How to Use PUE to Improve Efficiency

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Schneider Electric Table of Contents

How to Use PUE to Improve Efficiency

Table of Contents

Introduction .................................................................3

Conclusion ..................................................................9

PUE 101 ......................................................................4

Putting PUE into action ................................................6

Modeling vs. Measurments .....................................6

Reporting ...............................................................7

PUE can’t live in a vacuum ..........................................8

Business-wise, Future-drivenTMHow to Use PUE to Improve Efficiency

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Data CentersIntroduction

What’s consuming all that power in the data center? IT equipment uses only about half of the electricity in the data center; power and cooling infrastructure consumes the other half. As a result, managers are paying closer attention to power usage effectiveness (PUE), a metric that indicates the efficiency of a data center’s physical infra-structure. If managers measure and use PUE effectively, they have a great tool for ensuring energy is used more efficiently, and when combined with other management tools, a robust suite to control both capital and operating costs in the data center.

1 MW high-availability data center can consume $20,000,000 of electricity over its lifetime. Recent studies have found that in some data centers, the cost of electricity is greater than the cost of IT hardware over the lifetime of the data center.

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Data CentersPUE 101PUE is the ratio of total data center input power to the IT load power - an indication of how much power is going to the IT load versus “everything else” in the data center. With a theoretical PUE of 1, all of your energy is powering the IT load, and none is powering infrastruc-ture, such as power backup, cooling, and lights. While that scenario is impossible, the goal is to improve infrastructure efficiencies to bring the PUE as close to 1 as possible.

You should measure your PUE initially to establish a baseline, and then on an ongoing basis. You don’t need to measure every circuit – just the ones that are most likely to change over time and that are the largest contributors to power consumption. The key is consistency – measure the same circuits every time, and be consistent in defining which equipment is considered IT load and which is facility load.

In the case of comparing PUEs of different data centers, consistency is just as critical. If one data center’s PUE calculation incorporates power consumption estimates for switch gear, the other’s should too.

If one measures power at the racks, the other should measure at the racks, not at the uninterruptable power supply.You can measure periodically, once in the summer and once in the winter, or monthly, or ideally with a continuous real-time measurement system.

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PUE quantifies the power “overhead” that is consumed in supporting the IT load

PUE 101

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Data CentersPutting PUE into action

Modeling vs. MeasurementsPUE, by itself, is a great way to establish your baseline and measure improvement, but it doesn’t necessarily help you with the step in between—knowing where to take action. However, mathematical models and calculators turn PUE into actionable data. They take inputs specific to your data center, such as IT load, humidity levels and outdoor weather statistics, and calculate efficiency based on those inputs. But, a model will take these calculations a step further, allowing you to simulate different efficiency scenarios until you find the optimal one. With an efficiency model you can:

• Predict the effect of changes such as adding servers, adding cooling, or increasing ambient air temperature

• Estimate for conditions that are impractical to measure, such as industry benchmark conditions

• Estimate with only partial information• Estimate for various outdoor conditions• Allow averages over time• Identify and quantify the contributions of specific devices• Identify subsystems operating outside expected values• Benchmark and compare models of different data centers at the

same % IT load

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ReportingIn additional to simulating efficiency scenarios, a model should also report on key data points, such as:

• Actual annual PUE performance

• Expected annual PUE under industry-standard conditions, from the model

• Design value for annual PUE

• Annual average IT load (% of rated capacity)

• Breakdown of power consumption by major data center subsystem

• Comparison of actual annual PUE to other data centers of similar classification

• Comparison of expected annual PUE to other data centers of similar classification

• Total annual electrical consumption (kW-hr)

• Estimated annual electrical cost

• Trending actual annual PUE, annual electrical consumption, and electrical costs

Putting PUE into action

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Data CentersPUE can’t live in a vacuumAnother reason that modeling is so important, is that it puts PUE into a broader context. We’ve already mentioned that one isolated mea-surement of PUE doesn’t really yield actionable data: Is it good? Bad? Are we headed in the right direction?

More than this, PUE can be misleading when out of context. For instance, it’s possible to reduce your energy consumption, but make your PUE worse. Without the broader context (such as from a model), PUE loses it’s meaning and value.

The best approach is more holistic, using PUE as one component of a system that focuses on the larger objectives around efficiency and cost containment, including:

• Having a dashboard that can integrate metrics from all types of systems, including data center infrastructure management (DCIM) tools and systems management software to track application performance.

• Management and planning tools that incorporate PUE, performance, and physical layout data to support decisions, such as where to locate additional cooling or how to free stranded power capacity

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Data CentersConclusion

Whether your facility is new or existing, saving energy is a crucial step toward reducing operating and capital costs.

You can’t control it if you don’t measure it, so the first step in managing efficiency is knowing and understanding your data center’s PUE. Finally, using PUE in the proper context and in conjunction with other key metrics, as part of a holistic dashboard, give data center operators critical insight into efficiency trending, where inefficiencies exist, and where the opportunities for improvement are.

For more information on how PUE and DCIM tools work together, check out our white paper, “How Data Center Infrastructure Management Software Improves Planning and Cuts Operational Costs.”

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