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Views: 73 Author: Site Editor Publish Time: 2022-03-25 Origin: Site
Artificial intelligence has been around for a long time and its constant evolution has disrupted different industries and domains with its quality of performance enhancement and cost reduction. On the other hand, we are witnessing the rise of data science, which is able to harness the vast amount of data, process it, analyze it and make sense of it. Not long ago, it was impossible to interpret unstructured data, and now with the help of big data technologies, organizations are seeing huge gains from implementing huge data collection and analysis.
This means that large data centers will be deployed to store and process all this data. However, it also requires them to hire a large number of qualified personnel to monitor and maintain the data center, which is both expensive and complex.
Artificial intelligence opens up a host of new possibilities that can simplify things, so let's discuss the reasons for leveraging it in the data center.
Data centers require a lot of energy to operate properly, and a large portion of that energy goes to cooling systems. If we keep in mind that they power the entire Internet, it is clear why they emit as much CO2 as the aviation industry.
For example, a typical Google search uses the energy required to light a light bulb of about 60W for 17 seconds, thus producing 0.2gr of CO2. If that doesn't sound like too much, imagine how many searches there are in a day. Needless to say, as data traffic grows, energy consumption is expected to double.
Google has addressed this issue by introducing AI to optimize the energy use of its data centers in a rational and efficient manner. With this smart technology, Google managed to reduce the energy consumption of its data center cooling system by 40 percent.
The AI is able to learn and analyze temperatures, test flow rates and evaluate cooling equipment. Different smart sensors can be deployed to discover sources of energy inefficiency and optimize them autonomously.
Finally, the fact that the cooling system will be optimized will prevent wear and tear on the equipment.
Data centers sometimes lose power, which leads to downtime. The cost of these events can be high in terms of both financial and user experience - 25% of global enterprise servers lose $300,000 to $400,000 per hour during downtime.
To prevent this from happening, organizations employ many professionals to monitor and predict outages.
However, this is a complex task that requires employees to analyze and interpret different issues to be able to determine the root cause of the problem and predict outages. AI, on the other hand, can track many parameters, including server performance, network congestion or disk utilization, and predict outages.
In addition to this, the AI-powered predictive engine can identify failure areas that could lead to system crashes. It is worth mentioning the autonomy of this technology, as AI can be used to predict not only outages but also users that may be affected by them and suggest strategies to recover from them.