A Look at 2014

Posted on February 10, 2015 // Leave a Comment

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.

 

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Maintenance is Risky

Posted on November 13, 2014 // Leave a Comment

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.

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

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

Posted on September 10, 2014 // Leave a Comment

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.

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A Look at 2013

Posted on January 20, 2014 // Leave a Comment

We grew!

We moved!

We’ve had a heck of a year!

In 2013 alone, we reduced (and avoided the generation of) more than 85 thousand tons of carbon emissions from the atmosphere.

This is a statistic of which I am very, very proud and one that clearly demonstrates the double bottom line impact of the Vigilent solution.

We have directly impacted the planet by reducing energy requirements and CO2 emissions, even as the demands of our digital lifestyles increase.  We have impacted individual quality of life by increasing uptime reliability and contributing to the safety of treasured documents and photos, as well as helping to ensure the uninterrupted transmission of information that makes our world operate.  We are honored and privileged to contribute so directly to the well-being of our world and our customers.

While analysts have cited a DCIM market contraction in 2013, Vigilent has thrived.   We attracted new customers and engendered even deeper loyalty among existing customers – evidenced by our organic growth as one deployment turns into 3, then ten, then dozens across the United States when actual energy savings and thermal condition insights are realized.

I am pleased to share some of the milestones we achieved in 2013:

We moved to terrific new facilities in uptown Oakland.  Not only does our new facility (within a literally green building)  provide us with space for in-house product commissioning and expanded R&D,  it provides a vibrant collaborative atmosphere for employees.  The new location is adjacent to public transportation, honoring our commitment to a green corporate culture, and offers dozens of great restaurants, coffee shops and diverse entertainment options for employees.

We grew – in revenues, in customer base, into new markets and with staff.  With growth comes responsibility to provide more directed  leadership in business functions and market focus.  With this in mind, we expanded our executive management staff, hiring  Dave Hudson to oversee sales and operations worldwide, and  Alex Fielding to introduce Vigilent to federal markets and many new field engineers, software engineers, QA and support staff.

We expanded our product offering with new functionality including out-of-the-box reports that help with energy savings, SLA adherence, maintenance and capacity planning.  We continued to refine our trademark intelligence and control functionality enhancing both usability and energy savings in ever more complex data center environments – achieving an additional 30% savings in some cases.

Ultimately, all of this helps our customers succeed not only in direct bottom line impact, but with large-scale sustainability efforts that are widely recognized.  Avnet used the Vigilent system in corporate sustainability initiatives that garnered the company the Uptime Institute GEIT award, as well as recognition by InfoWorld as a top Green IT award winner.    Our sales partner, NTT Facilities, continues to roll out  Vigilent deployments in Japan.

Our ability to contribute to the Federal Government’s initiative to consolidate data centers and reduce overall energy savings is significant indeed.  Watch this space.

With a great year behind us, we recognize that there is much to do, as the data center industry – at last – is realizing how significantly data and analytics can improve day to day operations and efficiency endeavors.

The Emerson-Poneman Institute recently issued a study on Data Center outages that states accidental human error remains in the top-3 cited reasons for downtime and that 52% of survey respondents believe these accidents could have been prevented.

Intelligent software control and analytics will help operators make better,  more informed decisions and reduce such human errors.   These tools will increasingly help data centers proactively avoid trouble, while at the same time helping them diagnose and resolve actual issues more quickly.

This will be the year of analytics for data centers.  Vigilent is equipped and prepared to lead this charge, leveraging years of institutional knowledge we have gleaned  from hundreds of deployments in every conceivable configuration in mission critical facilities on four continents.  This mass of data influences the analytics we use to engage individual control decisions at every site, and also, more recently, places the benefit of this accumulated knowledge into the hands and minds of data center managers for more informed process management.

Happy New Year.

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

DCIM & ERP

Posted on August 6, 2013 // Leave a Comment

Yes, DCIM Systems ARE like ERP Systems, Critical for Both Cost and Risk Management

Technology and manufacturing companies nearly all use sophisticated ERP systems for oversight on the myriad functions that contribute to a company’s operation.  Service companies use SAP. 

Data center managers more typically use their own experience.   With all due respect to this experience, the complexity of today’s data center has long surpassed the ability of any human or even group of humans, to manage it for maximum safety and efficiency.

As data centers have come to acknowledge this fact, they are increasingly adopting DCIM, the data center’s answer to ERP.   The similarities between ERP systems and DCIM are striking.

Just as manufacturing and technology firms needed a system to manage the complexity of operations, data center operations have grown and matured to the state that such systems are now required as well.

 Data Center Knowledge’s  Jason Verge says that “… [DCIM] is being touted as the ERP for the data center; it is addressing a complicated challenge.  When a device is introduced changes or fails, it changes the make-up of these complex facilities.”

Mark Harris of Nlyte said in a related Data Center Journal article: “DCIM was envisioned to become the ERP for IT.  It was to become the enabler for the IT organization to extend and manage their span of control, much like all other organizations (Sales, Engineering, manufacturing, Finance, etc.) had adopted over the years.”

Just like ERP systems, DCIM attempts to de-silo and shed light, along with management control, on cost and waste, while also addressing risk concerns.   In initial DCIM deployments the focus has understandably been on asset management.  Understanding the equipment you have and if this equipment is appropriate for your challenges was the right place to start. However, DCIM vendors and users quickly realized that elimination of energy waste, particularly energy wasted by unused IT assets, was another useful area of focus.  Cooling as a resource or even area of waste, was a tertiary concern.  Business managers no longer have this luxury.  The cost of cooling and the risk of a cooling/heating-related data center failure is too high.  As Michelle Bailey, VP of 451 Datacenter Initiatives and Digital Infrastructure said in a recent webinar on Next Generation Data Centers, Data centers have become too big to fail.  She also said that data centers are still using imprecise measurements of accountability – which don’t match up to business goals.  Processes must be made more transparent to business managers, and that metrics must be established and tie directly back to business goals.

Data center managers can and do make extremely expensive energy-related decisions from a cost perspective in order to reduce risk.  These may not even be bad decisions.  But the issue is that, without site visibility and the transparency that Michelle suggests above, business managers don’t realize that these decisions are being made at all, or that there may be options available which, with more analysis, make more sense from a business cost and risk trade-off perspective. And, while cost is one driver of the need for management oversight, waste (and its obvious effect on cost), is another.

As an example, a facility manager may turn his chiller plant down a degree to manage his cost function and perception of risk control.  This action has the cost equivalent of expensing a Tesla, but likely has no visibility to management.  Nor, typically, does the facility manager realize that he has less expensive and even less risky alternatives, because he/she has never had to consider them.   Facility managers are not traditionally accountable to energy savings.  They are accountable to uptime.  This thinking is outdated.  The two are no longer mutually exclusive.  In fact they are inextricably tied.  Proactive and intelligently managed energy saves money and reduces downtime risk by reducing the possibility of cooling failures.  If DCIM, like an ERP system, is used to understand and manage where cost – and waste- is being generated, it must specifically address and incorporate cooling infrastructure.

DCIM systems, offering granular data center information, aggregated and analyzed for business case analysis enables such oversight and with this, improved operational management.

 

 

Filed Under: Data Centers, Energy Efficiency, Technology // Tagged: , , , , , , ,

Intelligent Efficiency

Posted on June 17, 2013 // Leave a Comment

Intelligent Efficiency, The Next New Thing.

Greentech Media’s senior editor Stephen Lacey reported that the convergence of the internet and distributed energy are contributing to a new economic paradigm for the 21st century.

Intelligent efficiency is the next new thing enabled by that paradigm, he says, in a special report  of the same name.  He also notes that this isn’t the “stale, conservation-based energy efficiency Americans often think about.”  He says that the new thinking around energy efficiency is information-driven.  It is granular. And it empowers consumers and businesses to turn energy from a cost into an asset.

I couldn’t agree more.

Consider how this contrast in thinking alone generates possibilities for resources that have been hidden or economically unavailable until now.

Conservation-based thinking or, as I think about it in data centers, “efficiency by design or replacement,” is capital intensive.  To date, this thinking has been focused on new construction, physical infrastructure change, or equipment swap-outs.  These efforts are slow and can’t take advantage of operational variations such as the time-varying costs of energy.

Intelligent energy efficiency thinking, on the other hand, leverages newly available information enabled by networked devices and wireless sensors  to make changes primarily through software.  Intelligent energy management is non-disruptive and easier to implement.  It reduces risk by offering greater transparency.   And, most importantly, it is fast.  Obstacles to the speed of implementation – and the welcome results of improved efficiency – have been removed by technology.

Intelligence is the key factor here.  You can have an efficient system, an efficient design, but if it isn’t operated effectively, it is inherently inefficient.  For example, you may deploy one perfectly efficient machine right next to another perfectly efficient machine believing that you have installed a state-of-the-art solution.  In reality, it’s more likely that these two machines are interacting and fighting with each other – at significant energy cost.   You also need to factor in and be able to track equipment degradation as well as the risks incurred by equipment swap-outs.

You need the third element – intelligence – working in tandem with efficient equipment, to make sure that the whole system works at peak level and continues to work at peak level, regardless of the operating conditions.  This information flow must be constant.  Even the newest, most perfectly optimized data centers will inevitably change.

Kudos to Greentech Media for this outstanding white paper and for highlighting how this new thinking and the” blending of real-time communications with physical systems”  is changing the game for energy efficiency.

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