It’s not a secret that AI is gaining more and more popularity not only within the IT sector but also for education and commerce.
According to the Artificial Intelligence Global Executive Study and Research Project conducted in 2019, nine out of ten companies are using AI.
Taking into account that the technology can be applied to everything from suggesting products based on previous choices and behavior anticipation to some more advanced case usage such as SQL server monitoring and load predictions, within the upcoming years, it’s expected that to cover all the business spheres.
With increasing demand, enterprises require extensive resources to develop and implement AI into their businesses using ML training and big data massives to ensure correct interpreting and acting on the analyzed results.
Thus, it poses a certain challenge to Datacenters’ operations as they need to grow their capacity and develop their server infrastructure to be able to cover the users’ need for accessing more resources.
It also contributes to the growth of Cloud technology since the ability of quick scaling is crucial.
Data Centers benefits from AI
But is it only the challenges that are coming from AI popularity growth?
Taking into account the optimization that it brings to the spheres where it’s introduced. As Erich Sanchack, EVP of Operations at Digital Realty said in Cabling Installation & Maintenance roundup,
“Implementation of AI in the data center will move us well beyond current DCIM systems and their limitations. Using AI, we are able to create an environment in which not only are all of our power and facilities decisions and processes completely optimized but that our resource planning and even advanced functions like dynamic bandwidth and server allocation are fully automated as well.”
Datacenters receive great potential for development too.
Introducing AI mechanisms it’s crucial to make sure that the Machine Learning training was conducted on the sufficient number of use cases and supply human supervision on the first stages as faulty suggestions and actions based on them can not only have a negative financial outcome but are harmful for Datacenter reputation.
But with proper implementation, the benefits overweigh:
In order to ensure smooth system performance, all the systems require constant monitoring. While there exist a lot of monitoring tools such as Nagios, SAM, Zabbix, and many others, they still require a lot of human intervention to investigate the possible load reasons only when the load occurs.
AI systems can be trained to execute the primary commands aimed at reducing the number of active requests, killing idle system processes, and predict the load increase before the server usage statistics show the minimal overuse symptoms.
In turn, it increases server stability and minimizes downtimes.
While malicious attacks are getting smarter, there should exist smart solutions to these threats.
For sure the defense systems by data centers provide the highest level of protection, but the risk of falling a victim of new vulnerabilities cannot be underestimated.
Some malware protection software vendors already started implementing AI in their firewalls and malware protection tools. With access to tons of data, such systems are learning fast how to single out potentially harmful requests and files, improving scanning algorithms to detect cyber threats faster.
In terms of network protection, it surfs through both incoming and outgoing data that helps to predict where a potential threat can occur. With the help of AI, it is possible to detect any abnormal activity on the server to signal about the potential threat and act on the received information to mitigate the attack before it scales.
Preventing hardware overheating
AI systems use smart sensors installed in the equipment to analyze the normal noise levels and acceptable device temperature to turn on additional cooling if it exceeds certain points.
It would allow acting on the smallest symptoms of overheat and prevent the outages. Leading suppliers already start developing hardware with machine learning in mind with a liquid cooling system that can be automatically set up.
Under temperature fluctuation conditionals ensuring smooth performance contributes to repair cost savings and prolonging the server lifespan.
The nature of data center operations presupposes an uninterrupted power supply. Implementing AI can significantly reduce electricity and ventilation consumption by optimizing resource usage and effective distribution.
Taking, for instance, Google’s experience after they implemented Deep Mind technology. The results are indeed impressive. They were able to reduce the cooling bills by 40% using Machine learning control recommendations.
Extrapolating the technology to other systems may even reduce the power costs even more.
Decreasing need for human resources
Routine tasks automation, smart monitoring tools, and machine control of cooling systems mentioned above as well as inviting AI technology in disaster recovery processes to reduce the workload on data center personnel meaning that they invest the saved time in other tasks.
For sure, it also entails staff shortening. But it’s not about machines dominance over humans but rather restructuring and updating vacancies responsibilities.
It’s also expected that the current datacenter workers will need to increase their competencies and acquire new skills adapting to these changes such as basic machine learning mechanisms understandings, supervising training processes, and introduce updates into learning algorithms.
For employers, it may mean temporary additional investment in staff training and helping people to adapt to the new circumstances.
Along with the challenges for datacenters, AI can also open opportunities for development and workflow optimization.
Smart forecasting and suggested actions can significantly reduce the time required for finding and eliminating downtime threats, preventing data leakages, and mitigate overload situations before they occur.
Industry representatives that realize it and include the AI integration into their roadmaps and global strategic focus will be more prosperous compared to their competitors as they will be able to spend the saved resources for modernization, network improvements and hardware stock.
Moreover, they will be able to offer more competitive prices than companies that neglect the AI potential.
So, it’s only a matter of time when data centers that do not use this chance to their advantage fall out of the market competition.
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