Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. In this edition, we’ll learn about AI deployment in data center operations and how it enables better performance and efficiency.
Artificial intelligence (AI) is the talk of the town. Organizations worldwide are using the technology to support their workforces and especially with mundane tasks so human efforts can be dedicated to more vital activities. Another focus of AI lately has been in data centers where the crucial data and applications reside and only authorized personnel are allowed entry. How can AI improve operations and performance within a data center? Let’s find out.
With the pandemic forcing the workforce community to stay at home or in remote locations, it became inevitable for data center service providers to look at other options that would be employee-friendly and still get the job done. AI came to the rescue. Even before the pandemic, Google employed AI to cool down its data centers, and this helped reduce energy requirements significantly. Now, since personnel movements are restricted due to the pandemic, data center vendors are turning to AI to implement a few innovative and important applications.
The impact of AI is huge and fascinating, and it is being deployed for many purposes in data centers. For instance, a deployment for capacity planning will manage the power load, computing resources, and other IT assets to meet customer demands. Other areas where AI is widely used are failure and anomaly detection, root cause analysis, and outage prediction, which also helps reduce an organization’s carbon footprint. Advancements now include using robots that are AI-powered to locate, inspect and replace faults within the premises. In addition, by using Internet of Things devices and the data collected by them, performance issues can be easily addressed proactively. With data collected in massive amounts within the data center, AI in security information and event management technology helps keep cyberattacks at bay, because it analyzes data logs and creates an incident response system to counteract anomalies.
Although AI complements human efforts within data centers, there are some gray areas that need constant attention. Industry standards that encompass monitoring of AI deployments are needed as the type and large amount of data are often unknown. A one-two punch can help with staying compliant with regulations to avoid violations and costly penalties, and streamline implementations. Simulations through AI can show the impact of changes to the infrastructure. Digital twin technology, along with AI integrated with DCIM tools, helps provide better insights before modifications to the data center are implemented.
Here are some interesting articles highlighting various use-cases and best practices while using AI in data center operations:
A good alternative to the conventional method of hiring staff to maintain and monitor data centers is to deploy AI. Not only can it handle tasks like server optimization and equipment handling, it can also help with data and network security, and energy conservation within the data center.
In well-equipped data centers, AI is used for data management, IT workload management, and cost management. Now, with chatbots in the picture, several tasks are automated to attain optimized results. Further, these bots can be leveraged to provide better interaction and user experiences based on the needs of the IT teams.
Another use-case revolves around increasing our reliability on the services that data centers provide. This is delivered by using predictive algorithms to take actions regarding power and latency to keep up the performance. In addition, every IT component can be investigated through AI, that is augmented by advanced monitoring.
To establish optimal performance, AI provides an understanding of expected vs. actual scenarios. This gives a level of insight for effective performance monitoring, which helps save cost and energy. Also, with exception reporting, teams can adapt a needs-based approach and still achieve efficiency in monitoring and maintaining data center operations.
While scaling out the infrastructure for growing data needs and analysis, data scientists and IT admins must work closely to determine the requirements for AI to become a valuable part of the operations. AI is supported by machine learning (ML) a subset of AI that provides the algorithms and techniques to make the computer learn, and deep learning (DL) that is often described as the evolution of ML. Further, setting benchmarks and metrics when determining scalability will help choose an approach that’s suitable for AI to be incorporated in the data centers without causing any challenges.
The future seems bright with AI taking up a lot of load, despite some challenges. If these hiccups are removed using appropriate actions, the benefits can be greater than we ever imagined. It is up to the data center vendors to use AI wisely to gain an edge over their competitors and stay on top of things. There’s more to AI in data center operations than meets the eye and we will see this progress in the years to come.