Machine learning will significantly change the data center economy and pave the way for an improved future.
As racks start to fill with ASICs, GPUs, FPGAs, and supercomputers, machine learning and artificial intelligence have entered the data center and are transforming how the hyperscale server farm looks.
These technologies increase the computer power available for training machine learning systems, a task that formerly required massive amounts of data processing. The ultimate goal is to build applications that are smarter and to enhance the services you already use on a daily basis. Relying solely on human judgment and common sense will fall well short of the required standards of precision and effectiveness. The only sustainable approach to meet the demand for IT services at scale is to completely move toward data-driven decisions and use all that data to improve outcomes. Due to the availability of vendors like Schneider Electric, Maya Heat Transfer Technologies (HTT), and Nlyte Software that provide data center management software or cloud-based services that make use of the technology, some businesses or colocation providers that don’t have the same scale or expertise have become early adopters of machine learning.
By 2022, according to IDC, 50% of IT assets in data centers will operate independently thanks to embedded AI technology. Many of the overall operations, including planning and design, workloads, uptime, and cost management, can be optimized in data centers using machine learning.
Here are some of the biggest use cases for machine learning in data center management today:
- Making data centers more efficient: Instead of relying on software alerts, businesses can use machine learning to autonomously manage the physical surroundings of their data centers. This would involve the software making changes to the architecture and physical layout of the data center in real-time.
- Planning for Capacity: Machine learning in datacenter can help IT companies predict demand so they don’t run out of space, power, cooling, or IT resources. Algorithms can assist a corporation in determining how the transfer affects capacity at a facility, for instance, if it is consolidating data centers and moving applications and data to a central data center.
- Reducing operational risk: Preventing downtime is a key task for data center operators, and machine learning can make it easier to predict and prevent it. Machine learning software in data center management keeps track of the performance data from crucial components, like cooling and power management systems, and forecasts when the gear might break. As a result, you can do preventative maintenance on these systems and avoid expensive outages.
- Using smart data to reduce customer churn: Companies may use machine learning in data centers to better understand their clients and perhaps predict client behavior. By integrating machine learning software with a customer relationship management (CRM) system, the AI-powered data center may be able to search for and retrieve data from historical databases that aren’t typically used for CRM, which would then enable the CRM system to develop new lead generation or customer success strategies.
- Budget Impact Analysis and Modeling: This technique combines operational and performance data from data centers with financial data, especially information on applicable taxes, to help determine the price of buying and maintaining IT equipment.
Machine learning can examine terabytes of historical data and apply parameters to its decisions in fractions of seconds because it can act faster than any human. This is helpful when you’re keeping track of all activity in a data center. The two main problems that vendors and data center operators are utilizing machine learning to solve are efficiency improvement and risk reduction.
For instance, the world’s largest provider of colocation, Digital Realty Trust, which operates more than 200 data centers, recently started testing machine learning technology. A human’s capacity to consume and process the number of underlying systems, devices, and data needed to sustain the infrastructure is soon running out. Due to its superior real-time processing, reaction, communication, and decision-making capabilities, Digital Realty will benefit from this.
The basic conclusion is that data center operators have many alternatives for utilizing AI/ML, and there will be even more as the technology becomes more affordable and advanced. A promising future lies ahead.
