Data Center Infrastructure Management Enhanced by Machine Learning
The data center is increasingly relying on Internet of Things (IoT) devices and machine learning for tasks like infrastructure management and services orchestration. Machines work in concert with human engineers to develop rules for reacting to incidents as they analyze sensor data, uncover relationships between events and make predictions about operational issues.
Hyperscale providers are particularly reliant on machine learning as they compete to deliver highly available, high-performance cloud services. Machine learning, in concert with IoT devices, makes it possible for one engineer to manage more servers. Once scripts are developed, machines can resolve incidents faster than human engineers. They can also use predictive analytics to identify problems before they happen.
Machine Learning in the Data Center
Infrastructure management is one of the initial use cases for machine learning, according to Data Center Knowledge. Sensors monitor a range of variables, including rack temperature, cooling unit operation and settings, cooling redundancy and power use. Based on data analysis, machines learn how to carefully manage infrastructure and generate alerts when certain units may be in danger of failing. They also learn the relationships between all of these variables to determine what factors deliver optimal operations.
Over time, administrators can use predictions to optimize maintenance schedules and procurement, helping to spread out capital expenditure costs over time and preventing expensive malfunctions. This predictive maintenance can minimize downtime, particularly as hyperscale providers compete to enhance their availability guarantees. Hyperscale providers that collect data from multiple data centers over widespread geographic areas have a particular advantage. The sheer volume of data they generate makes it faster for them to build predictive models.
Augmenting Human Insight
Machine learning is a relatively new entrant into the data center environment and works best when augmenting human work. For example, when illness strikes, teams still need to keep infrastructure running at peak performance and always-on availability. Artificial intelligence can help to pick up the slack.
AI hasn’t reached the level of maturity needed to make complex decisions on its own. Cognitive computing requires human partnership to understand patterns in the data it’s analyzing, and cognitive machines need human inputs to understand what actions to take in response to different variables. Once scripts are created, they still need to be fine-tuned for different machines, varying workloads and site-specific quirks.
Still, when it comes to infrastructure management, machine learning can provide enormous cost savings and enhanced performance. Over time, it will become an increasingly vital differentiator between data center providers.