Albert A. Ahdoot is a Business Development Executive at Colocation America.
When deployed strategically and paired with adept human oversight, artificial intelligence can generate a host of new efficiencies for next-generation data centers.
Whether they maintain their own in-house data centers or rely exclusively on offsite data centers, IT professionals need to ensure their servers are equipped to handle the increased demands generated by a wide range of emerging technologies that promise to reshape the corporate landscape in the years to come. Companies that fail to incorporate the revolutionary potential of these technologies—from cloud computing to big data to artificial intelligence (AI)—into their data center infrastructure may soon find themselves well behind the competitive curve.
In fact, Gartner predicts that more than 30 percent of data centers that fail to sufficiently prepare for AI will no longer be operationally or economically viable by 2020. In light of this stark reality, it’s incumbent upon companies and third-party vendors alike to invest in solutions that will help them make the most of these cutting-edge technologies.
Whether a company is making capital expenditures in its own facilities or building partnerships with forward-thinking third-party vendors, its transition to an AI-enabled data center infrastructure needs to start sooner rather than later. By hopping on the bandwagon while there’s still room, companies have the chance to leverage AI to improve their day-to-day data center operations in (at least!) the following three ways.
Leveraging Predictive Analytics to Optimize Workload Distribution
In the past, it was IT professionals’ responsibility to optimize the performance of their companies’ servers, ensuring workloads were strategically distributed across their data center portfolios. This process remains essential for maximizing the effectiveness of a company’s digital operations, regardless of whether they run on in-house or offsite server infrastructure. That said, it can be difficult for IT teams constrained by limited personnel and/or resources to scrupulously monitor workload distribution around the clock.
Fortunately, AI can help. By adopting a predictive analytics-powered management tool, an IT team can delegate the vast majority of its workload distribution responsibilities to a computer. These tools are able to optimize storage and compute load balancing in real time, enabling IT professionals to sit back and oversee operations at a higher—and less labor-intensive—level.
The advantages of a predictive analytics tool extend beyond mere self-management. Because of the inherently self-improving nature of AI technologies, servers managed by predictive analytics algorithms become more efficient over time. As the algorithms process more data and become more familiar with a company’s workflows, they’ll start to anticipate server demand before requests are even made.
Machine Learning Algorithms Cool Things Down (in the Best Way)
Data centers consume an enormous amount of power, even in handling the computing needs of a single mid-sized company. While some of this consumption stems directly from servers’ compute and storage operations, much of it stems from data centers’ cooling functions. It’s absolutely essential for companies to keep their servers cool in order to guarantee their proper operation—that’s Data Center 101—but at the industrial data center scale, this energy usage can quickly become a major financial burden. As such, any tool or technique that can help a company improve its data center cooling efficiency represents an immense value add.
In pursuit of better data center energy efficiency, Google and DeepMind recently experimented with using AI to optimize their cooling activities. According to the Alphabet-owned tech pioneers, the idea was that an AI-powered recommendation system—even one that only makes minor improvements across a wide network of data centers—could cut down on energy usage, slash costs, and make facilities more environmentally sustainable.
Thus far, the project has been an overwhelming success: the application of DeepMind’s machine learning algorithms in Google’s data centers has reduced the energy used for cooling by as much as 40 percent, without compromising server performance.
Using AI to Mitigate Staffing Shortages
Before the rise of cloud computing and the consequent proliferation of remote compute and storage assets, data centers were relatively straightforward systems that could be staffed by just a handful of qualified professionals. However, the emergence of new—and generally more complex—offerings in the cloud computing space (think: SaaS, PaaS, IaaS, etc.) has transformed the typical data center into a high-tech clearinghouse for a variety of critical corporate workloads. As these offerings have found their way into more and more data centers, the demand for IT professionals with the requisite skill sets to manage them has skyrocketed.
Unfortunately, the number of qualified candidates for these positions has remained fairly stagnant. As such, data center management teams are facing a severe staffing shortage that may one day threaten companies’ ability to adequately maintain their digital assets. In order to keep pace with the growing demand being placed on data centers, corporate stakeholders must now make a choice to either fight tooth and nail for limited talent or invest in solutions that allow data centers to thrive in the absence of extensive human oversight.
Thankfully, AI technology offers just such a solution, assisting with a range of server functions without automating IT management entirely. AI platforms can autonomously perform routine tasks like systems updating, security patching, and file backups while leaving more nuanced, qualitative tasks to IT personnel. Without the burden of handling each and every user request or incident alert, IT professionals can assume oversight roles over tasks that previously required their painstaking attention, affording them more time to focus on bigger picture management challenges.
For both individual companies and third-party data center vendors, this partnership-based approach provides a happy medium between outright automation and chronic understaffing. In five or ten years’ time, this “hybrid” management model is likely to be the norm throughout the data center industry. Machines are not going to replace human workers—at least not anytime soon—but they can help overworked IT teams do everything that needs to be done to keep a data center running smoothly.
Opinions expressed in the article above do not necessarily reflect the opinions of Data Center Knowledge and Informa.
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