2016 and Looking Forward

Posted on December 22, 2016 // Leave a Comment

2016-imageTo date, Vigilent has saved more than 1 billion kilowatt hours of energy, delivering $100 million in savings to our customers.  This also means we reduced the amount of CO2 released into the atmosphere by over 700,000 metric tons, equivalent to not acquiring and burning almost 4000 railcars of coal.  This matters because climate change is real.

Earlier this year, Vigilent announced its support for the Low-Carbon USA initiative, a consortium of leading businesses across the United States that support the Paris Climate Accord with the goal of reducing global temperature rise to well below 2 degrees Celsius.  Conservation plays its part, but innovation driving efficiency and renewable power creation will make the real difference.  Vigilent and its employees are fiercely proud to be making a tangible difference every day with the work that we do.

Beyond this remarkable energy savings milestone, I am very proud of the market recognition Vigilent achieved this year.  Bloomberg recognized Vigilent as a “New Energy Pioneer.”  Fierce Innovation named Vigilent the Best in Show:  Green Application & Data Centers (telecom category.)

Of equal significance, Vigilent has become broadly recognized as a leader in the emerging field of industrial IoT.  With our early start in this industry, integrating sensors and machine learning for measurable advantage long before they ever became a “thing,” Vigilent has demonstrated significant market traction with concrete results.  The industry has recognized Vigilent’s IoT achievements with the following awards this year:

TiE50                    Top Startup: IoT

IoT Innovator     Best Product: Commercial and Industrial Software

We introduced Vigilent prescriptive analytics this summer with shocking results, and I say that in a good way.  Our customers have uniformly received insights that surprised them.  These insights have ranged from unrealized capacity to failing equipment in critical areas.  The analytics are also helping customers meet SLA requirements with virtually no extra work and to identify areas ranging out of compliance, enabling facility operators to quickly resolve issues as soon as a temperature goes beyond a specified threshold.

Vigilent dynamic cooling management systems are actively used in the world’s largest colos and telcos, and in Fortune 500 companies spanning the globe.  We have expanded relationships with long-term partners’ NTT Facilities and Schneider Electric, who have introduced Vigilent to new regions such as Latin America and Greater Asia.  We signed a North America-focused partnership with Siemens, which leverages Siemens Demand Flow and the Vigilent system to optimize efficiency and manage data center challenges across the white space and chiller plant. We are very pleased that the world’s leading data center infrastructure and service vendors have chosen to include Vigilent in their solution portfolio.

We thank you, our friends, customers and partners, for your continued support and look forward to another breakout year as we help the businesses of the world manage energy use intelligently and combat climate change.

 

Filed Under: Energy Efficiency // Tagged: , , , , , , , , , , , , , , , , , ,

The Fastest Route to Using Data Analysis in Data Center Operations

Posted on November 2, 2016 // Leave a Comment

voltThe transition to data-driven operations within data centers is inevitable.  In fact, it has already begun.

With this in mind, my last blog questioned why data centers still resist data use, surmising that because data use doesn’t fall within traditional roles and training, third parties – and new tools – will be needed to help with the transition. “Retrofitting” existing personnel, at least in the short term, is unrealistic.  And time matters.

Consider the example of my Chevy Volt.  The Volt illustrates just how quickly a traditional industry can be caught flat-footed in a time of transition, opening opportunities for others to seize market share. The Volt is as much a rolling mass of interconnected computers as it is a car. It has 10 million lines of code. 10 million!  That’s more than a F-22 Raptor, the most advanced fighter plane on earth.

The Volt of course, needs regular service just like any car.  While car manufacturers were clearly pivoting toward complex software-driven engines, car dealerships were still staffed with engine mechanics, albeit highly skilled mechanics.  During my service experience, the dealership had one guy trained and equipped to diagnose and tune the Volt.  One guy.  Volts were and are selling like crazy.  And when that guy was on vacation, I had to wait.

So, the inevitable happened.  Third party service shops, which were fully staffed with digitally-savvy technicians specifically trained in electric vehicle maintenance, quickly gained business.  Those shops employed mechanics, but the car diagnostics were performed by technology experts who could provide the mechanics with very specific guidance from the car’s data.  In addition, I had direct access to detail about the operation of my car from monthly reports delivered by OnStar, enabling me to make more informed driving, maintenance and purchase decisions.

Most dealerships weren’t prepared for the rapid shift from servicing mechanical systems to servicing computerized systems.  Referencing my own experience, the independent service shop that had been servicing my other, older car, very quickly transitioned to service all kinds of electric service vehicles.  Their agility in adjusting to new market conditions brought them a whole new set of service opportunities.  The Chevy dealership, on the other hand, created a service vacuum that opened business for others.

The lesson here is to transition rapidly to new market conditions.  Oftentimes, using external resources is the fastest way to transition to a new skillset without taking your eye off operations, without making a giant investment, and while creating a path to incorporating these skills into your standard operating procedures over time. 

During transitions, and as your facility faces learning curve challenges, it makes sense to turn to resources that have the expertise and the tools at hand.  Because external expert resources work with multiple companies, they also bring the benefit of collective perspective, which can be brought to bear on many different types of situations.

In an outsourced model, and specifically in the case of data analytics services, highly experienced and focused data specialists can be responsible for collecting, reviewing and regularly reporting back to facility managers on trends, exceptions, actions to take and potentially developing issues.  These specialists augment the facility manager’s ability to steer his or her data centers through a transition to more software and data intensive systems, without the time hit or distraction of engaging a new set of skills.  Also, as familiarity with using data evolves, the third party can train data center personnel, providing operators with direct access to data and indicative metrics in the short term, while creating a foundation for the eventual onboarding of data analysis operations.  

Data analysis won’t displace existing data center personnel.  It is an additional and critical function that can be supported internally or externally.  Avoiding the use of data to improve data center operations is career-limiting.  Until data analysis skills and tools are embedded within day-to-day operations, hiring a data analysis service can provide immediate relief and help your team transition to adopt these skills over time.  

Filed Under: Buildings, Cooling Management, Data Analytics, Data Centers, DCIM, Energy Efficiency // Tagged: , , , , , , , , , , , , , ,

Why Don’t Data Centers Use Data?

Posted on September 12, 2016 // Leave a Comment

Data analysis doesn’t readily fall into the typical data center operator’s job description.   That fact, and the traditional hands-on focus of those operators, isn’t likely to change soon.

But turning a blind eye or ignoring the floodgate of data now available to data centers through IoT technology, sensors and cloud-based analytics is no longer tenable.  While the data impact of IoT has yet to be truly realized, most data centers have already become too complex to be managed manually.

What’s needed is a new role entirely, one with dotted line/cross-functional responsibility to operations, energy, sustainability and planning teams.

Consider this.  The aircraft industry has historically been driven by design, mechanical and engineering teams.  Yet General Electric aircraft engines, as an example, throw off terabytes of data on every single flight.  This massive quantity of data isn’t managed by these traditional teams.  It’s managed by data analysts who continually monitor this information to assess safety and performance, and update the traditional teams who can take any necessary actions.

Like aircraft, data centers are complex systems.  Why aren’t they operated in the same data-driven way given that the data is available today?

Data center operators aren’t trained in data analysis nor can they be expected to take it on.  The new data analyst role requires an understanding and mastery of an entirely different set of tools.  It requires domain-specific knowledge so that incoming information can be intelligently monitored and triaged to determine what constitutes a red flag event, versus something that could be addressed during normal work hours to improve reliability or reduce energy costs.

It’s increasingly clear that managing solely through experience and physical oversight is no longer best practice and will no longer keep pace with the increasing complexity of modern data centers.  Planning or modeling based only on current conditions – or a moment in time –  is also not sufficient.  The rate of change, both planned and unplanned, is too great.  Data, like data centers, is fluid and multidimensional. 

Beyond the undeniable necessity of incorporating data into day-to-day operations to manage operational complexity, data analysis provides significant value-added benefit by revealing cost savings and revenue generating opportunities in energy use, capacity and risk avoidance.  It’s time to build this competency into data center operations.

Filed Under: Energy Efficiency // Tagged: , , , , , , , , , , , , , , , , , ,

Does Efficiency Matter?

Posted on July 19, 2016 // Leave a Comment

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.

Filed Under: Data Centers, DCIM, Energy Efficiency, Predictive Analytics // Tagged: , , , , , , , , , , , , , ,

Analytics in Action for Data Center Cooling

Posted on May 28, 2015 // Leave a Comment

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

Filed Under: Buildings, Cooling Management, Data Centers, DCIM, Energy Efficiency, Predictive Analytics, Technology // Tagged: , , , , , , , , , , , , , , , , , , , ,

Data Center Capacity Planning – Why Keep Guessing?

Posted on January 16, 2015 // Leave a Comment

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.

 

 

Filed Under: Data Centers, DCIM, Energy Efficiency, Predictive Analytics // Tagged: , , , , , , , , , , , , , , , , , ,
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