In this blog post
The Impact of AI on your Data Center
In the present circumstances, data has become an essential component of business operations. For a variety of operations, a growing number of businesses are acquiring data and doing rigorous data analysis. Data centres, which are physical storage solutions for data collection and storage, are used by businesses.
Physical data centres, on the other hand, are no longer a viable alternative for organizations due to the increasing complexity of data. In the event of a power failure, all data in the data centre is at danger. As a result, businesses adopt Artificial Intelligence (AI) to safeguard themselves against data-related dangers.
Traditional data centers challenges
Reasons why businesses are forced to use AI due to the challenges with physical data centers are as follows:
- To monitor the stability of large data centres, businesses must recruit skilled system administrators. Hiring too many data analysts or system administrators can be extremely expensive for a company.
- Data centres are getting increasingly difficult to monitor as they become more connected to cloud-based architecture.
- The current COVID-19 pandemic-induced WFH (Work from Home) culture has made it harder to monitor the data centres deployed on the organization’s premises.
- Business data is more complex than ever before. Even the most skilled data analysts are unable to forecast IT failures that may have an impact on the data center’s functionality. A data center’s sudden failure might dramatically reduce service availability.
The scope of traditional data center
Modern-day advancements in technologies like ML and AI are rapidly shifting the services of traditional data centers. AI is influencing the design and scalability of modern data centres in particular. According to research, over 30% of traditional data centres have closed due to a lack of AI or machine learning strategy. Traditional data centres are challenging to operate due to their high operating and managerial costs. Over 70% of enterprises will quit traditional data centres in the next four years.
You can utilize an AIOps-based analytics platform for your data centre to defend your company from potential risks. AI can help you extend the life of your data centres in addition to lowering operational costs.
The following are some of the benefits of using AI-based tools for data centre management and monitoring:
Prediction of power outages with the help of AI
A power outage that causes data centres to shut down has an impact on service availability and revenue-generating potential. Large data centres are being built as a result of rising demand. It is critical to safeguard data centres against overheating and power outages. Organizations employ an extra power supply to cool down data centres. The more power used to cool data centres, the more money an enterprise will have to spend.
To prevent data centres from overheating while preserving energy efficiency, an AI-based technology can be utilised. IT automation combined with artificial intelligence (AI) can assist you in giving cooling power to only those data centres that require it. Many companies that use AIOps-based analytics tools claim to have reduced their power costs.
Role of AI in Server Optimization
In addition to storing data, data centers are also accountable for dispensing data to different servers within the IT infrastructure. A server may stop functioning and hamper the service availability due to excessive load. 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.
How Troubleshooting and failure prediction can be done with the help of AI?
Companies must monitor the performance of their data centres. Due to the rising complexity of IT infrastructures, application performance monitoring has grown more crucial. You can immediately troubleshoot difficulties if you can thoroughly monitor your data centres. AI aids data centre troubleshooting and failure prediction in the following ways:
- Several 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. Assuming, you know about an IT failure ahead of time, you could take proactive ways to cope with it.
- Several 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.
The worldwide AI market is expected to increase at a rate of more than 50%. Businesses will deploy AI-based data centres that are autonomous and more productive in the future years. Increase the availability of your services by implementing AI in your data centres!