Data is the lifeblood of any business. And while advances in machine learning have been revolutionizing almost every industry, some have lagged due to technical constraints. As a result, more and more companies have decided that big data is not just a buzzword, but a new fact of business life. More importantly, these disruptive technologies have the potential to generate a host of new efficiencies for next-generation data centers.
Do you remember the time when tech giant Google made headlines regarding handing control of cooling systems in several of its hyper-scale data centers to artificial intelligence (AI) algorithm developed by DeepMind? As a result, the AI program underwent testing for a couple of years, making recommendations to data center managers that might either be implemented or ignored.
Either way, the decision to hand direct control over to the algorithm represents a significant step in the development of AI. According to Gartner, more than 30 percent of data centers that fail to sufficiently prepare for AI will no longer be optionally or economically viable by 2020.
Many of you think artificial intelligence is not a modern concept but it was coined in the thirteenth century by a Franciscan philosopher who emphasized on how artificial being in his book Ars Magna. Initially, the pioneers of AI had lofty goals. They wanted to create machines that could learn like human beings.
After this, for the next few decades, research on AI continued to make progress. Although the information has been worn out, frayed and faded. And that’s the reason why it stayed out of sight and mind of the general population. Slowly and steadily, things seem to have changed in regards to the search engine, robotics, and speech recognition software started to gain popularity.
At the same time, AI was making some breakthrough in a subset of its algorithms called machine learning. With the dawn of the internet and the rise of powerful personal computers made it possible for AI to make rapid progress. In addition to this, machine learning and other AI concepts like deep learning seems to be gaining momentum like never before.
Today machine learning and deep learning are helping create applications that can learn autonomously and solve complex problems.
Data Centers and Artificial Intelligence
The rise of artificial intelligence is affecting global data centers in a couple of ways; AI applications need the global data centers to provide the necessary computational power and AI applications being developed to improve the data centers themselves. Further below, I would emphasize on certain tips regarding the interaction between data centers and AI technologies:
1. Need for Global Data Centers with GPUs
With the time passing by, demand for microprocessors and servers seems to have skyrocketed like never before. Disruptive technologies such as machine learning and deep learning seem to have enhanced the potential of voice search and image recognition featuring high-end GPUs. Due to the apparent business opportunities, companies are looking into building data centers that cater specifically to the needs of machine learning and deep learning.
2. Artificial Intelligence Helping Data Centers Become Energy Efficient
More and more developments in AI are seen helping to improve the energy efficiency of data centers significantly. According to the tech giant Google, it was able to cut down its energy usage by 40% and more. Google used 4,402,836 MWh of power in 2014. So, a power savings of 40% could result in millions of dollars saved down the line. More data centers will likely start using similar AI-based solutions to save on energy.
3. Using Artificial Intelligence for Server Optimization
Do you think AI-based solutions are limited to this industry? Well, not really because they seem to be moving into other areas too. Several data centers seem to have maintained physical servers and storage equipment. Nobody can afford Inefficiencies in server usage; it’s more like leaving money on the table.
It may quite interest you to know that AI-based predictive analysis can help data centers distribute workloads across the servers. Due to which these machines can learn from past data and run load distribution more efficiently. With AI-based monitoring, companies can better track server performance, disk utilization, and network congestions.
4. Using AI for Data Center Security-
There is no denying the fact that the landscape of cybersecurity is ever-changing. As a result, humans are often seen facing difficulties to stay up-to-date on all the information. Machine learning and deep learning applications now are seen helping data centers adapt to changing requirements faster. Generally, data centers have tried to deal with threats by restricting access and create impenetrable walls, but with the constant flux of users, the approach seems to be restricting access to ensure vast amount of security.