How Machine Learning Can and Will Impact Data Centers
Machine learning is related to artificial intelligence but it’s not quite the same thing. Artificial intelligence involves taking vast amounts of inputs and producing dynamic results. In reality, although some artificial intelligence uses machine learning, not all of it does or needs to. As long as AI produces a facsimile of intelligence, it gets that classification. Having access to gigantic sets of data results in millions of potential outcomes, making something like fairly accurate speech possible. However, this does not need to incorporate machine learning.
While an AI computer could talk to you and even respond to what you’re saying, a computer capable of machine learning could do a lot more. For instance, it may be able to watch your facial expressions as the computer speaks and then choose how to talk to you based on how you react. In that way, there is a genuine interaction between man and machine, where the machine uses non-verbal cues to adapt its responses.
Our fascination with how machines can approximate humans may stem from mere curiosity or perhaps an innate Prometheus-like desire to push our limitations until we can no longer see them. However, settling back to earth and the present, tangible reality of the world of business, machine learning data centers can help achieve things equally impressive as the feats of human-like computers.
How Machine Learning Helps Data Center Efficiency
As data is processed, huge amounts of heat are produced. The electricity traveling through circuit boards and microchips heats things up, and quickly. Energy is needed to cool down each computer. Also, if the room where a computer is housed gets too warm, all the fan cooling in the world won’t save the computers from overheating. Hence, a cooling system needs to be put in place in order to keep things at an operational temperature. Data has even been lost due to water shortages as a result of a drought. Because the water was used to cool the data center environment, when there wasn’t enough, computers overheated, and critical data was lost in the ensuing damage.
Managing the data center environment efficiently is as important as having the amount of storage you need to keep and process the data. Machine learning computers have learned how to manage the cooling systems of data centers. They are able to monitor temperatures, keeping track of their ebbs and flows, and then adjust the cooling mechanisms accordingly.
Google’s machine learning is able to observe, measure and analyze 21 different variables including outside temperature, air pressure in the back of the servers, and the amount of data being processed in order to ascertain the right way to cool down the rooms.
Machine learning also improves the way a data center actually performs the processing of information. This is where the artificial intelligence starts to really get impressive. Each data center is packed with numerous resources, and each resource is capable of performing different tasks. Some work in tandem or are essentially daisy chained together, therefore they may end up making redundant usage of resources.
Machine learning is able to take the desired end result and reverse engineer that outcome by altering how the different resources are used to attain it. It would be the equivalent of the boss of a huge factory taking one quick look at all of his workers and instantly having 200 or them work the night shift because he knows their sleep patterns will make them more efficient at that time, and then having another 150 of his workers take a break at 9:15 instead of 10:30 because he understands how this will benefit their metabolism, and so on. As the artificial intelligence continues to learn, it can even make adjustments to previously devised approaches. In effect, it is self-reflective—introspective, if you will.
Machine learning can even help design the floor plan of a data center. The physical locations of certain servers and support elements can result in greater efficiencies and even improved safety. A machine learning network can make suggestions as to how to best design the space. This enables a data center to take advantage of having a truly modular setup, finding ways of improving processes that would have otherwise gone completely unnoticed.
How Machine Learning Analyzes Risk in a Data Center
The management of the physical environment of a data center using artificial intelligence presents other opportunities as well. When a data center is as dependent on cooling elements and the mechanical operation of the intricate web of components that drive the processing, a lot can go wrong. While some events may be unpredictable, most can be either predicted to within a small timeframe or at least one that’s accurate enough to help inform stakeholders of the danger ahead of time. Machine learning is already at work in this vital task.
As a machine fails due to mechanical wear, the deterioration process can usually be observed using the collection of data. If a bearing assembly is failing, for example, there will be an increase in vibration. Or if a belt is getting worn, the velocity of the element it is spinning will start to fade. There is a large number of things that can be quantified in an instant. Machine learning is able to do this in a way that enables it to alert stakeholders of a failure before it happens. This information can be used to tell a company to migrate its data to a different resource, so it is not vulnerable when the impending failure occurs. This can also be used to estimate the spending of a data center in the next quarter, or even the next fiscal year, on components with dwindling shelf lives.
Machine Learning and the Analysis of Customer Behavior
The Internet of Things is highly data center-reliant. All of the information has to be processed quickly enough to make the actions performed on various devices, appliances, and the like have a seamless integration with the outputs that may originate hundreds of miles away. One of the more recent types of data flowing into data centers is that which comes from emails and support calls.
Machine learning can use artificial intelligence to analyze the content of emails. It can then take that content and use it to predict what customers are going to say or do next. The same can be done with support phone calls using voice recognition software integrated with the artificial intelligence. If data centers are equipped with this kind of artificial intelligence, they will be well-suited to meet the needs of any company interested in predicting customer behavior and, of course, using that to increase profits.
In the end, the importance of profits cannot be ignored. After all, even though we love the “human” aspects of artificial intelligence, these are, ultimately, just machines. We use machines to make our lives easier and to make the generation of income more efficient. Machine learning is the next level. The incorporation of machine learning into the soul of modern business, the data center, is going to continue to alter the way everyone, including the artificially intelligent machines themselves, do business.