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Energy-aware computing

New ways to keep cool


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The key technical challenge of an always-on compute utility is the management of energy as a resource.

By Jamie Beckett, Jan. 2006

Soaring demand for information technology, increasingly powerful microprocessors and the growing popularity of compact blade servers means that data centers are more densely packed than ever – and that means a tremendous demand for cooling.

There are two primary ways to reduce the costs of cooling data centers: Design more energy-efficient data centers and design machines that generate less heat. HP Labs is developing both.

'We believe in looking at energy-aware computing holistically – from the chips to the servers to the data centers to the services that run in the data centers," says John Sontag, director of the lab's infrastructure virtualization and data center architecture research.

This month, HP is announcing several new products and services, including Data Center Thermal Assessment Services, that grew in part from these research efforts.

Managing energy consumption

Researchers are now pursuing new frontiers -- designing hardware that consumes less energy, deploying computing workloads in a more energy-efficient manner, developing systems for dynamically allocating cooling resources and creating sophisticated sensing systems to control cooling.

The goal: To provide better energy efficiency, increase data center uptime and to allow data centers to operate at higher power densities so users can get the most out of their IT investments."

The key technical challenge of an always-on compute utility is the management of energy as a resource," says Chandrakant Patel, who leads the HP Labs component of the "Cool Team," a network of technologists across HP who are developing energy-management technologies.

Generating less heat

Partha Ranganathan is focused on the subtraction side of the energy equation. He's investigating technologies that will squeeze inefficiencies out of current systems to reduce the amount of heat they generate.

One approach involves introducing heterogeneity. Rather than using identical processors on multi-core chips, for example, Ranganathan says his research has shown that mixing different processors on a chip can reduce energy consumption by 40 percent with almost no effect on performance. (See related technical paper, Power-aware computing: Heterogeneous Chip Multiprocessors.)

How so? By assigning tasks to whichever processor can perform it most efficiently.

Similar principles apply to individual systems in a blade server, he says.

Managing a power budget

Ranganathan and his team have also developed an algorithm for workload placement and resource provisioning that, in essence, directs the most heat-intensive workloads to the coolest location in the data center.

In addition, researchers have prototyped a system that can work with 20 to 30 percent reduced cooling by managing a heat "budget" across an entire collection of systems -- allocating resources according the urgency of a job, its importance to the organization, service-level agreements or other factors.

The prototype works in much the way any other budget does, Ranganathan says. "My manager knows that potentially, every one in his team could order a new PC in a quarter, but he does not plan his purchase budget for that worst case. He just plans it for a likely scenario based on past experience. You should be able to plan for heat in the same way."

Adding precision

Chandrakant Patel and his team are focused on better managing what heat a data center generates. They've designed a dynamic thermal management system aimed at more closely monitoring and controlling temperature in a data center.

Traditional systems measure data center temperature at the hot-air return of air conditioning units instead at the heat source – the racks. Because controls are imprecise, these data centers are often operated at less-than maximum capacity to avoid overheating. That's an expensive proposition.

The researchers' solution uses a distributed sensor network attached to standard racks, providing a direct measurement of the environment where it is most useful. Experiments to compare the HP Labs solution with a conventional system show potential savings of more than 50 percent in cooling costs.

"We're putting the control point where it should be," says Cullen Bash, a "Cool Team" researcher in HP Labs. "And instead of relying on a single sensor, we're controlling cooling based on many sensors."

Increased availability

HP Labs' dynamic smart cooling technology processes sensor data to determine how best to allocate cooling resources to maintain specified rack temperatures.

"It's not just that we can save energy. By monitoring temperature close to the heat source, we can react a lot faster," Bash says.

That adds up to increased availability for computing systems. Not only do system uptimes increase, but IT managers can run equipment at higher power densities, which allows them to get more use out of existing machines. Bottom line: Businesses can get the most out of their IT investments.

Earlier work, next steps

Energy-aware computing is not a new problem for HP Labs, which has been working in this area since the early 1990s.
In the process, they've found some novel ways to use old technologies -- including HP's classic inkjet technology, which they put to use for targeted spray cooling of microprocessors.

Working with engineers in HP's printing and imaging group and elsewhere in the company, the researchers re-configured the inkjet head to spray tiny droplets of dielectric liquid coolant instead of ink.

Later, researchers developed technology that became the basis for HP's recently announced Data Center Thermal Assessment Services. This service, already in use by several customers, assesses the unique thermal conditions in a data center and develops recommendations for better cooling.

A follow-on technology called dynamic smart cooling is used in HP Labs' Palo Alto, CA, data center. In the lab's 100-rack data center, dynamic smart cooling has generated savings of 60 percent.

The next step for researchers is merge all their work into a single integrated system.

"What we're doing is using the most efficient ways of distributing computing resources," says Patel, "then threading those together in a stack in such a way that we're minimizing energy consumption."

Jamie Beckett is managing editor of this Web site and a former reporter and editor at the San Francisco Chronicle.

Related links

» Smart power research
» Smart cooling research

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Related technical publications

» Power-aware computing : Heterogeneous Chip Multiprocessors
» Thermo-fluids provisioning of a high-performance, high-density data center
» A vision of energy-aware computing, from chips to data centers
» Balance of power: Dynamic thermal management for Internet data centers
» Ink-assisted spray cooling of electronics

Power modeling in a data center

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