Simple ways to start addressing DataCenter power needs

Anyone that run’s IT infrastructure is aware that power consumption is one of the biggest costs in datacenter provisioning and ongoing expenses.  If they are not, they will soon become aware, as energy costs are predicted to increase in the future, and are the fastest rising cost in the datacenter.

Maximizing power efficiency is a complex topic, which can involve:

  • virtualization to consolidate physical servers
  • adoption of on-demand cloud computing
  • evaluating whether your applications scale in a way such that new, more powerful equipment (which draws more load) will actually be efficient in delivering more requests per Amp (which may not be the case if your bottleneck is latency of an external storage system,  or database, not CPU speed)

However, there are some simple things that all IT Managers should be on top of.

Track your power usage.

You should be tracking your power usage over time.  You should be able to see the total usage, by datacenter, so you can see how your usage is changing as you bring on new servers, or add load to those servers.  As the saying goes, you can’t manage what you don’t measure.

Large numbers of underutilized servers that could be consolidated.

If you have a pool of servers serving a web site, but they are consistently running at less than 10% load, even during peaks, it is often feasible to drastically reduce the number of servers, and just power down a subset of servers.  At LogicMonitor, we’ve seen this situation in our customers more often than you’d think. Often it arises from web functions being migrated from one set of servers to another, as new code is released. The load on the old servers drops, but the number of servers is never reduced.  (One issue to be aware of non-linear scaling of load – but that’s a topic for another post.)

Pay some attention to older servers

Older servers are often the least efficient in terms of productivity per Amp.  By definition, anything running on an older server does not require the latest CPU and memory speeds – which makes such systems a prime candidate for combining on a newer, energy efficient CPU.