I have read this article in the economist about the economics of sharing. I also read this paper by a Yale Law professor referenced in the article.
The internet has made essentially free the "cost-of-distribution" of accessing extra computing power for things as SETI@Home and file sharing networks. In these cases the resource being distributed is essentially free. In contrast, an open source developer must dedicate substantial time to his work. The article poses the question: why would a developer freely distribute open source works? Developing trust and reciprocity, as well as gaining respect of the programming community are offered as reasons. This is true, but I think a major reason why programmers release open source code is because they enjoy the products of their coding and they wish to share the fruits of their labor with others. Open source software is like the grafitti murals of the software industry.
The essay on sharing is by Yochai Benkler of the Yale Law School. Benkler argues that sharing is a new mode of economic production using distributed computing and carpooling as case studies. Carpooling and distributed computed are similar in that they are very wide-scale sharing practice of a privately-owned good, whose excess capacity has established market models. Benkler writes: "In these characteristics carpooling and distributed computing are like peer-to-peer networks, or ad hoc wireless mesh networks, or like the labor individual programmers put into free software."
Benkler uses the notion of "bumpiness" to define the quantization of capactiy. A car is often designed to accomodate multiple passangers and thus generally has excess capactiy. Similarly computers have been designed to operate extremely quickly for media intensive applications, but are often used simply for word processing. In both domains, cars or computers, there is an excess capacity due to the quanitization of the supplied good.
There are established market models to capitalize on quantization inefficiencies. Bus systems meet transportation demand and data centers meet computational demands. In both of these models we consolidate multiple subsystems in order to achieve high utilization. The added efficiency can often present an economic advantage to the bus rider or data-center customer alike. Another relevant example of consolidation and sharing reducing quantization induced capacity innefficiencies are fabless semiconductor companies that eliminate extremely large capital expenditures by outsourcing fabrication.
A great way to identify opportunities for business innovation is by identifying such quantization inefficiencies. In order to identify if there is a market for resource sharing, we must consider the model of both the consumer and the requisite material providers. How must our consumers adapt to apply our innovation? How much dependency is there on prerequisite component providers? In the case of fabless semiconductor industry these may be more specific: how must the engineer specify the IC design? Can fabrication facilities be made "exchangeable?" If there is a variance between fabrication facilities, can we measure tradeoffs affecting the design. For example suppose we may decide between using a 90nm and a 65nm fabrication facility to implement an HDL specification. If the resulting 65 nm chip would have higher unit costs at the anticipated volume level, yet run faster and consume less dynamic power such that, it may be sold at a higher price. Depending on cost analyses, we may need to make a decision between a variety of different options.
The very same decision must be made by airline consumers who must choose between faster or more convenient, but also more expensive flights. If time is a strong constraint, as it may be for a person flying on a tight schedule, then the person may be willing or required to pay a premium price for a particularly convenient flight. An airline must optimize its flight scheduling to make sure each different type of airplane is maximally shared in order to maximize profit. The pricing and scheduling model for an airline company reflects demand for different flights. Jet Blue has been able to dramatically reduce operating costs by managing a homogenous fleet. By reducing operating costs and serving high demand markets they can reduce prices to maximize quantity sold and thus maximize profit. Yet clearly there is a market space for an alternative to Jet Blue, and we will not want to use a passanger ailrliner to ship cargo. Similarly some degree of heterogeneity will maximize the efficiency computing array. Could the management costs of a heterogenous computing array present an overhead such that we would prefer to view computational resources as homogeneous clusters?
In order to have an effective process scheduling system for a heterogenous reconfigurable computer, processes must share resources with varying costs and constraints. In a heterogeneous array, there may be x86s, GPUs, FPGAs, which overlap in functionality, but are optimal for specific instances, just as there are planes, buses, cars, bikes, and feet which can all get you there. By incentivizing "car pooling," we may find ways to maximize efficiency. It's not even a distant a metaphor to suggest that shared resources should get to travel in the fast lane.
An algorithm to optimize computational resource sharing should inherit semantics and structure from a general economic model. Such an economic model would provide a method for multiple locally optimizing (greedy) process scheduling agents to globally optimize the system.
Examining strategies derived from athropological and economic bases provides a good perspective for exploring complex multi-agent management models. Benkler's essay presents a case study on "dynamic ad-hoc carpooling." An example relevant to security, is that a women is much less likely to carpool with two men both of whom she does not know (page 286 of the law journal). Thus "security" and "trust" are relevant motivational factors for making a sharing decision. That same women would probably not have any objection to sharing an airplane regardless of the gender ratio. This is based on a trust relationship with the service provider: each agent sharing the bus has been authenticated prior to enterring the shared arrangement. In a shared, distributed computer system, methods of autheticating agents and forming trust relationships between agents must be established to guarantee some level of security.
Security issues related to technological innovations are usually not unbridgeable gaps in the system design, but rather psychological gaps. Such situations similarly require that the client and vendor have a means of forming a trust relationship or autheticating eachother through other relationships. There may be a psychological barrier to convince someone to outsource their data-center due to security concerns, yet Google mass appeal demonstrates that trust relationships can develop between a user and a comptuing service provider.
Distributed computing networks present two separate security issues on the requisitie provider and the computation consumer. Suppose I were to sell a service using spare cycles for distributed analytics. I buy the spare computing cycles from companies A, B, C, D and E, and use them to perform distributed analytics for company A. I would have to be able to convince A that its analysis will be secure, and not revealed to B through E. Similarly I would have to convince B through E that their internal network is secure when they are serviceing requests from A. This problem is a psychological problem rather than an inherent limitation of the computing system, since all of the computers invovled can be securely consolidated to provide A through E with their computation requirements already. It is likely that this will be the most major limiting factor to utility computing.
Finally, on the economics of sharing and open source programming. During my experience at Xilinx, I got a better understanding of the motivation for closed source tools. It has everything to do with dependency and part lock-in, less to do with support and only psychologically related to security (one of the great one-liner that I took out of DEFCON this year was "The system is secure because it is only accessible by our proprietary readers"). Today I read a thread from 2000 on comp.arch.fpga about FPGA openness. The FPGA hardware market is hindering it's own growth by each manufacturer structuring it's toolset around closed specifications. There is an enormous waste of potential with the goal of disincentivizing interchangeability. This is contrary to the natural tendancy towards commoditization of computing components. Xilinx has been enjoy high margins and even with respectably high volume, though I fully believe they can trigger an increase volume by a factor of 5 without causing margins to drop by more than 1/5. This argument is clearly hand-waving, but the point is that they could be able to take on new customers without losing anything substantial. The first FPGA company (there aren't many to choose from) to open critical parts of their system and toolset will see an appreciable increase in their computing market share because they will be more easily adopted as accelerator co-processors. This may in fact be a case where 1024 chickens (open source programmers) would plow the field faster if only the two strong oxens (Xilinx and Altera) would get their proprietary formats out of the way.
With or without Xilinx, the market for accelerator co-processors will continue to grow as consolidated heterogeneous data-centers become more and more commonplace. I'm willing to bet the next seven years of my life to it.