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…or perhaps Sharp Greenhouse Gas Accounting with Fuzzy Measurements

I am deep into greenhouse gas (GHG) accounting and reporting requirements just now, including ISO 14064, 14065 and training at the Greenhouse Gas Institute. This a partial explanation for the period of silence on this blog. Some readers might question whether GHG accounting topics fit on Crustybytes.com.  It turns out our perceptions of GHG are information-based, and despite the fact that each of us are responsible for producing tons of GHG each year, we generally don’t see or sense them.  As I write this post, the U.S. House of Representatives has just passed the American Clean Energy and Security Act which includes Cap & Trade for carbon/CO2 equivalents (CO2e) which will have considerable impacts on business.  Finally, each of us has to decide our position on climate change and what we are willing to do about it.  All of which is to say the subject fits the Tech, Biz and Open Source Brains meme for Crustybytes.

I am particularly interested in calculating CO2e for stationary combustion sources for numerous reasons:  they are one of the largest man-made contributors to atmospheric carbon, they are relatively well-instrumented for good measurements and therefore insight, and they are well understood.  So when a training exercise presented a chance to compute CO2e emissions for a fictitious company, I got the chance to do what I enjoy — take the apparatus apart in order to understand it better.

My challenge is to understand the confidence we can expect for calculated CO2e emissions.  These results are or soon will be the basis for complying with regulations, and may become the basis for observing caps (limits) and the buying and selling of offsets and credits.

For my purposes, I narrowed my inquiry of the exercise to stationary emission sources that burn natural gas.  I will not try to go into the detail of the calculations here, but characterize them as relatively straightforward and modestly rigorous.  Understanding accuracy or confidence is a matter of understanding the variability (the ‘fuzziness’ in the subtitle) that can arise from all the bits that make up the answer — calculated CO2e emissions.  The ‘bits’ as I call them, fall into three categories: field measurements or records; constants and conversion factors; and Global Warming Potentials (GWP).

I frame this discussion to be about uncertainty, rather than the other side of the coin, accuracy. I quantify uncertainty as the amount added or subtracted from a stated value that defines a range where 95% of the time a result can be expected to fall within that range.  For statistically normal distributions of samples, this is approximately 2 standard deviations.

Sources of Uncertainty

The constants and conversion factors involved generally are derived from first principles,  can be accepted as fixed and not subject to uncertainty (without redefining our understanding of chemistry). Fuel gas measurement, fuel gas composition and percent combustion are subject to uncertainty, and factors are assigned consistent with typical measurement technologies, fuel system variabilities and equipment performance, respectively.  Uncertainty is always present in these numbers, though we are adept at ignoring it.  We are particularly prone to accepting numbers verbatim from instruments we do not understand and computers.

GWP values are highly uncertain, reported to be as high as 35%. However, by convention GHG organizations have agreed to use the same values globally — so keep them fixed.  No need for outrage, there are precedents.  We do it all the time with the electric meter or the gas pump — we ignore uncertainty and use the measurement as the basis for a business transaction.

Method

Here’s the approach:

• randomly assign a value to each of the uncertain variables (fuel gas measurement, composition, combustion fraction) within the range elected (+/- 2 standard deviations)
• record the result
• repeat the above — a 1000 times or so, 10,000 or maybe a million, shown below
• analyze the set of results, such as mean, standard deviation and for me, a graph works best.

Implications

Take a single sample at any given hour in a year (8760 hours in a year), and expect the result to fall somewhere on the curve.  Understanding the curve and the probability of landing on a specific location on it, gives quantitative confidence to regulatory reporting numbers or to sizing the margin under a cap that might be sold to others.  We do not yet have a price for a ton of CO2e in the U.S., but this week’s European price is approximately $18.84 U.S. making 1 standard deviation on the graph above worth ~$14,526 U.S. per year. So sharp greenhouse gas accounting may pay.     db

Titling this post was a challenge: The Engineer and the Mayonnaise was tempting, but I am reluctant thinking about what Googlers might be thinking if the search engines bring them here.  No matter.  The nameplate says “Tech, Biz and Open Source Brains,” and we will get around to all three here.

This post is about abundance, which is a persistent theme with regard to information technologies here at CrustyBytes.  This morning I shopped for the annual purchase of mayonnaise.  My store had 38 distinct choices of mayonnaise.  When I counted, several shoppers steered wide to put a more comfortable distance between themselves and me.  I marvel — what an incredible abundance and what a marvelous supply chain that can deliver 38 choices for that once-a-year moment when I will buy the stuff.

Human brains are wired to expect scarcity, or more accurately, to prefer behaviors that would improve our chances of survival in the face of scarcity.  So confrontation with 38 choices of mayonnaise is not something we are tuned for, evolutionarily speaking.  Choose poorly and I face a year less than delighted with my choice of mayonnaise.  I lived 4 years in Europe, where my neighborhood store and supply chain were tuned to bicycle and pedestrian patrons, and  offered 2 choices of mayonnaise.  I vividly recall repatriating to the U.S. , where the supply chain is tuned to the SUV-equipped shopper, and feeling absolutely paralyzed at the sight of the choices presented — mayonnaise and everything else.

What to do?  3 choices come to mind:

1. If I had a therapist, I could ask her for coping skills for the anxiety.  After all, I will be living with the choice for a year and what if I get it wrong? Think of the buyer’s remorse.  On the other hand, thinking one needs to talk to a therapist about mayonnaise anxiety is a sure sign of needing a therapist, and my budget is not in shape for that level of recursion.
2. I could model my dilemma in the form of an equation, and solve for it.  The years have de-tuned my write-down-and-solve-the-equation skills, but it could be done.
3. I could take some of the ubiquitous, free computing I am always talking about and run a few hundred thousand simulations in search of mayonnaise choice happiness.    …     Yep.  That’s my ticket.

Here’s how I modeled it.  First, apologies to my friends at P&G who know so much about this stuff that I am sure they know what I will pick, and why, before I stop the shopping cart.

1. 38 choices.
2. Despite my refined palate, I would be happy with some number of them.
3. I try a small number of them, and record the one I like best.
4. The question becomes, how many to sample to be reasonably sure I get one that qualifies as one of my winners.

Here’s what the results look like for the case where I imagine there are 3 which I would accept as best.

The results show I have to try at least 4 before I get a better than 50:50 chance of selecting one of the three I imagined as best for me.  After trying 9, the chances diminish smoothly.  The logic is well explained in Digital Dice, by Paul J. Nahin, in the context of dating and marriage. The stakes for mayo are much lower.

The simulation was done using Octave, an open source math package available free under the GNU General Public License. Powerful, elegant and free — yet another amazing example of the abundant information technology available to everyone.  In fact, here are the components I used:

1. Octave for a simple program to run 10,000s of simulations;  running thousands now, while writing this post, with no noticeable degradation in my computer’s performance for my typing.  In this sense, the computing processor is free for solving these simulations.
2. Octave has an installed java-based plotting package, jhandles, for generating wonderful plots.  Because it just works, you and I do not need to know this, other than to acknowledge the fine work (Michael Goffioul) and powerful results. I  subscribe to the picture-worth-a-thousand-words school of thought.
3. Picnik to edit/crop the plot output into a pleasing form.  Free and no registration required.

Here’s the real headline: the $2.34 I spent on the jar of mayonnaise is more than I spent on the vast computing and simulation capability at my fingertips to explore the whimsical dilemma of making a shopping choice in the face of abundance.  What’s more, I had to drive to the store to buy the mayo.  The simulation tools and the documentation, came to me over the internet.

We live in an amazing time of computing abundance, and have hardly begun to realise the implications or possibilities.  db

A persistent theme in these writings is the abundance of computing power available for free, or almost free.  In the Featherlight series at ExecutiveEngines, we are illustrating how much critical IT horsepower can deployed in a startup for much less than the daily cost of a fancy coffee, a latte grande. These ideas are forged from the real-world, as I have had the privilege of setting up resources for a number of startups and enterprising individuals.  One of the free apps is the standard edition of Google Apps (for your domain) including email.  Google offers a premium edition of Google Apps for currently $50/user/year, which is a bargain — but that is another discussion.

The very substantial email capability Google provides for free, comes via a value exchange, a term which we have covered before.  In return for the email, Google gets to provide the sponsored links that have become so ubiquitous with search on the right side of the web view of mail or as a short text string at the top of the inbox.  I get an abundance of reactions to this.

The issue of email privacy is the issue I want to explore here.  Anecdotally, the vast majority of the people I set this email up for don’t seem to think about it — perhaps because we have become to accustomed to the aforementioned sponsored links that appear with our routine search results.  When I explain how it works, I see a few individuals become concerned, or even alarmed.  Here’s the deal:  the links are generated by processing the message contents for keywords that advertisers buy, brokered by Google.  So the machines are looking at your message, and serving up links that might be relevant in hopes that you might click one.  The part that raises concerns is the fact other parties, silicon-based or otherwise, are reading the mail.

There is no privacy … not sure there ever was.

The discomfort arises from the notion that what you, person A, say to person B on email is thought to be private and confidential.  If you are thinking about an email at work, game over.  Chances are, the email and the contents are the company’s, not yours.

Perhaps this expectation of privacy stems from our experience with the postal service, where we inspect the condition of the envelope upon arrival, and if in good condition, we presume it has not been read by others.  Anti-tampering laws may give some comfort that law-abiding people and companies are not reading our mail.  Of course, we often throw sensitive correspondence in the trash, where it can be picked and read by motivated miscreants.

Or perhaps we inherit the notion of privacy and confidentiality from our use of the telephone.  We expect that person A and person B, if each is in a private space, can conduct a conversation in private — unless a judge has issued a court order authorizing a tap.

Both cases, postal and telephone, seem to be some comfortable status quo where we prefer not to think how brittle our privacy may be.

Your glass house

What if I told you:

• how much you paid for your house, and the size of your mortgage, is just a few free clicks away for anyone who wants to know.  Makes you wonder why there is still a taboo against discussing it at cocktail parties.
• data about items you buy, including those which are known to be hazardous to your health and insurability, for how much and when, is sold to third parties you do not know.  Your affinity or loyalty card at the grocery store secured your permission to do so, often in exchange for a few cents off on items you could have bought for the same price at the discount store.
• that you carry a microphone in your pocket, that can be remotely turned on to listen to any conversations in the room.  Again, a court order is required, but it is done.
• that the time and date your car’s transponder — you know, that EZ Tag on the windshield — passed a reader is logged and available.  My local turnpike authority was embarrassed when a simple hack was published that allowed anyone to query their system for the logs of the account of anyone else; the easy security hole was quickly closed, but they still keep your data.

All of which is to say, many aspects of our lives are no as private as you imagine and in many cases, we willingly trade convenience or a few cents for our privacy.  This is like the old joke:  ‘we have already established the vice, all we are doing now is negotiating the price.’

Your best defense

So what do you do about it?

Accept the trade, unless you have a lot of money to waste on a private email servers and maintenance.

More important, do no harm in email. Email lasts forever.  Imagine your emails will be read by your worst enemy’s attorney in public, to  your parents, grandparents and children. Sobering image.

The reckoning

It is the inconsistency that screams out.  Volunteer privacy compromises daily for pennies or convenience, then refuse free enterprise-class email services because Google wants to have their machines process your message contents for words that might trigger ad-words they sold? Think it through.  db

Since publishing the post on Realizing the Bounty of Free Computing, I have been socializing the concept with many people including business execs, venture capitalists, scientists, and a host of other smart people I have the privilege to know and who have tolerance for my ideas.  And while they are uniformly kind, it is clear that the phenomenon and the potential is not yet real to them. This week a couple of items crossed my radar screen that may reveal some insight.

Dark clouds

I heard Phil Windley and Scott Lemon interview Andre’ M. DiMino of the Shadowserver foundation on Phil’s Technometria podcast at IT Conversations. Andre’ describes the organizations efforts to track bots and botnets across the internet.  These are computers, servers numbering in the 1000s and personal computers numbering in the neighborhood of 750,000 at this writing, that are compromised with nefarious software, aka malware, that can be tasked to do the bidding of the botnet commander.  Botnets are well explained at Shadowserver.

Make no mistake, this is an example of utility computing.  Except the owners of these computers have been duped into letting their machines be conscripted to do the deeds of the commanders.  These deeds are often illegal, like SPAMming.  From previous posts, you learned I am no fan of the label Cloud Computing.  If we did apply the cloud metaphor, this use would be the dark side.

Also make no mistake, the people who create these bot networks are clever and resourceful.  And while I have argued that abundant computing is almost free, it is really free for these people.  Isn’t it interesting that the dark side provides early adopters and innovative exploits of this new resource?

Economics of SPAM

Phil Windley also mentioned a fascinating article by BBC, Study Shows How Spammers Cash In on SPAM.  The article reports a study conducted by a group of researchers at the University of California, San Diego, led by Stefan Savage.  They hijacked a subset of a bot network (yes, there is no honor…), inserted their harmless SPAM directing customers to a fake pharmacy site appearing to sell a herbal supplement for libido enhancement, and counted.  Here is the summary:

• 75,869 computers were hijacked
• over the course of 26 days, 350 million SPAM emails were relayed
• 28 user clicks resulted, that would have been sales had the harmless website processed them (they didn’t) for a total of $2,732 or an average order of $98.

The paper describing the study is here.

28 sales per 350 million emails is a yield of 0.000008% or 1 per 12.5 million emails. $105 revenue per day. Use of 75,869 computers for $2732, or the use of more than 27 computers per $1 revenue. Remember the researchers only hijacked a subset of the bot network, which they estimated to be 1.5% of the total.  The researchers estimate the full network would yield $7000 revenue per day.

Pause and reflect on how much free resource the botnet commanders exploited for modest gain.

Post office sees less mail volume, in part due to the internet

Also this week, the Postal Service reports a reduction in mail volume, in part to what Postmaster General John E. Potter explains as,  “a revolution in the way people communicate has structurally changed the way America uses the mail,”  and offered as part of the rationale for reducing some mail delivery from 6 days to 5 days per week.

This news arriving the same week as the SPAM economics above, prompted me to do some back-of-the-envelope calculations comparing the economics of a direct mail campaign versus a botnet SPAM relay campaign.  Granted, I am not including the case of a legitimate (!?) bulk email campaign.

Turns out I can’t responsibly make the comparison.  Sure, I ran the calculations but there is no rational market for a 350 million piece direct mail campaign.  Some things do jump out.

Asymmetric Costs

The cost of the message payload in the case of direct mail, the printed envelope and contents, has approached some low-cost asymptote from years of cost pressures, but is still probably in the neighborhood of $0.22.  If 350 million pieces were rational to send, the payload cost would be in the tens of millions.  The weight at 0.5 ounces per piece would imply well over 5000 tons.  Of course, the cost of the payload in the case of email is nil.

Also jumping out is the cost of transport, effectively zero in the case of SPAM.  According to the calculator at the postal service website, we can expect another $0.22 for postage per piece of direct mail.  Again, tens of millions in expense.

These wildly different costs drive wildly different behaviors.  Free or almost free means you can afford to oversend, without any regard to being selective about it.

Not so great versus terrible response rates

The direct mailers tell us to expect a 2.15% response rate for a well-designed campaign.  That’s not so great, but the business case works and lots of companies and products depend on it. I suspect the 2.15% rate holds with the typical direct mail campaign in the range of 10s of thousands of pieces, not 100s of millions.

The 0.000008% response rate reported by UCSD study is terrible, something like 250,000x worse than direct mail. The business case works too, although most classical market analysis would classify it as a niche. I like niches.  Chris Anderson would call it the far end of the long tail.

I must point out, that as much as I hear people protesting SPAM, the reason we have it is that it works.  There are a few people out there who will buy in response to it.

Implications for using the Cloud

Developing and prospering from niches is not new. Realizing the benefits of utility computing is not as far off as you may think.  Clever people exploiting the dark side of the Cloud have been doing it for years.  Check your inbox.

Time for the rest of us to step up and participate. db

Nick Carr asked a simple question on his blog:  “Are we missing the point about cloud computing?”  He goes on to share an example from Derek Gottfrid at the New York Times, where Gottfrid solved a big problem converting 4 terabytes of Times TIFF files to PDFs using Amazon’s Elastic Compute Cloud (EC2).  100 virtual computers working for something under 24 hours at a cost of $240, and out comes 11 million PDFs. I am speculating here, but I imagine Gottfrid put the $240 on his credit card.  The mission according to Gottfrid:  “The New York Times has decided to make all the public domain articles from 1851-1922 available free of charge.”  Very cool.  Read Gottfrid’s account here.

Point is, utility computing (the preferred, more descriptive label than cloud computing in this case) solved a juicy new problem, not some warmed over set of requirements well-served by Times’ current systems.

When you read Clayton Christensen’s Innovator’s Solution, you’ll find fascinating models which describe the utility of a product and they may help us select the right problems to solve with cloud computing.  Christensen expertly develops the hypothesis that any new product introduced that fits on the current trajectory – the continuum of functionality vs. utility – is subsumed by the incumbent suppliers that inhabit the curve.  Christensen goes on to assert that innovators that enter a market at some point on the curve or an expected point on its trajectory, will get crushed by the incumbents whose products already inhabit the curve.  His lessons?  Incremental and expected improvements revert to the benefit of the incumbents.  And if you want a distinctive, defensible position in a market, find an off-trajectory position that the incumbents cannot or will not attempt to serve.

Christensen’s model is extensible beyond a simple product.  If we consider “product” to be a “solution” consisting of a set of processes, people and technology, the model still holds.  Introduce a problem / potential solution into an organization that falls near the [improving] trajectory of existing processes, people and technologies within the organization, and the existing organization will handle it as it always does.  That’s inertia, and it resists disruption (Christensen’s term), the derivative of true innovation.

So what does this have to do with cloud computing?

Returning to our lessons above, cloud or utility computing applied to solutions we may reasonably have achieved with incumbent processes, people or technologies will likely not be innovative, disruptive or frankly very interesting.  They may indeed be cheaper, and the wheels of the competitive markets will turn over the next years to find some new equilibrium in a cost-driven model.  This is the evolutionary progression in computing of the last 40 years as we traverse from service bureau computing to corporate mainframe to departmental mini to personal computer to client server, and so on.

The spackling over of all the computing stuff we have now with the hyped “Cloud Computing” label is happening with abandon.  Good news in that awareness and buzz is high; bad news in that the inevitable post-hype backlash is coming.

What to do?

Choose wisely in selecting your problems and how you frame them to achieve the breakthrough advantages of utility computing.

I have no first-hand knowledge of the NY Times, but I imagine their IT and finance processes and people are top shelf.  Go to any well-run company’s IT shop and ask for 100 servers, or go to the ‘New Applications’ window and try signing up a project like Gottfrid’s.  You’ve chosen to play on the trajectory of the IT Infrastructure group, the new applications group, or insert some label from the current org chart here group.  Good luck.

Instead, pick a problem not served by some application already in the data center, one considered impossible by the professionals or better yet one they cannot or will solve for you.  Even better, try a business model with the most tricked-out computing requirements without owning any servers.  Pull out your credit card and get going.

Theme:

We are awash with a unprecedented abundance of Free* computing capacity.  The equivalent of millions of servers, hundreds of millions of PCs and who knows how many mobile phone handsets, referring to the unused capacity on machines bought, paid for and running.  See “It’s Not a Cloud.  It’s a Mine.“  In addition, we have thousands of Free Applications (FreeApps) available via Web 2.0.  We have hardly begun to realize the potential wealth — scientific, social, educational and monetary — that could be unlocked by virtue of exploiting this capacity.

Evidence of the Theme, present today:

We have rich 3d simulated space in which to:

• Collaboratively develop insight, knowledge and overcome our limited human ability to perceive scale.  Example:  Second Life Drexel Island with avatar-sized molecules rezzed to view, interact with, and experience how they dock with proteins, etc. Free.
• Collaboratively develop new insights by rezzing data in novel structures in which to interact, view and experience them.  Example:  Second Life Data Visualization;  Green Phosphor Data Visualization; and Sun’s Project Wonderland.  Free and almost Free.
• Modify physics engine to better understand interactions of materials on at nano-scale, for example.
• Modify physics engine to simulate a Mars expedition, for example.  No reason we couldn’t create a Mars mission simulation and let the 5 to 8 y.o. kids of today (Club Penguin and Webkinz digital natives) play with it, creating an impulse to the right educational track and to create an abundant pool of qualified crew candidates 20+ years from now.  Free to use.
• Eliminate geographic and language barriers for those who may have reason to come together to solve a problem, learn a concept, evaluate a medical outcome.  Free.
• Overcome physical and social barriers.  Simple example:  allow a handicapped person to experience and express some human interaction such as dance, not otherwise available.  Free.
• Amazon with EC2 and Google with App Engine, attempting to monetize directly and indirectly (new business models, perceived stock value, etc.) spare computing capacity through Utility Computing. Almost free.
• Paraphrasing Clay Shirky from Here Comes Everybody:  It is now cheaper to just try something on the web than it is to do the analysis as to whether or not it will work.  Almost free.
• Seti@Home achieved 528 TeraFLOPs via 334,155 active computers in 210 countries, as of August 2008.

Missing Pieces:

Meanwhile, I note the elements missing in order to take advantage of this great abundance of our time:

• Educated and informed practitioners who can identify the problems to solve, formulate them in a manner which allows Utility Computing and Free Apps to solve them
• Informed business leaders who understand the power of the abundant and free computing that can be brought to bear on big problems, and can most importantly frame the value propositions such that we can attract and fund talent (not so free) to solve them
• Leadership.

* I am taking license to define “Free” as any cost less than a Starbuck’s latte grande, on a per month, week or day basis (haven’t decided).

Mining for Silicon Gold

In my last post, I asked you to switch the metaphor for abundant free (at the margin) computing from clouds and Cloud Computing to mines and miners. What you find in the mine — the water, oil or computing — is valuable when you use it.  Seems obvious for water and oil.  Computing is valuable too, but takes a little more thought to get a handle on it.

Let’s consider the provider side of cloud computing, and turn to the really big mines. Google, Amazon and Facebook, for example, operate sites that are run by thousands of computers.  Business Week reports estimates that Google probably has in excess of 500,000 servers, Facebook going from 10,000 to 50,000 servers, and so on.  I speculate Amazon employs some number of servers between Facebook’s and Google’s.  These are big numbers, and while servers are cheaper than ever to buy, 100,000 of them represent a significant capital commitment.

Why so many?  So you and I get a great experience.  Take Amazon for example.  Amazon is one of the best experiences on the web, and almost all those pages are assembled just for you on the fly.  All done for the mission of creating a compelling experience in which to buy what Amazon sells.  It takes considerable horsepower to put it all together just for us, and considerable scale to deliver it to thousands, perhaps hundreds of thousands, simultaneously.  The scale has to deliver the good experience on peak shopping days like the day after Thanksgiving, or the days leading up to Christmas.  A poor experience, a slow session, or an unavailable connection directly leads to missed sales and loss of revenue for Amazon.  So Amazon has excellent, quantifiable business reasons to invest capital in scaling its computing resources.

But what of other days, like April 15th when you and I are standing in line at the post office to file our tax returns, not shopping at Amazon?  That pre-Christmas shopping capacity is idling.  The gap, the difference between the computing capacity present and paid for, and the amount actually being used by Amazon at any given time, is the free computing to which I refer.  Invoking our metaphor again, Amazon is sitting on a huge mine.

Let’s review the direct reasons why a mine owner like Amazon, might pursue becoming a cloud computing supplier.  Managers have a duty to increase enterprise market value (EMV).  I use the EMV framework so often used in calculating value of a corporation and its stock, because it is an excellent mechanism for translating seemingly arcane issues into something that executives, employees and stockholders care about.

I include a couple of levers which are not applicable for a cloud computing supplier – because I want to keep the framework intact so that we can return to it again in the future.  In any case, see Supercharging Supply Chains by Tyndall, Gopal, Partsch and Kamauff for an excellent, practical overview of EMV.

Profitable revenue growth is straightforward – selling something you have already paid for.   Fixed capital efficiency is likewise easy:  if you have $10 million or a $100 million in servers, it makes good sense to use them for all they’re worth.  If you don’t, you’re better off leaving the server money in a bank earning interest.

Tax minimization is a little more nuanced.  First, taxes take cash out of the free cash flow stream, which is a critical contributor to EMV.  A corporation like Amazon, Google, and so on, will have multiple data centers and operations in multiple locations.  Computing the likes of which we are discussing here, can be shifted reasonably from one data center to another, subject to some limits like latency (which is one of the reasons to have multiple data centers in the first place).  Some operating costs can therefore be shifted.  In a like manner, personnel with their costs to manage the computing resources can be shifted.  All other things being equal or normalized (talent, energy, risks, etc.), a company is usually better off to spend the money – that is to take the costs – in the region with the highest effective tax rates.  Why?  Greater deductible costs result in a lower tax bill, which is cash lost permanently from free cash flow, and hence detrimental to EMV.

As much as I appreciate the classical EMV levers as business drivers, I like the indirect drivers for a credible cloud computing supplier even more.  Selling, or even giving away, computing capacity makes them better!

Think about it.  Glass houses let outsiders see your stuff (silicon dioxide, the substrate for processors, is glass BTW, making ‘glass house’ a rich double entendre as we speak of Google, Amazon and similar).

Opening up one’s servers to the public requires the traditional support processes such as billing, customer service, training, communications, etc.  These are not unique to cloud computing, and a company has a reasonably good chance of being good [or bad] at these processes without regard to computing.

There are a number of processes that are distinctive to computing including design, operations, risk management, testing, performance management, governance, training, communications, help desk and security.  These are critical IT processes that challenge most companies internally.

To expose these processes to customers and the public requires solid grasp of the operations, confidence in the management processes, and commitment to make everything better.  In short, these companies have to do these things so well, that they are confident to enter competition – and that competition will make them better.

I want to stress a point which is even sharper for Cloud Computing.  The internet is largely enabled by open source software and technologies, with the ubiquitous, free and open source LAMP stack as the poster child.  These are wrought by legions of very smart, very talented developers motivated for many reasons.  Mostly volunteers, they are not employed or paid to make the software and the tech of the internet better, which they do regularly with passion.  They care about building and maintaining cool tech, and earning recognition for their personal genius.  They care about reputation and they care about each other.  They do not suffer ‘lame’ software lightly, nor will they accept marketing hype at face value.  They are fiercely independent, not quiet and not passive.  At this early stage of cloud computing, these people are the Consumers and if a company wants to engage them, the company has to ‘have game’ sufficient to earn their participation and respect.

Fortunately, at least three of the largest mines for this kind of computing, Amazon, Google and Microsoft, have the people and skills to engage successfully with the cloud computing consumers to which I refer.  I am sure there are others, I simply haven’t looked.  All are publicly traded companies, which brings an additional driver of excellence – market perception.
Computing is a core competence of these companies.  Embracing cloud computing, with the forces for excellence it brings to their internal processes and systems, makes these companies better.  — db

If you don’t already know about Cloud Computing, look now because the field is abuzz and smart people are talking excitedly about it. You’ll feel good. If you wait until the inevitable post-hype cycle crash, you won’t get as much information and you certainly won’t feel good.

Cloud Computing is a whimsical term our tech culture adopted, and we could have done better.  I understand why we landed on the cloud label, as I contributed.  You see, we all put fluffy cloud images on our slides in place of the tedium and scale of the networked computer architecture, hardware and software that is the internet.  It was all too easy to answer “It happens in the cloud,” in response to too many questions.  Our fault.

Now money is flowing into Cloud Computing and expert marketing people are busy recycling a bunch of old stuff with a Cloud Computing label.  Pity, because the predictable after-the-hype hangover will disenfranchise too many people from the merit in cloud computing.  And we desperately need their creativity to mine this untapped resource.

Obscured by the Cloud label, is an incredibly important point.  We are awash with more free but unused compute capacity than we can imagine.  I am sure that the current unused capacity far exceeds the total computing capacity, used plus unused, that existed on earth during many periods in my lifetime.

Free computing.

Our computers include servers which are the workhorses of the enterprise and the internet.  Millions of servers.   Netcraft has a survey of websites that would loosely correlate with the number of web servers (remember that many small websites like this one may be hosted on a single server, while big websites may employ thousands of web servers).  There are many hundreds of millions of personal computers and workstations, networked to the servers and each other. Forrester says there will be over a billion personal computers in operation by the end of 2008.

Stay with me.  Think about a running computer.  You bought it, powered it, cooled it and tended to it.  You spent your money.  Now that computer computes whatever you load, and in so doing is consumed some amount – say, 90% or 50% or 10%.  Then you add some additional compute load such as 1%, so the total becomes 91% or 51% or 11%.  That incremental amount doesn’t cost any more than you already spent.  At the margin, it’s free.

Now the sticklers will say, “You’re wrong, it will use a little more power and a/c.”  Technically, they’re right but it doesn’t matter.  Still free.  And the reason they are negligibly right is slightly perverse:  we are so rich with computing abundance that designers add capability to throttle back computers when not fully loaded.  Can you imagine explaining to Galileo (1564 – 1642)  or Copernicus (1473 – 1543)  that modern man is so rich with marvelous computing machines that he devises clever ways not to use them?

Calling it a Cloud does not help us think about the opportunity to use our abundant, free resource.

Let’s try another metaphor:  think about your house with a big yard, and a deep hole you dig into the ground.  You spent good money on the house, land and shovel.  At the bottom of the hole, you find water, oil or free computing.  Whether you use the water, oil or computing doesn’t change what you already spent on the house, yard and shovel. You found Computing Resources.  Mine and miner illustrate much better what’s going on with Cloud Computing, than clouds, in terms of finding and exploiting computing resources. (I realize holes where water and oil are found are wells, but ‘mines’ works too for purposes here.)

Mining for Computing Gold

(Illustration was created using Powerpoint.  Noncommercial reuse with attribution permitted.)

How big are our global computing reserves?

Before I make the calculation, you must understand this estimate is prototypical and for the purposes of illustrating the abundance.

If my numbers were to hold (i.e., not pulled out of thin air), we would have Global Computing Reserves in excess of 5 million servers and 500 million PCs.

I expect to get vigorous advice about the numbers being wrong.  And just as with global petroleum reserves, the ability to estimate the reserves does not translate into the ability to produce them all.  Unlike global petroleum reserves, we are briskly creating additional global computing reserves. By the way, I did not include mobile telephone handsets, an increasingly important and powerful set of networked computing resources.  Nor did I include the graphics cards in PCs, which are powerful computers barely used except when gaming or viewing Youtube.

So my numbers may be wrong.  Bring better numbers and your own method of making the estimate.  Even if I am over by 2 orders of magnitude, the net available computers may be between 1 and 10 million.

Hey entrepreneurs (miners), free computing!

Let’s use it.  Here is where the creativity I longed for far above is so desparately needed. - db

Like so many with a vested interest, I am bombarded by coverage about the current credit crisis.  My office television is often tuned to a financial channel such as CNBC, and positioned behind me as I normally face the computer screen.  I swivel 180 degrees from the computer to view the TV and vice versa.  Both screens deliver video content.

The crisis set up a situation where executives from a large financial services firm were on television to address the crisis, and presumably reassure us.  The TV piece went just like you would expect - they said what they had to, didn’t say what they couldn’t and got away clean to a commercial break.  The hosts where satisfied.  The lawyers had no reason to be alarmed.  In short, just what you would expect to happen, happened.  So much so, that even in a crisis where I am keenly interested in the firm’s affairs, it was easy to ignore.  Like the background music playing in the lobby.

A few moments later, I watched a video on my computers from the same financial services firm.  Same subject, same motives, same high production values for the content.  The big difference was my reaction.  I became progressively more anxious, even alarmed as the executive recited the disclaimers and reminded us that money market funds can lose money too.

OK.  Video on the TV and video served from the internet are different on the surface - size, format, quality, etc., but that would not account for my reaction.

The difference that matters relates to our expectations.  It’s how we look at the content and the people who deliver it on the internet versus how we look at content on TV that makes all the difference.

When I look at TV, I expect it to be professional in all respects:  writing, performance, production and delivery.  I expect the content to be safe and routine.  Scrubbed.  I am accustomed to the TV rhythms, and know when to tune out.

Video content on the internet is different.  The Amateur rules this domain, the land of the long tail.  Content is unrefined, often unlicensed, sometimes illegal — causing me to screen it through different mental filters.  Professional content stands out and seems peculiar somehow.  And when I do see professional content, I may be viewing it from someone who does not have a legitimate license to be showing it to me.  In summary, I am on guard.

Back to the scene in my office with the two screens, same financial organization,  and same crisis.  I know all the caveats:  read and understand the prospectus … yada, yada, yada.  But when the polished exec guy trained and conditioned for TV came to my internet, and recited the boilerplate message that I could lose money with his organization, I found myself becoming alarmed.

I was already on high alert watching internet video content after all.  And I already knew all the caveats to attach to whatever this executive said.  To recite them on the internet, caused me to ask:  ‘In this dangerous environment, why is this guy telling me I can get hurt - which I already know?’  I was having trouble tuning out what I normally tune out on TV.

Thinking about it, I concluded I was expecting a real person, an amateur like me, on my computer screen.  I was expecting a fellow internet citizen.   Someone who takes personal responsibility for what they say and do.  Thinking harder, I expect many of the same things I expect from people in my physical world community.  Driving this point home, the financial services firm is in my geographic region and if I ran into the executive at the local diner, I would be alarmed if he recited disclaimers to me in a conversation.

On TV I expect actors and news readers.  TV people deliver content that is packaged, scrubbed and delivered without necessarily being processed deeply if at all by the person delivering it.  Perhaps this is why we attach so little to anything our politicians and elected officials say on TV.  And this notion that TV content is usually not owned by the person saying it may be why conspiracy theorist is a gainful profession these days.

It is my internet and our internet.  We share responsibility for the content.

If you are an executive in a company and you want to speak to me via video on the internet, I expect you to be more real than TV requires of you.  Better educate your lawyers and public relations handlers fast.   - db

Welcome to CrustyBytes, where as the tagline suggests, we focus on the connectedness of technology, business and brains.

Despite the fact that technology and business have been around for a very long time, their interplays are relatively immature - leaving lots of opportunity for CrustyBytes to add novel ideas and commentary.  Of course, there are segments of publishing and media industries trying to do the same, making for a ‘big pond.’

Since we all have one, and it is the oldest of the three domains, ‘brains’ seem to be obvious, ubiquitous and potentially uninteresting.  Not any longer. Striking is the difference between the resource we put into building computer apps that run behind the screen, versus the effectiveness of the same apps as they play behind the users’ retinas.

Something profound has been happening over the last few years.  Information and insights into how we make decisions, behave, react, move and even think have emerged.  It is as if the brain owner’s manual not provided at birth is getting filled in and slowly revealed to us.   Now some may say:  “It was there all along, and you just weren’t looking;” or “It was always available, you just didn’t find it;” or perhaps, “You didn’t understand the [scientific or medical] language in which it was written.”   I don’t think so!

What we are seeing is neuroscience going open source.  Engineers, writers, academics, doctors outside the neuro-specialties and other amateurs are now participating.  This should be no surprise and appropriate, as we are all owners after all.  Brain science for the crowd is a rich and the expanding body of information.  My intention here is to apply these concepts to problems, situations and opportunities from business and tech, and explore the implications. That’s novel.  I find it fascinating.  Hope you agree.   - db