With Data Centers, What Can Happen Will Happen (Eventually).

Because data centers and telecom switching centers are designed to withstand failures without interrupting business operations, a 3 a.m. emergency due to a malfunctioning air conditioner should never occur – in theory. But Murphy’s Law says that if a single failure can create an emergency, it will. So, to date, operators have had to react to single-component failures as if they are business-critical. Because they might be.

In my previous blog, I pointed out the two components of risk: the probability of and the consequence of failure. While both of these components are important in failure analysis, it is the consequence of failure that’s most effective at helping decision-makers manage the cost of failure.

If you know there is a high probability of impending failure, but you don’t know the potential consequence, you have to act as though every threat has the potential for an expensive business interruption. Taking such actions is typically expensive. But if you know the consequence, even without knowing the probability of failure, you can react to inconsequential failures at your leisure and plan so that consequential failures are less likely.

In the past, the consequences of a failure weren’t knowable or predictable. The combination of Internet of Things (IoT) data and machine learning has changed all that. It’s now possible to predict the consequence of failure by analyzing large quantities of historical sensor data. These predictions can be performed on demand and without the need for geometrical data hall descriptions.

The advantage of machine learning-based systems is that predictive models are continually tuned to actual operating conditions. Even as things change and scale over time, the model remains accurate without manual intervention. The consequences of actions, in addition to equipment failures, become knowable and predictable.

This type of consequence analysis is particularly important for organizations that have a run-to-failure policy for mechanical equipment. Run-to-failure is common in organizations with severe capital constraints, but it only works, and avoids business interruptions, if the consequence of the next failure is predictable.

Predicting the consequence of failure allows an operations team to avoid over-reacting to failures that do not affect business continuity. Rather than dispatching a technician in the middle of the night, an operations team can address a predicted failure with minimal or no consequence during its next scheduled maintenance. If consequence analysis indicates that a cooling unit failure may put more significant assets at risk, the ability to predict how much time is available before a critical temperature is reached provides time for graceful shutdown – and mitigation.

Preventative maintenance carries risk, but equipment still needs to be shut off at times for maintenance. Will it cause a problem? Predictive consequence analysis can provide the answer. If there’s an issue with shutting off a particular unit, you can know in advance and provide spot cooling to mitigate the risks.

 The ability to predict the consequences of failure, or intentional action such as preventative maintenance, gives facility managers greater control over the reliability of their facilities, and the peace of mind that their operations are as safe as possible.

The Real Cost of Cooling Configuration Errors

Hands in the network cause problems. A setting adjusted once, based on someone’s instinct of what needed to be changed at one moment in time, is often unmodified years later.

This is configuration rot. If your data center has been running for a while, the chances are pretty high that your cooling configurations, to name one example, are wildly out of sync. It’s even more likely you don’t know about it.

Every air conditioner is controlled by an embedded computer. Each computer supports multiple configuration parameters. Each of these different configurations can be perfectly acceptable. But a roomful of air conditioners with individually sensible configurations can produce bad outcomes when their collective impact is considered.

I recently toured a new data center in which each air conditioner supported 17 configuration parameters affecting temperature and humidity. There was a lot of unexplainable variation in the configurations. Six of the 17 configuration settings varied by more than 30%, unit to unit. Only five configurations were the same. Configuration variation initially and entropy over time wastes energy and prevents the overall air conditioning system from producing an acceptable temperature and humidity distribution.

Configuration errors contribute to accidental de-rating and loss of capacity. This wastes energy, and it’s costly from a capex perceptive. Perhaps you don’t need a new air conditioner. Instead, perhaps you can optimize or synchronize the configurations for the air conditioners you already have and unlock the capacity you need. Another common misconfiguration error is incompatible set points. If one air conditioner is trying to make a room cold and another is trying to make it warmer, the units will fight.

Configuration errors also contribute to poor free cooling performance. Misconfiguration can lock out free cooling in many ways.

The problem is significant. Large organizations use thousands of air conditioners. Manual management of individual configurations is impossible. Do the math. If you have 2000 air conditioners, each of which has up to 17 configuration parameters, you have 34,000 configuration possibilities, not to mention the additional external variables. How can you manage, much less optimize configurations over time?

Ideally, you need intelligent software that manages these configurations automatically. You need templates that prescribe optimized configuration. You need visibility to determine, on a regular basis, which configurations are necessary as conditions change. You need exception handling, so you can temporarily change configurations when you perform tasks such as maintenance, equipment swaps, and new customer additions, and then make sure the configurations return to their optimized state afterward. And, you need a system that will alert you when someone tries to change a configuration, and/or enforce optimized configurations automatically.

This concept isn’t new. It’s just rarely done. But if you aren’t aggressively managing configurations, you are losing money.