Energy Efficient Data Centre, Realistically

Adopted from www.datacenterknowledge.com

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 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

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 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. What happens to the PUE in such a case?
PUE (after virtualization) = 75 / 25 = 3.0

But isn’t this higher value what we want to avoid? Well, not necessarily. Let us understand the reason behind the increase or decrease in PUE value. While it may seem ambiguous, any reduction in IT usage will actually result in a higher PUE.

Here’s another formula for PUE:

PUE = IT Load + Infrastructure Load / IT Load = 1+Infrastructure Load / IT Load

Thus, when IT load is reduced, Infrastructure Load / IT Load will always increase, thereby, resulting in an increase in the PUE. Conversely, increasing the IT load will always decrease the PUE. So, if the PUE has gone up, does this mean the data center is now less energy efficient? On the contrary, the data center is now more energy efficient. We are able to do more with less now, that is, same work with less energy at a lesser cost.

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:
Annual energy utilization = 75kW x 8760 hrs/yr = 657000 kWh
Annual electricity cost = 657000 kWh x Rs. 3.10/kWh* = Rs. 20, 36,700
*Base Tariff for HT I – Industries – Mahadiscom

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.

PUE becomes a meaningless number if we do not know how to use it to measure the outcome of changes in the data center. Knowing that virtualization will eventually increase the PUE of our data center, should we avoid it? No, infact when we examine the PUE of our data center over a period of time we should also take into account when the virtualization actually took place.

Other Variables in Energy Efficiency

We must track any changes that may have taken place in the IT infrastructure or IT Load in addition to tracking our PUE, so that we are able to correlate the changes to the PUE value. There are many other factors which may impact PUE. Redundancy, for example, will increase PUE. There will always be tradeoffs between availability and energy efficiency. Data center equipment – from cooling equipment to UPSs to server power supplies – will run more efficiently when they are heavily loaded.

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.


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