AI tackles workload management challenges in the data center

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As data center workloads spiral upward, a growing number of enterprises are looking to artificial intelligence (AI), hoping that technology will enable them to reduce the management burden on IT teams while boosting efficiency and slashing expenses.

AI promises to automate the movement of workloads to the most efficient infrastructure in real time, both inside the data center as well as in a hybrid-cloud setting comprised of on-prem, cloud, and edge environments. As AI transforms workload management, future data centers may look far different than today’s facilities. One possible scenario is a collection of small, interconnected edge data centers, all managed by a remote administrator.

Due to a variety of factors, including tighter competition, inflation, and pandemic-necessitated budget cuts, many organizations are seeking ways to reduce their data center operating costs, observes Jeff Kavanaugh, head of the Infosys Knowledge Institute, an organization focused on business and technology trends analysis. “AI and automation have proven to be powerful tools in workload management, as it frees employees from time-consuming and mundane tasks and allows them to focus on work that actually requires a human,” he says.

Most data center managers already use various types of conventional, non-AI tools to assist with and optimize workload management. Yet these tools tend to be reactive rather than proactive, says Sean Kenney, director, advisory, at professional services firm KPMG. “They react to the problems in the data center, but they don’t collect data to determine any foresight to reduce the problem behavior,” he notes.

Sanket Shah, a clinical assistant professor of biomedical and health information sciences at the University of Illinois, Chicago, believes that AI now is poised to help data center managers who find themselves with no reliable way to predict or plan for future needs. “With AI, capacity and horsepower can be allocated in a more efficient manner, allowing organizations to scale and become flexible,” he explains. “Automating certain processes and shifting power where necessary will ultimately lower costs for those [managers] that have rapidly evolving data needs.”

The idea of using AI technology for data center management is hardly new. Back in 2014, for instance, Google disclosed that it was using technology acquired by its purchase of UK-based AI specialist DeepMind to enhance data center facilities equipment management at several of its sites. Today, the AI workload management field has expanded considerably to include a number of startups, such as DLabs, digitate, Redwood Software, and Tidal Software. Larger players, such as Cisco, IBM and VMware, have also started entering the market.

As with most things AI, workload management technology is advancing rapidly. “There are a ton of choices and a ton of limitations, but there are usually ways to mitigate those limitations,” notes Bill Howe, an associate professor at The Information School of the University of Washington. “I don’t see the problem of choosing the right methods and engineering solutions … to be particularly more or less challenging in workload management than any other complex AI application,” he observes.

Source : AI tackles workload management challenges in the data center | Network World

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