How to Begin Integrating AI into Data Center Operations
Rich Rogers, a senior vice president of product and engineering at Hitachi Vantara, envisions a data center in which AI-driven management software (some or all of it cloud-based) will monitor and control IT and facilities infrastructure, as well as applications, seamlessly and completely across single or multiple sites. Compute, power, storage, networking and cooling operations will flex dynamically to achieve maximum efficiency, productivity and availability. Human operators, meanwhile, will be free to do what they do best: plan new capabilities and innovate improvements.
“IoT and AI will enable data center issues to be root-caused and resolved automatically
AI-driven automation’s long-term goal is to drive IT managed services toward zero downtime. “Over time we expect the traditional SLA model—99.xx availability, etc.—will have no meaning as the system is always on, compliant, secure, agile and flexible,” advised Satheesh Kumar, IBM’s vice president of hybrid services, AI platform.
Data center infrastructure management is currently highly reactive due to the unexpected arrival of disruptions and delays. AI aims to fix this. “As infrastructure becomes increasingly vital and complex, this resource-intensive approach won’t work,” observed Milan Shetti, general manager of Hewlett Packard Enterprise’s storage division. “It’s no longer acceptable to find out about a disruption after it has occurred or spend the resources to resolve them—that’s the opportunity for AI.”
A rapidly growing number of smart sensors are becoming available to receive data from various data center elements, relaying critical insights into mechanical, electrical and environmental conditions.
“This data can be then used
<img class="docimage" src="https://img.deusm.com/informationweek/february/Gab"Either way, humans-in-the-loop will be critical to ensure rules are accurate and machine learning models are performing adequately," said Stanton Jones, a director and analyst at technology market research firm Information Services Group (ISG).
Time consuming and repetitive IT tasks are ideal starting points for an AI automation initiative. “Server restarts, low disk space remediation [and] password resets are great examples of tasks that benefit from AI and can easily be automated in a data center,” explained Gab
Experiment with AI services in sandbox environments before considering a full deployment, suggested Tom Coughlin, IEEE Senior Member and president of Coughlin Associates, a data storage consulting firm located in Atascadero, Calif. “Be sure to have a scalable plan that starts with some features and adds other capabilities as your time and budget allows,” he added. “Make sure that you ask good questions of VARs and consultants on how to best implement AI in data centers.”
It is also important to recognize that AI benefits tend to improve over time. “The more data fed into AI systems, the better it can make recommendations and facilitate better outcomes,” explained Dell EMC CTO John Roese. “An additional benefit in deploying AI is helping technical staff improve their skills and become more productive as they learn and adapt to the new technology.”
Avoiding Pitfalls
Regardless of the approach taken, it’s always a good idea to exercise caution when deploying an AI-driven service. “Don’t hand over decision-making power to AI until you are comfortable that it is making the right decision every time,” warned Marcel Shaw,
a federal systems engineer with Ivanti, a unified IT management software developer. It’s important to give AI technology the time to learn and grow into its environment. “In starting out with AI, have it provide recommendations instead of letting it take action
Another major pitfall is lacking an IT team that can effectively manage the AI system and interpret insights to their maximum value. “AI automation in data centers gives organizations powerful capabilities and insights, but without a team to manage the system and leverage those insights, organizations will be less likely to take full advantage of AI,” Roese advised.
“Organizations shouldn’t underestimate the time, process knowledge and data required to effectively implement AI systems,” Jones said. “Also, organizations should be aware that this is very new technology and getting started now means being on the leading edge of this transformation.”
Adopters must also look beyond vendor hype to accurately judge AI’s potential benefits. “An AI system should have a history of proven success before it can be trusted to make decisions and take actions,” Shetti said. “You’re not going to let a car with only 15 self-driven miles take control, and you should be wary of AI for infrastructure in the same manner.”