How machine learning is changing data center management

Machine learning will dramatically change data center economics and pave the way for an improved future.

As racks begin to fill with ASICs, GPUs, FPGAs, and supercomputers, machine learning and artificial intelligence have entered the data center and are changing the look of hyperscale server farms.

These techniques increase the computer power available for training machine learning systems, a task that previously required extensive data processing. The ultimate goal is to build smarter applications and enhance the services businesses already use every day. Relying solely on human judgment and common sense will fall far short of the required standards of accuracy and validity. The only sustainable way to meet the demand for IT services at scale is to move entirely to data-driven decision-making and use all data to improve outcomes. Due to the availability of industry vendors offering data center management software or cloud-based services that leverage the technology, some enterprises or managed service providers without the same scale or expertise have become early adopters of machine learning.

According to IDC, by 2022, 50% of IT assets in data centers will operate independently due to embedded artificial intelligence technology. Many overall operations, including planning and design, workloads, uptime, and cost management, can be optimized in the data center using machine learning.

Here are some of the biggest use cases of machine learning in data center management today:

• Improving data center efficiency: Enterprises can use machine learning to autonomously manage the physical environment of their data centers, Instead of relying on software alerts. This will involve software making real-time changes to the architecture and physical layout of the data center.
• Capacity Planning: Machine learning in data centers can help IT companies predict demand so they don’t run out of space, power, cooling or IT resources. Algorithms can help a company determine how a shift affects a facility's capacity, for example if it is consolidating data centers and moving applications and data to a central data center.
• Reduce operational risk: Preventing downtime is a critical mission for data center operators, and machine learning can make it easier to predict and prevent. Machine learning software in data center management tracks performance data of critical components, such as cooling and power management systems, and predicts when equipment is likely to fail. As a result, preventive maintenance can be performed on these systems and costly downtime can be avoided.
• Use smart data to reduce customer churn: Companies can use machine learning in data centers to better understand their customers and potentially predict customer behavior. By integrating machine learning software with customer relationship management (CRM) systems, AI-driven data centers may be able to search and retrieve data from historical databases that are not typically used in CRM, which would enable the CRM system to develop new leads or customers. Strategies for success.
• Budget Impact Analysis and Modeling: This technology combines operational and performance data from the data center with financial data (especially applicable tax information) to help determine the price to purchase and maintain IT equipment.

Machine learning can examine terabytes of historical data and apply parameters to its decisions in fractions of a second because it can act faster than any human. This is helpful when you track all activity in your data center. The two main problems vendors and data center operators are solving with machine learning are increasing efficiency and reducing risk.

For example, DigitalRealtyTrust, the world’s largest hosting provider with more than 200 data centers, recently began testing machine learning technology. Human capacity to consume and process the vast array of underlying systems, devices, and data required to sustain infrastructure is quickly exhausted. DigitalRealty will benefit from this due to its superior real-time processing, reaction, communication and decision-making capabilities.

The basic conclusion is that data center operators have many options for leveraging artificial intelligence and machine learning, and there will be more options as the technology becomes more affordable and advanced. A bright future is ahead. ​

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