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.

Analytics in Action for Data Center Cooling

When a data center is first designed, everything is tightly controlled. Rack densities are all the same. The layout is precisely planned and very consistent. Power and space constraints are well-understood. The cooling system is modeled – sometimes even with CFD – and all of the cooling units operate at the same level.

But the original design is often a short-lived utopia. The realty of most data centers becomes much more complex as business needs and IT requirements change and equipment moves in and out.

As soon as physical infrastructure changes, cooling capacity and redundancy are affected.  Given the complexity of design versus operational reality, many organizations have not had the tools to understand what has changed or degraded, so cannot make informed decisions about their cooling infrastructure. Traditional DCIM products often focus on space, network and power.  They don’t provide detailed, measured data on the cooling system.  So, decisions about cooling are made without visibility into actual conditions.

Analytics can help. Contrary to prevailing views, analytics don’t necessarily take a lot of know-how or data analysis skills to be extremely helpful in day-to-day operations management. Analytics can be simple and actionable. Consider the following examples of how a daily morning glance at thermal analytics helped these data center managers quickly identify and resolve some otherwise tricky thermal issues.

In our first example, the manager of a legacy, urban colo data center with DX CRAC units was asked to determine the right place for some new IT equipment. There were several areas with space and power available, but determining which of these areas had sufficient cooling was more challenging. The manager used a cooling influence map to identify racks cooled by multiple CRACs. He then referenced a cooling capacity report to confirm that more than one of these CRACs had capacity to spare. By using these visual analytics, the manager was able to place the IT equipment in an area with sufficient, and redundant, cooling.

In a second facility, a mobile switching center for a major telco, the manager noticed a hot spot on the thermal map and sent a technician to investigate the location. The technician saw that some of the cooling coils had low delta T even though the valves were open, which implied a problem with the hydronics. Upon physical investigation of the area, he discovered that this was caused by trapped air in the coil, so he bled it off. The delta T quickly went from 3 to 8.5 – a capacity increase of more than 65 percent – as displayed on the following graph:



These examples are deceptively simple. But without analytics, the managers would not have been able to as easily identify the exact location of the problem, the cooling units involved, and have enough information to direct trouble-shooting action within the short time needed to resolve problems in a mission critical facility.

Analytics typically use the information already available in a properly monitored data center. They complement the experienced intuition of data center personnel with at-a-glance data that helps identify potential issues more quickly and bypasses much of the tedious, blood pressure-raising and time-consuming diagnostic activities of hotspot resolution.

Analytics are not the future. Analytics have arrived. Data centers that aren’t taking advantage of them are riskier and more expensive to operate, and place themselves at competitive disadvantage

Maintenance is Risky

No real surprise here. Mission critical facilities that pride themselves on and/or are contractually obligated to provide the “five 9’s” of reliability know that sooner or later they must turn critical cooling equipment off to perform maintenance. And they know that they face risk each time they do so.

This is true even for the newest facilities. The minute a facility is turned up, or IT load is added, things start to change. The minute a brand new cooling unit is deployed, it starts to degrade – however incrementally. And that degree of degradation is different from unit to unit, even when those units are nominally identical.

In a risk and financial performance panel presentation at a recent data center event sponsored by Digital Realty, ebay’s Vice President of Global Foundation Services Dean Nelson recently stated that “touching equipment for maintenance increases Probability of Failure (PoF).” Nelson actively manages and focuses on reducing ebay’s PoF metric throughout the facilities he manages.

Performing maintenance puts most facility managers between the proverbial rock and a hard place. If equipment isn’t maintained, by definition you have a “run to failure” maintenance policy. If you do maintain equipment, you incur risk each time you turn something off. The telecom industry calls this “hands in the network” which they manage as a significant risk factor.

What if maintenance risks could be mitigated? What if you could predict what would happen to the thermal conditions of a room and, even more specifically, what racks or servers could be affected if you took a particular HVAC unit offline?

This ability is available today. It doesn’t require computational fluid dynamics (CFD) or other complicated tools that rely on physical models. It can be accomplished through data and analytics. That is, analytics continually updated by real-time data from sensors instrumented throughout a data center floor. Gartner Research says that hindsight based on historical data, followed by insight based on current trends, drives foresight.

Using predictive analytics, facility managers can also determine exactly which units to maintain and when – in addition to understanding the potential thermal affect that each maintenance action will have on every location in the data center floor.

If this knowledge was easily available, what facility manager wouldn’t choose to take advantage of it before taking a maintenance action? My next blog post will provide a visual example of the analysis facility managers can perform to determine when and where to perform maintenance while simultaneously reducing risk to more critical assets and the floor as a whole.

Predictive Analytics & Data Centers: A Technology Whose Time Has Come

Back in 1993, ASHRAE organized a competition called the “Great Energy Predictor Shootout,” a competition designed to evaluate various analytical methods used to predict energy usage in buildings.  Five of the top six entries used artificial neural networks.  ASHRAE organized a second energy predictor shootout in 1994, and this time the winners included a balance of neural networks and non-linear regression approaches to prediction and machine learning.  And yet, as successful as the case studies were, there was little to no adoption of this compelling technology.

Fast forward to 2014 when Google announced its use of machine learning leveraging neural networks to “optimize data center operations and drive…energy use to new lows.”  Google uses neural networks to predict power usage effectiveness (PUE) as a function of exogenous variables such as outdoor temperature, and operating variables such as pump speed. Microsoft too has stepped up to endorse the significance of machine learning for more effective prediction analysis.  Joseph Sirosh, corporate vice president at Microsoft, says:  “traditional analysis lets you predict the future. Machine learning lets you change the future.”  And this recent article advocates the use of predictive analytics for the power industry.

The Vigilent system also embraces this thinking, and uses machine learning as an integral part of its control software.  Specifically, Vigilent uses continuous machine learning to ensure that predictions driving cooling control decisions remain accurate over time, even as conditions change (see my May 2013 blog for more details).  Vigilent predictive analysis continually informs the software of the likely result of any particular control decision, which in turn allows the software to extinguish hot spots – and most effectively optimize cooling operations with desired parameters to the extent that data center design, layout and physical configuration will allow.

This is where additional analysis tools, such as the Vigilent Influence Map™, become useful.  The Influence Map provides a current, real-time and highly visual display of which cooling units are cooling which parts of the data floor.

As an example, one of our customers saw that he had a hot spot in a particular area that hadn’t been automatically corrected by Vigilent.  He reviewed his Vigilent Influence Map and saw that the three cooling units closest to the hot spot had little or no influence on the hot spot.  The Influence Map showed that cooling units located much farther away were providing some cooling to the problem area.  Armed with this information, he investigated the cooling infrastructure near the hot spot and found that dampers in the supply ductwork from the three closest units were closed.  Opening them resolved the hot spot.  The influence map provided insight that helped an experienced data center professional more quickly identify and resolve his problem and ensure high reliability of the data center.

Operating a data center without predictive analytics is like driving a car facing backwards.  All you can see is where you’ve been and where you are right now.  Driving a car facing backwards is dangerous.   Why would anyone “drive” their data center in this way?

Predictive analytics are available, proven and endorsed by technology’s most respected organizations.  This is a technology whose time has not only come, but is critical to the reliability of increasingly complex data center operations.


The Value of Efficiency-Aware Decision Making

My Chevy Volt displays my gas mileage.  In fact, I knew what the mileage performance would be before I bought the car. It was a factor in my purchase choice.

In addition to cars, most large appliances display power use along with Energy-Star certification. Residential air conditioners display standard energy efficiency ratings (SEER).   Even large commercial building air conditioners have to meet standard rating conditions for efficiency.

Yet, it is only recently that efficiency ratings have been specified for data center cooling.  The primary reason is that for years, manufacturers of cooling units for mission critical facilities avoided efficiency ratings requirements claiming that, because their products were used for process cooling versus comfort cooling, efficiency standards shouldn’t apply.  Fortunately, ASHRAE took up the charge and updated Standard 90.1 so that equipment covered by ASHRAE Standard 127 is required to meet minimum efficiency standards.  Standard 90.1 has been adopted by the Department of Energy as a federal energy standard and is now referenced by many code authorities.

While useful and certainly progress, the choice enabled by these two standards is just a start.  Certainly new equipment can and should be compared based on energy efficiency ratings.  However we all know that equipment efficiency will vary considerably through use. It would also be useful to be able to  view and compare the operational efficiency of existing equipment in order  to evaluate which machines are working well, which should be replaced (using the new equipment efficiency ratings as a baseline of comparison) –  and how much efficiency could be gained (and calculated from an ROI perspective) through replacement.

Some HVAC manufacturers have taken up this challenge. NTT, for example, provides the coefficient of performance for its computer room air conditioners in real time, viewable on the front panel of each unit and through a communications interface.  We commend them.

The ability to compare initial purchase energy efficiency ratings against actual performance over time for a particular machine, gives data center managers the ability to not only track and evaluate a machine for individual performance durability, and compare its performance with that of similar machines.  Mechanisms and procedures can be put in place for maintenance as degradation is spotted.   Inefficient machines can be used less, fixed or phased out.

We challenge mission critical cooling system manufacturers to pull back the veil of secrecy on energy efficiency.  The time for transparency is at hand because this information is knowable.  The combination of smart sensors and analytics technology can already report dynamic machine-to-machine efficiency as this information is required to drive cooling optimization.  The smart decision is for HVAC manufacturers to get out ahead of this data, and use efficiency reporting as a differentiator and means of driving continual improvement.

Just as mandatory EPA mileage ratings and rising gas prices changed consumer buying decisions – and drove car manufacturers to offer cars with better gas mileage, more granular energy performance ratings will improve the efficiency of cooling equipment.  And this benefits all of us.