Comments 2 This is my first Cloudonomics. Links to the papers and supporting simulations at ComplexModels. Technology convergence in hardware, e. The correct way to look at the problem, however, is not unit cost, but total cost.

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My analysis examines the conditions under which dedicated capacity, on-demand capacity, or a hybrid of the two are lowest cost. The analysis applies not just to cloud computing, but also to similar decisions, e. To jump right to the punchline s , a pay-per-use solution obviously makes sense if the unit cost of cloud services is lower than dedicated, owned capacity. And, in many cases, clouds provide this cost advantage.

Counterintuitively, though, a pure cloud solution also makes sense even if its unit cost is higher, as long as the peak-to-average ratio of the demand curve is higher than the cost differential between on-demand and dedicated capacity. In other words, even if cloud services cost, say, twice as much, a pure cloud solution makes sense for those demand curves where the peak-to-average ratio is two-to-one or higher. This is very often the case across a variety of industries.

The reason for this is that the fixed capacity dedicated solution must be built to peak, whereas the cost of the on-demand pay-per-use solution is proportional to the average. Specifically, if the percentage of time spent at peak is less than the inverse of the utility premium, using a cloud or other pay-per-use utility for at least part of the solution makes sense. For example, even if the cost of cloud services were, say, four times as much as owned capacity, they still make sense as part of the solution if peak demand only occurs one-quarter of the time or less.

In practice, this means that cloud services should be widely adopted, since absolute peaks rarely last that long. For example, today, Cyber Monday, represents peak demand for many etailers. While retailers experience most of their business during one month of the year, there are busy days and slow days even during those peaks. I look at the optimal cost solutions between dedicated capacity, which is paid for whether it is used or not, and pay-per-use utilities.

My assumptions for this analysis are that pay-per-use capacity is 1 paid for when used and not paid for when not used; 2 the cost for such capacity does not depend on the time of request or use; 3 the unit cost for on-demand or dedicated capacity does not depend on the quantity of resources requested; 4 there are no additional relevant costs needed for the analysis; 5 all demand must be served without delay.

These are assumptions which may or may not correspond to reality. For example, with respect to assumption 1 , most pay-per-use pricing mechanisms offered today are pure.

However, in many domains there are membership fees, non-refundable deposits, option fees, or reservation fees where one may end up paying even if the capacity is not used. Assumption 2 may not hold due to the time value of money, or to the extent that dynamic pricing exists in the industry under consideration. Finally, assumption 5 actually says two things.

Serving all demand makes sense, because presumably the cost to serve the demand is greatly exceeded by the revenue or value of serving it. Otherwise, the lowest cost solution is zero dedicated and zero utility resources; in other words, just shut down the business. In some cases we can defer demand, e. Let the unit cost per unit time of fixed capacity be C, and let U be the utility premium.

By utility premium, I mean the multiplier for utility pay-per-use capacity vs. As stated above, this assumption may not be valid in all cases. Even under these unit cost assumptions, a pure utility or hybrid solution may be less expensive in terms of total cost, as we shall see.

Thanks to assumption 2 , we can rearrange the demand curve to be monotonically non-decreasing, i. In practical terms, this means that, for a site supporting special events, like concert or movie ticket sales, if they have a peak during 3 days each month, we can just treat it as if this peak occurred for 36 days at the end of the year. In the real world, such an assumption may not be the case.

Continuously growing, or at least non-decreasing, demand may be suitable for resourcing via fixed capacity. Finally, it should be noted that thanks to assumptions 2 and 3 , the cost of providing utility capacity to meet the demand D is just the utility premium U times the base cost C times the arithmetic mean A times the duration of time T.

Colloquially, the cloud total cost is advantaged due to only paying for resources when needed, as well as paying less for those resources when used.

Of course, this assumes that your demand is predictable and that there is no financial risk, neither of which is typically the case. Even if you believed this to be true, all other things being equal, you might prefer the cloud solution due to demand forecasting risk and due to financial risk, e. In other words, as I point out in my first law of Cloudonomics , even if a utility costs more on a unit cost basis , the total cost can be lower than a dedicated solution, because of the savings when resources are not needed due to variations in demand.

If one needs a car for only a few days, it makes sense to rent it, even though the rate might be, say, fifty dollars a day. And if one needs a car for only a few minutes, it makes sense to grab a taxi, even though paying a dollar a minute works out to an equivalent rate of over a thousand dollars per day.

Let us define the total duration of the peak of the demand D to be Tp. That is, even if there are multiple periods when D is at peak, we sum them up to get Tp. This turns out to be an important criterion for determining the value of hybrid clouds.

To find an optimal solution we would need to know more about the characteristics of the underlying demand curve, as we shall see below. Conversely, let us define the total duration of non-zero demand to be TNZ. That is, even if there are multiple periods when D is greater than zero, we sum up their durations to get TNZ. This turns out to be an important criterion for determining when a hybrid architecture beats a pure cloud. Proposition 6: If the utility premium is greater than unity and the percentage duration of non-zero demand is greater than the inverse of the utility premium, i.

Proof: This proof is the mirror image of the prior one. In other words, if there is usually some baseline demand and utilities are somewhat costly, you may as well serve the typical baseline demand with the cheaper dedicated resources and save the on-demand resources for the variable portion of the demand.

For that, we will solve a specific example first, then argue for the general condition. The total cost of the solution is then the sum of the fixed cost plus the on-demand cost.

The variable cost is based on the size of the triangle, which has height V. To solve this, it helps to remember that the derivative of a constant is zero, the derivative of a sum is the sum of the derivatives as long as they exist , the derivative of xn is nxn-1, and the derivative of a constant times a function is the constant times the derivative of the function.

In other words, for uniformly distributed demand, the percentage of resources that should be on-demand is the inverse of the utility premium. If there is no premium, all resources should be on-demand, if the utility premium is 2, half the resources should be on-demand, if the utility premium is 4, a quarter of the resources should be on-demand, and so forth.

It turns out that this points the way to finding an optimal hybrid solution for any demand curve. These last few propositions show the value of hybrid resourcing strategies. If there is a short enough period of peak demand, rather than use only dedicated resources it makes sense to slice at least that out of the total solution and use on-demand pay-per-use resources to serve it.

On the other hand, if there is a long enough duration of non-zero demand, you may as well use dedicated resources to serve that baseline. So, these are the criteria for determining when pure clouds, pure dedicated solutions, or hybrid dedicated and pay-per-use solutions may be cost-optimal. The analysis above is oversimplified, since it assumes that there are no additional marginal costs for hybrid solutions.

Joe Weinman is employed by a large telecommunications and cloud services company. The views expressed herein are his own, and not necessarily the views of his employer. C Joe Weinman Share this:.



Description Details The ultimate guide to assessing and exploiting the customer value and revenue potential of the Cloud A new business model is sweeping the world—the Cloud. And, as with any new technology, there is a great deal of fear, uncertainty, and doubt surrounding cloud computing. Cloudonomics radically upends the conventional wisdom, clearly explains the underlying principles and illustrates through understandable examples how Cloud computing can create compelling value—whether you are a customer, a provider, a strategist, or an investor. Cloudonomics covers everything you need to consider for the delivery of business solutions, opportunities, and customer satisfaction through the Cloud, so you can understand it—and put it to work for your business. Cloudonomics also delivers insight into when to avoid the cloud, and why. Cloudonomics provides deep insights into the business value of the Cloud for executives, practitioners, and strategists in virtually any industry—not just technology executives but also those in the marketing, operations, economics, venture capital, and financial fields.


And,as with any new technology, there is a great deal of fear,uncertainty, and doubt surrounding cloud computing. Cloudonomics radically upends the conventional wisdom,clearly explains the underlying principles and illustrates throughunderstandable examples how Cloud computing can create compellingvalue--whether you are a customer, a provider, a strategist,or an investor. Cloudonomics covers everything you need toconsider for the delivery of business solutions, opportunities, andcustomer satisfaction through the Cloud, so you can understandit--and put it to work for your business. Cloudonomicsalso delivers insight into when to avoid the cloud, and why.


Cloudonomics: The Business Value of Cloud Computing, + Website

This is largely due to the position of the cloud at the nexus of macro trends such as social media, the Internet, Web 2. Cloudonomics—a term and discipline founded by the author Weinman, —seeks to provide a rigorous foundation based on calculus, statistics, trigonometry, system dynamics, economics, and computational complexity theory, which can be used to interpret empirical results. We will provide an overview of these results together with references to more detailed analyses. Perhaps the most widely accepted is the one developed by the National Institute of Standards and Technology, now stable at version 15 Mell and Grance, : Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources e. This cloud model promotes availability and is composed of five essential characteristics: …on-demand, self service … broad network access … resource pooling … rapid elasticity…[and] measured service.

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