‘Comatose’ Servers Waste Massive Amounts of Energy

August 27, 2014 By Linda Hardesty

server energy manageA report released by the Natural Resources Defense Council finds that massive amounts of energy are being wasted by data centers and that improved energy efficiency practices could cut energy waste by at least 40 percent, saving over $3 billion annually.

The report, developed in partnership with Anthesis, “Scaling Up Energy Efficiency Across the Data Center Industry: Evaluating Key Drivers and Barriers,” says up to 30 percent of servers are “comatose” and no longer needed because projects have ended or business processes changed, but are still plugged in and consuming electricity. Much of the energy consumed by US data centers powers servers operating at 12 to 18 percent of capacity. Even sitting virtually idle, servers use significant amounts of power 24/7.

While sophisticated data centers owned by the likes of Google and Facebook are very energy efficient, they represent less than 5 percent of US data center electricity consumption, says NRDC. Meanwhile, the nearly three million other small and medium, corporate and multi-tenant data centers are still squandering huge amounts of energy.

Although the full extent of energy waste is unknown due to a lack of consistent metrics, the report estimates that if just half of the potential savings from cost-effective energy efficiency best practices were realized, electricity use could be cut by 40 percent. In 2014, that would equal $3.8 billion in savings for businesses and cut 39 billion kWh of electricity.

Photo: Server room via Shutterstock

One comment on “‘Comatose’ Servers Waste Massive Amounts of Energy

  1. The issue regarding “comatose” servers that are still plugged in may be prolonged because no one is aware that they are in fact no longer being used. Employees likely have no idea as to how much energy is wasted by continuing to power these servers. With the potential to save over $3 billion annually, these data centers may want to take advantage of big energy data to figure out where they can reduce energy consumption and cost.

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