This is a section of a working paper for the MIT Sloan School of Management. I am interested in further discussions with anyone who has an interest in the dynamics of pricing utility computing services.
Key to the value proposition offered by utility computing is the ability to share resources across multiple users. If the peak computing requirements of a customer are not correlated, then the total peak demand will smooth out. In G.A. Paleologo’s paper, he illustrates the phenomena with a simple example. Imagine a customer whose demand oscillates between 5 and 10 service units per day, with an average of 7.5. If the customer were to build their own computing system, they would need 10 service units per day to meet the peak demand. The average utilization of the system is 7.5/10, or 75%. If there are 8 customers running the same system with the same demand profile, a utility can aggregate them. The total demand would be smoothed, and the capacity required would be 66, to serve an average demand of 60. The average utilization is 60/66, or 91%, a 16% gain. (Paleologo, 2004)
Businesses with Variability: In modeling businesses that do have variations, there will be gamut of profiles. Obviously, a relatively established business will differ from a new business (for example, venture backed startup). In stochastic modeling, the potential large variation in demand that goes hand-in-hand with some of the dynamics of the Internet must be taken into account.
Constant Demand Profiles: A substantial portion of the demand for computing services will have constant demand profiles without the peaks and valleys of typical business users. These users include bioinformatics processing and other scientific simulations that will run around the clock. Sellers of utility computing services would do well to segment these customers out and offer them lower prices. First off, since their demand is easily forecast and stable, the value proposition of being able to smooth out demand peaks does not apply to these customers. As such, their willingness to pay should be lower per unit of computing power. They will still benefits shorter time to deployment, disaster recovery and other benefits of utility computing. These customers offer a significant potential advantage for utility computing vendors – they will provide consistent utilization at lower demand times of the day and year. In many cases, they may be able to shift their usage to maximize capacity utilization (for example, some customers may need X units per day performed and are indifferent to whether that is one processing node running all day or a number of them running in parallel during off peak hours.
Pricing for Utility Computing
Pricing for utility computing services will be challenging in a number of ways, particularly as the service matures. In traditional pricing models, demand is forecast, the cost of meeting that demand at an optimal level is gathered, and a certain markup is applied. (Hall and Hitch, 1939; Paleologo, 2004) Demand will initially be difficult to forecast, and it will take times for economies of scale and demand smoothing from having large quantities of customers to be realized.
G.A. Paleologo suggests a pricing-at-risk methodology. This model leverages stochastic modeling of the uncertain parameters involved in forecasting demand, utilization and adoption. Such a model would allow for a best and worst case scenario and run optimization models for the scenarios in between. The result will be a probability curve. Varying the price as the independent variable and using Net Present Value as the dependent gives a picture of how various scenarios would play out. The tricky part of this is that the elasticity of demand is not well understood. Paleologo’s model assumes a monopoly situation – we know that this will not be the case in the utility computing space.