Does Efficiency Matter?

Currently, it seems that lots of things matter more than energy efficiency. Investments in reliability, capacity expansion and revenue protection all receive higher priority in data centers than any investment focusing on cutting operating expenses through greater efficiency.

So does this mean that efficiency really doesn’t matter? Of course efficiency matters. Lawrence Berkeley National Labs just issued a data center energy report proving just how much efficiency improvements have slowed the data center industry’s energy consumption; saving a projected 620 billion kWh between 2010 and 2020.

The investment priority disconnect occurs when people view efficiency from the too narrow perspective of cutting back.

Efficiency, in fact, has transformational power – when viewed through a different lens.

Productivity is an area ripe for improvements specifically enabled by IoT and automation. Automation’s impact on productivity often gets downplayed by employees who believe automation is the first step toward job reductions. And sure, this happens. Automation will replace some jobs. But if you have experienced and talented people working on tasks that could be automated, your operational productivity is suffering. Those employees can and should be repurposed for work that’s more valuable. And, as most datacenters run with very lean staffing, your employees are already working under enormous pressure to keep operations working perfectly and without downtime. Productivity matters here as well. Making sure your employees are working on the right, highest impact activities generates direct returns in cost, facility reliability and job satisfaction.

Outsourcing is another target. Outsourcing maintenance operations has become common practice. Yet how often are third party services monitored for efficiency? Viewing the before and after performance of a room or a piece of equipment following maintenance is telling. These details, in context with operational data, can identify where you are over-spending on maintenance contracts or where dollars can be allocated elsewhere for higher benefit.

And then there is time. Bain and Company in a 2014 Harvard Business Review article called time “your scarcest resource,” and as such is a logical target for efficiency improvement.  Here’s an example. Quite often data center staff will automatically add cooling equipment to facilities to support new or additional IT load. A quick and deeper look into the right data often reveals that the facilities can handle the additional load immediately and without new equipment. A quick data dive can save months of procurement and deployment time, while simultaneously accelerating your time to the revenue generated by the additional IT load.

Every time employees can stop or reduce time spent on a low value activity, they can achieve results in a different area, faster. Conversely, every time you free up employee time for more creative or innovative endeavors, you have an opportunity to capture competitive advantage. According to a report by KPMG as cited by the Silicon Valley Beat, the tech sector is already focused on this concept, leveraging automation and machine learning for new revenue advantages as well as efficiency improvements.

“Tech CEOs see the benefits of digital labor augmenting workforce capabilities,” said Gary Matuszak, global and U.S. chair of KPMG’s Technology, Media and Telecommunications practice.

“The increased automation and machine learning could enable new ways for tech companies to conduct business so they can add customer value, become more efficient and slash costs.”

Investments in efficiency when viewed through the lens of “cutting back” will continue to receive low priority. However, efficiency projects focusing on productivity or time to revenue will pay off with immediate top line effect. They will uncover ways to simultaneously increase return on capital, improve workforce productivity, and accelerate new sources of revenue. And that’s where you need to put your money.

Breaking Down Communication Barriers with IoT

The Internet of Things holds the unprecedented opportunity to improve the long-standing conflict between facilities, IT and sustainability managers.  Traditionally, these three silos are orthogonal, and don’t share each other’s priorities.

Data generated from more granular sensing in data centers reveals information that has traditionally been difficult to access, and not easily shared between groups.  This data can provide both an incentive and a means to work together by establishing a common source for business discussions.  This concept is becoming increasingly important.  As Bill Kleyman said in a Data Center Knowledge article projecting Data Center and Cloud Considerations for 2016: “The days of resources locked in silos are quickly coming to an end.”  We agree.  While Kleyman was referring to architecture convergence in the reference we believe his forecast applies equally forcefully to data.  Multi-group access to more comprehensive data has collaborative power.  IoT contributes to both the generation of such data and the ability to act on it, instantaneously.

Consider the following examples of how IoT operations can accelerate decision-making and collaboration between IT and Facilities.

IT Expansion Deployments

As service shifts to the network edge, or higher traffic is needed for a particular geographic region, IT is usually tasked to identify the most desired sites for these expansions.  In bigger companies, the possible sites can number 50 or more.  IT and Facilities need to quickly determine a short list.

A highly granular view of the actual (versus designed) operating cooling capacity available in each of the considered sites would greatly speed and simplify this selection.  With operating cooling capacity information readily in hand, facilities can easily create a case for the most attractive sites from a cost and time perspective, and/or create a business case for the upgrades necessary to support IT’s expansion deployments.

Data can expose previously hidden or unknowable information.  Capacity planners are provided with the right information for asset deployment in the right places, faster and with less expense.  Everyone gets what they want.

Repurposing capital assets

After airflow is balanced, and redundant or unnecessary cooling is put into standby through automated control, IT and facilities can view the real-time amount of cooling actually available in a particular area.  It becomes easy to identify rooms that have way more cooling than needed.  The surplus cooling units can be moved to a different part of the facility, or to a different site as needed.

IoT powered by smart software can thus expose inefficient capital asset allocation.  Rather than spending money on new capital assets, existing capital can be moved from one place to another.  This has huge and nearly instant financial benefits.  It also establishes a method of cooperation between the facilities team that is maintaining the cooling system and the IT team that needs to deploy additional IT assets and that is tasked with paying for additional cooling.

In both situations, data produced by IoT becomes the arbiter and the language on which the business cases can be focused.

Data essentially becomes the “neutral party.”

All stakeholders can benefit from IoT-produced data to make rational and mutually understood decisions.  As more IoT-based data becomes available, stakeholders who use it to augment their intuition will find that data’s collaborative power is profitable as well as insightful.

IOT: A Unifying Force for the Data Center

A recent McKinsey & Company Global Institute report states that that factories, including industrial facilities and data centers, will receive the lion’s share of value enabled by IoT.  That’s up to $3.7 trillion dollars of incremental value over the next ten years.   Within that focus, McKinsey states that the areas of greatest potential are optimization and predictive maintenance – things that every data center facility manager addresses on a daily basis. The report also states that Industrial IoT (combining the strength of both industry and the Internet) will accelerate global GDP per capita to a pace never seen before during the industrial and Internet revolutions.

The McKinsey study described key enablers required for the success of Industrial IoT as: software and hardware technology, interoperability, security and privacy, business organization and cultural support.  Translated into the requirements for a data center, these are: low power & inexpensive sensors, mesh connectivity, smart software to analyze and act on the data (analytics), standardization and APIs across technology stacks, interoperability across vendors, and ways to share data that retain security and privacy.

Many of these enabling factors are readily available today.  Data centers must have telemetry and communications.  If you don’t have it, you can add it in the form of mesh network sensors.  Newer data centers and equipment will have this telemetry embedded.  The data center industry already has standards that can be used to share data.  Smart software capable of aggregating, analyzing and acting on this data is also available. Security isn’t as well evolved, or understood.  As more data becomes available through the Internet of Things, the network must be secure, private and locked down.

Transitions always involve change, and sometimes challenge the tried and true ways of doing things.  In the case of industrial IoT, I really think that change is good.  Telemetry and analytics reveal previously hidden information and patterns that will help facility professionals develop even more efficient processes.  Alternately, it may help these same professionals prove to their executive management that existing processes are working very well.  The point is that to date, no one has known for sure, because the data just hasn’t been available.

The emergence of IoT in the data center is inevitable, and facility managers who embrace this change and use it to their operational advantage can turn their attention to more strategic projects.

My next blog will address how telemetry and IoT can break down the traditional conflicts between facilities, IT and sustainability managers.

Stay tuned.

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:

 

DeltaT

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

A Look at 2014

In 2014 we leveraged the significant company, market and customer expansion we achieved in 2013 to focus on strategic partnerships.  Our goal was to significantly increase our global footprint with the considerable resources and vision of these industry leaders.  We have achieved that goal and more.

Together with our long-standing partner NTT Facilities, we continue to add power and agility to complementary data center product lines managed by NTT in pan-Asia deployments.  In partnership with Schneider Electric, we are proud to announce the integration of Vigilent dynamic cooling management technology into the Cooling Optimize module of Schneider Electric’s industry-leading DCIM suite, StruxureWare for Data Centers.

Beyond the technical StruxureWare integration, Vigilent has also worked closely with Schneider Electric to train hundreds of Schneider Electric sales and field operations professionals in preparation for the worldwide roll-out of Cooling Optimize.  Schneider Electric’s faith in us has already proven well-founded as deployments are already underway across multiple continents.  With the reach of Schneider Electric’s global sales and marketing operations, their self-described “Big Green Machine,” and NTT Facilities’ expanding traction in and outside of Japan, we anticipate a banner year.

As an early adopter of machine learning, Vigilent has been recognized as a pioneer of the Internet of Things (IoT) for energy.  Data collected over seven years from hundreds of deployments continually informs and improves Vigilent system performance.  The analytics we have developed provide unprecedented visibility into data center operations and are driving the introduction of new Vigilent capabilities.

Business success aside, our positive impact on the world continues to grow.  In late 2014, we announced that Vigilent systems have reduced energy consumption by more than half a billion kilowatt hours and eliminated more than 351,000 tons of CO2 emissions.  These figures are persistent and grow with each new deployment.

We are proud to see our customers turn pilot projects into multiple deployments as the energy savings and data center operational benefits of the system prove themselves over and over again.  This organic growth is testimony to the consistency of the Vigilent product’s operation in widely varying mission critical environments.

Stay tuned to watch this process repeat itself as we add new Fortune 50 logos to our customer base in 2015.  We applaud the growing sophistication of the data center industry as it struggles with the dual challenges of explosive growth and environmental stewardship and remain thankful for our part in that process.

 

Data Center Capacity Planning – Why Keep Guessing?

Capacity management involves decisions about space, power, and cooling.

Space is the easiest. You can assess it by inspection.

Power is also fairly easy. The capacity of a circuit is knowable. It never changes. The load on a circuit is easy to measure.

Cooling is the hardest. The capacity of cooling equipment changes with time. Capacity depends on how the equipment is operated, and it degrades over time. Even harder is the fact that cooling is distributed. Heat and air follow the paths of least resistance and don’t always go where you would expect. For these reasons and more, mission-critical facilities are designed for and built with far more cooling capacity than they need. And yet many operators add even more cooling each time there is a move, add, or change to IT equipment, because that’s been a safer bet than guessing wrong.

Here is a situation we frequently observe:

Operations will receive frequent requests to add or change IT loads as a normal course of business.  In large or multi-site facilities, these requests may occur daily.  Let’s say that operations receives a request to add 50 kW to a particular room.  Operations will typically add 70 kW of new cooling.

This provisioning is calculated assuming a full load for each server, with the full load being determined from server nameplate data.  In reality, it’s highly unlikely that all cabinets in a room will be fully loaded, and it is equally unlikely that the server will ever require its nameplate power.  And remember, the room was originally designed with excess cooling capacity.  When you add even more cooling to these rooms, you have escalated over-provisioning.  Capital and energy are wasted.

We find that cooling utilization is typically 35 to 40%, which leaves plenty of excess capacity for IT equipment expansions.  We also find that in 5-10% of situations, equipment performance and capacity has degraded to the point where cooling redundancy is compromised.  In these cases, maintenance becomes difficult and there is a greater risk of IT failure due to a thermal event. So, it’s important to know how a room is running before adding cooling.  But it isn’t always easy to tell if cooling units are not performing as designed and specified.

How can operations managers make more cost effective – and safe – planning decisions?  Analytics.

Analytics using real-time data provides managers with the insight to determine whether or not cooling infrastructure can handle a change or expansion to IT equipment, and to manage these changes while minimizing risk.  Specifically, analytics can quantify actual cooling capacity, expose equipment degradation, and reveal where there is more or less cooling reserve in a room for optimal placement of physical and virtual IT assets.

Consider the following analytics-driven capacity report.  Continually updated by a sensor network, the report clearly displays exactly where capacity is available and where it is not.  With this data alone, you can determine where capacity exists and where you can safely and immediately add capacity with no CapEx investment.  And, in those situations where you do need to add additional cooling, it will predict with high confidence what you need. (click on the image for a full-size version)

Cooling Capacity

Yet you can go deeper still.  By pairing the capacity report with a cooling reserve map (below), you can determine where you can safely place additional load in the desired room.  You can also see where you should locate your most critical assets and, when you need that new air conditioner, and where you should place it.

(click on the image for a full size version)thermalcircle

Using these reports, operations can:

  • avoid the CapEx cost of more cooling every time IT equipment is added;
  • avoid the risk of cooling construction in production data rooms when it is often not needed;
  • avoid the delayed time to revenue from adding cooling to a facility that doesn’t need it.

In addition, analytics used in this way avoids unnecessary energy and maintenance OpEx costs.

Stop guessing and start practicing the art of avoidance with analytics.

 

 

Does Efficiency Matter?

Currently, it seems that lots of things matter more than energy efficiency. Investments in reliability, capacity expansion and revenue protection all receive higher priority in data centers than any investment focusing on cutting operating expenses … [Read more]

Breaking Down Communication Barriers with IoT

The Internet of Things holds the unprecedented opportunity to improve the long-standing conflict between facilities, IT and sustainability managers.  Traditionally, these three silos are orthogonal, and don’t share each other’s priorities. Data … [Read more]

IOT: A Unifying Force for the Data Center

A recent McKinsey & Company Global Institute report states that that factories, including industrial facilities and data centers, will receive the lion’s share of value enabled by IoT.  That’s up to $3.7 trillion dollars of incremental value over … [Read more]

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 … [Read more]

A Look at 2014

In 2014 we leveraged the significant company, market and customer expansion we achieved in 2013 to focus on strategic partnerships.  Our goal was to significantly increase our global footprint with the considerable resources and vision of these … [Read more]

Data Center Capacity Planning – Why Keep Guessing?

Capacity management involves decisions about space, power, and cooling. Space is the easiest. You can assess it by inspection. Power is also fairly easy. The capacity of a circuit is knowable. It never changes. The load on a circuit is easy to … [Read more]