action.

The success of Google’s latest AIOps initiative will be closely monitored, with a view to learning how AI can be applied to other areas of data center operations. Other potential applications of AI within the data center include using AI to enhance data center security, to optimize the use of server and storage equipment, and to augment facility management systems, which oversee everything from temperature and humidity levels to hazard prevention.

Energy consumption represents one of the single largest costs for data center operators. However, cooling accounts for just 10% of a data center’s total energy consumption, which means that there are potentially many other ways AI could be applied to enhance data center energy efficiency. These include the use of AI to optimize the performance of power-hungry computer chips.

With both data center capacity and energy consumption predicted to rise considerably over the next few years, expect to see even more novel applications of AI technology to data center operational systems. As with other emerging technology sub-segments, there are opportunities for industry first movers. There are also additional commercial opportunities for Google here, which could leverage its growing expertise in applying AI to its own internal operations by expanding the range and capabilities of the AI solutions it offers its own business customers. For example, one possibility would be to offer operational improvement apps that use remote monitoring of sensor information on usage and environmental conditions (i.e., IoT) to automate decisions on energy management and other processes.

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