Data Center Key Performance Indicators – A Look At PUE and Other Numbers to Evaluate Performance

March 17, 2016 Lauren Mahoney-Pick

Written by Dan Hirsh
View the original post on the Schneider Electric blog

Metrics and key performance indicators (KPIs) for data centers used to be inward looking. In the not so distant past, KPIs tended to be “about the box,” measuring factors like server utilization.

Information technology (IT) managers steadily realized there was more to data centers than utilization, or even uptime. In particular, quality of service to business users grew in importance. As a result, we began to see metrics in areas like query and transaction response times, or with IT help desk, job ticket turnaround.

In recent years, the energy costs of data centers came into focus. In a 2007 report to Congress, the Environmental Protection Agency figured thatU.S. data centers consumed about 61 billion kilowatt-hours (kWh) in 2006, about as much energy as used by 5.8 millionU.S. households. It also predicted that by 2011, energy use inU.S. data centers would exceed 100 billion kWh, or $7.4 billion in annual cost.

As highlighted in the white paper 154, “Electrical Efficiency Measurement for Data Centers,” a 1 MW high availability data center can consume $20 million in electricity over its lifespan. What can be done to minimize such huge spend levels? Well, we know it’s not just IT hardware consuming electricity. As the paper points out, the power and cooling infrastructure in a typical installation can consume half the electricity.

This realization has elevated the importance of the Power Usage Effectiveness (PUE) metric as a way to measure the efficiency of a data center’s physical infrastructure.  PUE has become one of the most vital data center metrics. If managers measure PUE effectively, they have a great tool for ensuring energy is spent where it matters most, but there also is the danger of getting overly hung up on one metric.

The best approach is more holistic, taking a step back to consider bigger objectives, including:

  • Having a dashboard framework that is able to integrate metrics from all types of systems: data center infrastructure management (DCIM), systems management software to track application performance, IT help desk metrics, and even metrics from enterprise resources planning systems that might contain financial goals for IT operations.
  • The ability to drill down from higher level metrics such as PUE to lower level metrics on factors like temperature or humidity. A DCIM framework can provide links to lower-level visualization to support a closed-loop approach to moving metrics in the right direction. For a detailed examination of how the subsets within DCIM work together to improve operations, check out white paper 107, “How Data Center Infrastructure Management Software Improves Planning and Cuts Operational Costs.”
  • Consistency in measurement, especially with distributed data centers. For instance, is one data center PUE calculation incorporating power consumption estimates for switch gear, while another isn’t?  Does one data center measure power at the racks, while the others measure at the uninterruptable power supply? (For detailed guidance on PUE, see Schneider Electric white paper 158).

In other areas of business, the need to think more broadly about metrics and link them with strategy is seen in methods such as The Balanced Scorecard, which combines financial and non-financial measures to better gauge corporate performance. Similarly, data center managers need the right mix of metrics.

As Kevin Brown notes in a recent blog post, we risk becoming “metric zombies” if we assess a metric like PUE in a vacuum, irrespective of projects like data center consolidations that might temporarily harm PUE, but whose achievement is necessary to keep budgetary mandates on track. In short, think first about the whole range of metrics and drill downs you want in a dashboard—not just one number.

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