Going Beyond PUE for Data Center Efficiency

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Nitin Mishra, VP, Product Management & Solutions Engineering, Netmagic Solutions Pvt. Ltd.

Nitin MishraNITIN MISHRA
NetMagic

This column is part one of a two-part series on PUE.

Today, many organizations are looking for new ways of doing more with less, reducing IT budgets or curtailing the incidental costs associated with data center expansions. And data center managers need to focus on creating efficient operating environments to augment the life of existing data centers. Data center efficiencies can be attained through increasing compute densities, creating cold aisle containment systems or more effective use of outside air, but the key component over time is to have an easily understood metric to gauge data center efficiency, and how much improvement is taking place.

Power Usage Effectiveness (PUE) is one of the basic and most effective metrics for measuring data center energy efficiency. It is calculated by taking the total power consumed by a data center facility and dividing it by the power consumed by the IT equipment. The resulting ratio provides the effective power overhead for a unit of IT load. For example, a PUE value of 2.0 means that for every watt used to power IT equipment, an additional watt is required to deliver the power and keep the equipment cool. Data center managers are increasingly working to take measures to reduce PUE.

The PUE metric was introduced by the Green Grid, an association of IT professionals focused on increasing the energy efficiency of data centers. Green Grid also published the DCiE (Data Center Infrastructure Efficiency) metric. Both metrics measure the same two parameters, the total power to the data center and the IT equipment power.

PUE = Total Power into Datacenter/IT Equipment Power

DCiE = IT Equipment Power/Total Power into Datacenter

A PUE value of 1 depicts the optimal level of data center efficiency. In practical terms, a PUE value of 1 means that all power going into the data center is being used to power IT equipment. Anything above a value of 1 means there is data center overhead required to support the IT load.

Data Center Infrastructure Effectiveness (DCiE) is the reciprocal of PUE. It is calculated as a percentage by taking the total power of the IT equipment and dividing it by the total power into the data center multiplied by 100. A PUE value of 3.0 would equate to a DCiE value of 33%, or suggest that the IT equipment was consuming 33% of the facility’s power.

As I stated above, in an ideal case scenario, all the power entering the data center should be used to operate the IT load (servers, storage and network). If we consider that all the power entering the data center is consumed for operating it, then the resultant PUE should ideally be 1. Realistically, however, some of this power is diverted to support cooling, lighting and other support infrastructure. Some of the remaining power is consumed due to losses in the power system, and the rest then goes to service the IT load.

Calculating PUE

Consider that the power entering the data center (measured at the utility meter) is 100 kW and the power consumed by the IT load (measured at the output of the UPS) is 50 kW, PUE will be calculated as follows:

PUE = 100 / 50 = 2.0

A PUE value of 2.0 is quite usual for a data center. It means that for every watt required to power a server, 2 watts of power is consumed. Since we pay for every watt of power entering the data center, every watt of overhead represents an additional cost. Reducing this overhead will reduce the overall operating costs for the data center.

The two ways in which we can bring about a change and improve data center energy efficiency include:

  • Reducing the power going to the support infrastructure
  • Reducing losses in the power system.

This way we can ensure that more of the power entering the data center should make it to the IT load; consequently, improving data center energy efficiency and reducing the PUE.

PUE Metric Is Not Always Ideal

[Editor's note: This section was updated Jan 2, 2012 with new example and calculations.]

Are there drawbacks to using PUE as a measurement of data center efficiency? Data center managers are under immense pressure to reduce costs and match the reported PUE with that of other companies. Unfortunately, this is not always the right approach and can have a negative impact. If data center managers focus only on reducing PUE, they may inadvertently use more energy and increase data center costs.

For example, a captive data center which has input power of 100 kW, 50kW of which is being used to power IT equipment. As previously illustrated, this would give us an initial PUE value of 2.0.
Suppose the organization now decides to virtualize some servers. In fact, it is so successful with virtualization that it is able to reduce the power to IT equipment by 25 kW and the overall power to the data center by the same amount. In this case with the same compute capacity, the PUE may go up as data center utilization goes down but it will still lead to a higher saving on overall power cost. So PUE should not be the only focus for saving power.

Example of Virtualized/Unvirtualized Data Centers

Here’s an example using power-pricing data from Maharashtra state in India.

Before virtualization:

Annual energy utilization = 100kW x 8760 hrs/yr = 876000 kWh
Annual electricity cost = 876000kWh x Rs. 3.10/kWh* = Rs. 27, 15,600
*Base Tariff for HT I – Industries – Mahadiscom

After Virtualization:

Assumption that the PUE goes up to 2.1 due to reduced capacity utilization.
Annual energy utilization = (25+25*1.1=52.5) kW x 8760 hrs/yr = 459900 kWh
Annual electricity cost = 459900 kWh x Rs. 3.10/kWh* = Rs. 14,25,690
*Base Tariff for HT I – Industries – Mahadiscom, for example at commercial consumer level prices are at more than 2x of these levels.

Above example shows that there is huge amount of savings despite of increase in PUE, which demonstrates that IT load management can deliver better results than just PUE optimization. Considering that both data centers (both before and after virtualization) are able to perform the same amount of work, we can see from the above calculation that the virtualized data center is noticeably more energy efficient. In fact, the virtualized data center can be made even more energy efficient if the support infrastructure is now reduced to match the reduced IT load.

The bottom line is that PUE, while an important piece of the energy efficiency puzzle, is just that – one piece of the energy efficiency puzzle. PUE constitutes only one component of a comprehensive energy management program which must consider both sides of the coin – the IT and the facility.

This is part one of a two-part series.

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2 Comments

  1. Nice article, Nitin, thanks for promoting The Green Grid work. 2 comments: First, when people analyze the impact of IT reductions on PUE, like you do in your virtualization example, they frequently ignore the facilities saving that comes with a reduction in IT power. In your example, IT + Facilities before virtualization is 50kW + 50kW. After virtualization, you use 25kW + 50kW. In fact, the facilities consumption should reduce with reduced IT load, since there is 25kW less power to cool, condition, and distribute. A more realistic reduction would be after virtualization consumption of 25kW + 35KW, resulting in a post-virtualization PUE of 2.4 (vs the 3.0 in your calculation) Second, the fact that PUE can increase after reduction of IT load correctly puts the focus on the increased waste in the facility. If overhead does not scale linearly with load (as in both of our examples) an increased PUE shows data center operators where to look next to reduce unnecessary consumption. No one should use PUE as the only measure of efficiency: total energy consumption is probably the most important metric, and that measure was reduced in both of our examples. I hope you continue to write about PUE and resource efficiency!

  2. Nitin Mishra

    Your observations are correct. PUE is a function of DC design and utilization of the data center. We see PUE of as high as 3 when the utilization is low 10-20% and it reaches it optimum design of 1.6 to 1.9 on touching 70%+ utilisation of designed capacity. Sorry that the example doesn't spell out the assumption / context clearly and also has error in showing that the Infra power usage going up on IT power consumption reduction. Clearly it will not reduce in proportion of reduction of IT load by it will reduce from 50 KW. The original intended point has got lost somewhere in translation and simplification.