When artificial intelligence is deployed strategically and supervised artificially, it can create a series of new efficiencies for the next generation of data centers.
Whether maintaining their own internal data centers or relying entirely on remote data centers, enterprise IT professionals need to ensure that their servers are able to cope with the growing demands of emerging technologies that are expected to reshape the enterprise landscape in the next few years. Companies that fail to integrate the revolutionary potential of these technologies from cloud computing to big data to artificial intelligence (AI) into data center infrastructure may soon find themselves far behind their competitors.
In fact, research firm Gartner predicts that by 2020, more than 30% of data centers that fail to adequately prepare for AI will not be operational or economically viable. Given this grim reality, businesses and third-party suppliers have a responsibility to invest in solutions to help them make full use of these cutting-edge technologies.
Whether an enterprise invests in its own data center facilities or establishes a partnership with a forward-looking third-party supplier, it needs to start the data center infrastructure to support AI as soon as possible. By joining the development trend while there is still room for market development, enterprises can improve their daily operation of data centers by using artificial intelligence in three ways.
In the past, IT professionals had a responsibility to optimize the performance of their company’s servers to ensure that workloads were strategically distributed in their data center portfolios. Whether running on internal server infrastructure or off-site server infrastructure, this process is still crucial to maximize the effectiveness of enterprise digital operations. That is to say, IT teams with limited staff and/or resources may find it difficult to strictly monitor workload allocation around the clock.
Fortunately, AI can help. By using predictive analysis-driven management tools, IT teams can distribute most of their workload to servers. These tools can optimize storage and computing load balancing in real time, enabling IT professionals to supervise operations at higher and lower labor-intensive levels.
The advantages of forecasting and analysis tools are not just self-management. Because of the inherent self-improvement characteristics of AI technology, predictive analysis algorithm managed servers become more efficient over time. As the improved algorithms process more data and become more familiar with the workflow of the enterprise, they will begin to predict server requirements before the request is sent.
Even when dealing with the computing needs of a small and medium-sized company, data centers consume a lot of electricity. Although some of these consumptions come directly from the server’s computing and storage operations, most of them come from the cooling function of the data center. For enterprises, it is very important to keep the servers cool to ensure their normal operation, but on the scale of industrial data centers, this energy use will soon become a major financial burden. Therefore, any tool or technology that can help enterprises improve the cooling efficiency of their data centers has brought tremendous added value.
In pursuit of better energy efficiency in data centers, Google and DeepMind recently tried to use artificial intelligence to optimize their cooling activities. Alphabet’s idea as a technological pioneer is that AI-driven recommendation systems, even with minor improvements in a wide range of data center networks, can reduce energy consumption, reduce costs, and make data center facilities more environmentally friendly and sustainable.
So far, the project has been a huge success: DeepMind’s machine learning algorithm in Google’s data center applications, without affecting server performance, will be used to reduce cooling energy by 40%.
Before the rise of cloud computing and the subsequent explosion of remote computing and storage assets, data centers were relatively simple systems composed of a few qualified professionals. However, the emergence of new and more sophisticated products in cloud computing (such as SaaS, PaaS, IaaS, etc.) has transformed typical data centers into high-tech information centers suitable for various key enterprise workloads. As these products enter more and more data centers, the demand for IT professionals with the necessary skill sets to manage them has surged.
Unfortunately, the number of qualified candidates for these positions is still limited. Therefore, the data center management team is facing a serious shortage of personnel, which may one day threaten the ability of enterprises to fully maintain their digital assets. In order to meet the growing demand for data centers, enterprise stakeholders must now make choices, either to recruit Limited talents or to invest in solutions, so that data centers can flourish without extensive human supervision.
Fortunately, AI technology provides a solution to help implement a series of server functions without fully automated IT management. AI platform can automatically perform routine tasks such as system updates, security patches and file backups, while leaving more subtle and qualitative tasks to IT personnel. Without the burden of dealing with each user’s request or event alert, IT professionals can assume the supervisory role of tasks that previously required their focus, thus allowing them more time to focus on larger management challenges.
For individual businesses and third-party data center providers, this partnership-based approach provides a medium between automation and chronic understaffing. In five or ten years, this hybrid management model is likely to become the norm for the entire data center industry. Machines will not replace human workers, at least not soon, but they can help overburdened IT teams do everything they need to do to keep data centers running smoothly.
For individual businesses and third-party data center providers, this partnership-based approach provides a medium between automation and chronic understaffing. In five or ten years, this hybrid management model is likely to become the norm for the entire data center industry. Machines will not replace human workers, at least not soon, but they can help overburdened IT teams do everything they need to do to keep data centers running smoothly.
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