Data has become an indispensable part of business operations in the current scenario. More and more businesses are collecting data and performing rigorous data analysis for numerous operations. For the collection and storage of data, businesses deploy data centers that are physical storage solutions.

However, due to the ever-increasing complexity of data, only physical data centers are not a viable solution for businesses. If a power outage occurs, all the data in the data center is at risk. To protect themselves from any data-associated risks, businesses are using Artificial Intelligence (AI).

Challenges with traditional data centers

The challenges with physical data centers that force businesses to use AI are as follows:

• Businesses have to hire skilled system administers to monitor the stability of large data centers. Hiring too many data analysts or system administrators can pose a huge cost to an organization.
• With the increasing connection of data centers to cloud-based architecture, it is becoming more difficult to monitor them.
• The recent WFH (Work from Home) culture forced due to the COVID-19 pandemic has made it difficult to monitor the data centers deployed at the organization’s premises.
• The complexity of business data is more than ever. Even skilled data analysts cannot predict the IT failures that could impact the performance of the data center. Sudden failure of a data center can significantly downgrade service availability.

What is the future of traditional data centers?

Modern-day technologies like ML and AI are quickly displacing the services of traditional data centers. AI, in particular, is influencing the design and scalability of modern-day data centers. Studies have shown that around 30% of traditional data centers have closed as they did not implement AI or ML strategy. Due to high operational and management costs, it is difficult to maintain traditional data centers. In the next four years, more than 70% of businesses will give up on their traditional data centers.

To protect your business from future risks, you can use an AIOps based analytics platform for your data center. Besides cutting operational costs, you can increase the life of your data centers by using AI.

The benefits of using AI-based tools for the management and monitoring of data centers are as follows:

Predict power outages with AI

A power outage that leads to the shutting down of data centers affects the service availabilityand hampers the revenue-generating ability. Firms are deploying large data centers due to increasing demands. It is very important to protect the data centers from overheating and power outages. To cool down the data centers, organizations use an extra power supply. The more power is needed to cool down the data centers, the more an organization has to pay.

An AI-based tool can be used to prevent data centers from overheating while maintaining energy efficiency. IT automation with AI can help you in supplying cooling power to only those data centers that need it. Many businesses that are using AIOps based analytics platforms have claimed that they have slashed power costs.

Server optimization with AI

Besides storing data, data centers are also responsible for distributing data to different servers within the IT infrastructure. Due to excessive load, a server may stop functioning and hamper the service availability. Since server optimization is becoming difficult, businesses are using AI for the following benefits:

• AI-based tools use predictive analytics models to distribute even workloads to different servers in the IT infrastructure. Since the load on all servers will be managed effectively with AI, you can boost your service reliability and availability.
• AI-based load balancers remember the past distribution of loads across different servers.
• Network congestions within the IT infrastructure that affect the service availability can be resolved with the help of AI.

Troubleshooting and failure prediction with AI

Businesses need to track the performance of their data centers. Application performance monitoring has become more important due to the increasing complexity of IT infrastructures. If you can monitor your data centers rigorously, you can troubleshoot incidents quickly. AI helps in troubleshooting and failure prediction of data centers in the following ways:

• Many businesses have large data centers that need to be managed. IT teams struggle to find the root cause of an incidence within the large data centers. An AI automated root cause analysis solution can help IT teams in pinpointing the exact location and cause of an IT incidence. AI-based analytics platforms study the relationships and patterns between data sets to find the underlying cause of an IT incidence.
• Self-managing data centers use AI to predict the exhaustive capacity. If you know about an IT failure in advance, you could take proactive steps to cope with it.
• Many organizations use a cloud-based IT infrastructure where data is added and wiped off the next minute. AI-based tools can boost data availability and accessibility even for the most complex IT infrastructure.
• AI tools help in reducing the alert noise coming from data centers. For example, an AIOps based analytics platform can remove redundant alert noises that are generated for the same failure in data centers. AI will also provide actionable insights to deal with data center failures and power outages.

In a nutshell

The global market of AI is growing with an impressive CAGR of more than 50%. In the coming years, businesses will use AI-based data centers that are autonomous and more productive. Boost your service availability by using AI for your data centers!