Artificial intelligence has existed for a long time. Its continuous development has destroyed different industries and fields with its quality of improving performance and reducing cost. On the other hand, we are witnessing the rise of data science, which can use a large amount of data to process, analyze and make it meaningful. Not long ago, it was impossible to interpret unstructured data. Now, with the help of big data technology, organizations see great benefits from implementing huge data collection and analysis.

This means that a large data center 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 expensive and complex.

AI brings a range of new possibilities that can simplify things, so let’s discuss why we use it in the data center.

1、 Can save energy

The data center needs a lot of energy to operate normally, and a large part of the energy is used for the cooling system. If we keep in mind that they power the entire Internet, it is clear why they emit as much carbon dioxide as the aviation industry.

For example, a typical Google search uses the energy required to light a 60W bulb for 17 seconds, resulting in a carbon dioxide content of 0.2gr. If it doesn’t sound too much, imagine how many searches there are in a day. Needless to say, with the growth of data traffic, energy consumption is expected to double.

Google has solved this problem by introducing AI to reasonably and effectively optimize the energy use of its data center. With this intelligent technology, Google managed to reduce the energy consumption of its data center cooling system by 40%.

AI can learn and analyze temperature, test flow and evaluate cooling equipment. Different smart sensors can be deployed to discover energy inefficient sources and optimize them autonomously.

Finally, the fact that the cooling system will be optimized will prevent equipment wear.

2、 This will reduce downtime

Data centers sometimes lose power, resulting in downtime. The financial and user experience costs of these events can be high – 25% of global enterprise servers lose $300000 to $400000 during hourly downtime.

To prevent this, the organization employs many professionals to monitor and predict interruptions.

However, this is a complex task that requires employees to analyze and explain different problems so that they can identify the root cause of the problem and predict interruptions. On the other hand, AI can track many parameters, including server performance, network congestion or disk utilization, and predict outages.

In addition, AI driven prediction engine can also identify fault areas that may lead to system crash. It is worth mentioning the autonomy of this technology, because AI can be used not only to predict interrupts, but also to predict users who may be affected by interrupts, and propose strategies to recover from interrupts.

3、 Workload allocation will be optimized

Predictive analysis will enable workload distribution. In the past, it experts were responsible for optimizing the performance of servers in the company to ensure the correct distribution of workload.

Maximizing optimization ensures cost reduction and better allocation of resources, which are critical to an organization’s digital operations. However, it teams are often understaffed or do not have enough resources to pay close attention to the complex process of 24 / 7, which is limited.

AI uses powerful algorithms that can perform a large number of calculations immediately, optimize storage and determine load balance in real time.

4、 Unmanned automation will be realized

Automation is one of the most important parts of AI. Recent developments enable organizations to try to use the so-called “lights out” data center. In short, these data centers do not have to be monitored and supervised by personnel.

Driverless automation will make traditional data centers obsolete, which can carry out effective computing and reduce data consumption, and these data centers have been supervised by technicians. The goal is to reduce the fire risk by reducing the oxygen content, more effective cooling design, and improve the storage capacity by increasing the rack height and making the robot accessible, so as to achieve higher efficiency and autonomy.

In the future, DCMI software will be used to remotely monitor the AI driven data center, and the incidence of human errors will be minimized due to unattended automation.

5、 Will improve security

It is no secret that data centers are vulnerable to different network threats. Hackers are always looking for new ways to grab sensitive data.

The problem is that when they try to break into the organization’s network, they can gain access to millions of users’ personal and confidential information. The key to preventing cyber threats lies in anticipation and early detection.

That’s why every organization hires data security experts to prevent these events. However, analyzing cyber attacks is a difficult and time-consuming task, which is why AI and its powerful analysis ability can surprise those who perform this task. That is, AI will learn normal network behavior, which means it will be able to notice any behavior that deviates from it. This deviation is usually the result of different security threats.

AI will also enable data centers to detect malware and security vulnerabilities.

Obviously, the future of the data center depends largely on the use of AI technology. These five reasons are the most important, but this is only the tip of the iceberg, because artificial intelligence and its subset technologies (such as machine learning and neural network) will be necessary to gain competitive advantage and keep up with the latest trends.

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