A Pricing Mechanism for Digital Content Distribution Over Peer-to ...

Proceedings of the 38th Hawaii International Conference on System Sciences - 2005

A Pricing Mechanism for Digital Content Distribution Over Peer-to-Peer Networks Karl R. Lang and Roumen Vragov Department of Statistics and Computer Information Systems Zicklin School of Business Baruch College, City University of New York (CUNY) New York City, NY 10010, USA {karl_lang, roumen_vragov}@baruch.cuny.edu

Abstract This paper describes a usage-based pricing scheme for distributing digital content over peer-to-peer networks that rewards peer users who actively participate in the distribution process. We present a dynamic distribution model that is used to compare centralized, client-server distribuition with peer-to-peer network distribution. The conventional problem of free-riding in peer-to-peer networks is eliminated or vastly reduced. The participation incentive creates effective peer-to-peer network communities and leads to faster content distribution than in equivalent client-server settings while retaining the same profit level.

1. Introduction As the media industry has begun shifting its operations from physical to digital distribution models to deliver its content products to consumer markets, it faces the question whether to adopt a centralized or a decentralized solution. The former approach will typically be implemented as some kind of client-server system where content provision and customer relationships are managed centrally. Decentralized distribution, on the other hand, might be most effective when implemented over peer-to-peer (P2P) networks where consumers partake in the distribution process by trading information and content files [1]. For media companies, however, adopting P2P distribution means trading-off control over content supply and distribution channels for possibly increased distribution efficiencies and service levels [2]. This paper presents a pricing mechanism for digital content distribution over P2P networks that shows that content providers can adopt P2P distribution and tap into the powers of self-

organizing consumer networks [3] while retaining profitable pricing capabilities. In general, client-server based distribution models provide content providers with several advantages. Since all content product files reside on a centrally organized server system it allows content providers to maintain control over content supply and product promotion. Consumers need to connect directly with the provider when looking for a product and download it from its central server site if they decide to purchase a copy. Hence, customer relationships and personalized marketing efforts can be managed centrally. However, centralized, client-server distribution also poses several limitations. Besides potential capacity problems with regard to simultaneously servicing a large number of customers, its hierarchical design also constrains the immediate participation of the consumers in generating additional interest and demand for content products. Peer-to-peer consumer networks, on the other hand, are truly interactive network designs that can serve as effective viral marketing platforms where any peer can directly and proactively communicate with any other peer, exchange information, promote certain products through word of mouth, and of course trade content. Thus, peer nodes can act at the same time as both suppliers and consumers of content products and information. Moreover, by sharing computing resources, such as file storage space and communication bandwidth, P2P networks can adapt to peak demand times and distribute work over the peer nodes in the network and serve high volumes of content. The higher the demand for a particular content file the more it will be available in the network as peer nodes download a copy and become potential suppliers of the same product. The collective content supply by the peer users of a large P2P network substantially increases product selection in terms of breadth (number of different product offerings) and depth (number of sources for a particular product).

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Table 1 compares client-server and peer-to-peer distribution and suggests that organizing content distribution over P2P consumer networks could improve service levels in terms of content provision, content delivery, consumer satisfaction, and possibly distribution cost. The potential for piracy presents a big concern in either of the two models. But technical solutions like digital rights management and content encryptions methods and legal actions to enforce existing copyright laws apply in both situations and can be employed to mitigate piracy problems [4]. Table 1: A comparison of client-server and p2p distribution models Service Attribute

Client-Server Distribution Model

P2P Distribution Model decentralized

organizing principle

centralized

user role

passive Consumer

active participant

product selection

content provider site

collective user holdings

content search

central product catalog

distributed play lists

content distribution

one to many delivery

User to user exchange

service capacity

centrally allocated resources

distributed, shared resources

piracy threat

leaking products to outside channels

sharing without remuneration

Several additional issues need to be addressed before a content provider can successfully implement a digital distribution model over P2P networks. Assuming that providers charge for content and that users value content and are willing to pay for it, there needs to be a mechanism that makes information about costs and value available to participants in the network. While the occurrence of some free-riding may not lead to a serious deterioration of system performance [5], the network does need to provide incentives to users to participate in distributing content through exchanging product files in order to achieve stability, efficiency and effectiveness [6, 7, 8]. In order to optimize service performance it is necessary that the self-interested participants reveal important information concerning the value of demand

products and costs for sharing resources and providing their own content holdings and trade-off information concerning available bandwidth and storage capacities across the network [9]. Our P2P distribution model presented in the next section describes a pricing mechanism that allows a monopolistic provider of a digital product to use the advantages of P2P networks while collecting the same revenue as in an equivalent client-server network, but, importantly, customers are serviced faster on average, that is, distribution efficiency is increased. Users who upload more content than they download receive compensation from the network, which motivates users to increase participation [10]. In section 3, we relax some of the assumptions made in the basic model and specifically discuss some implications of temporal consumer preferences and variable bandwidth. We also discuss, in section 4, an example that shows that a similar pricing mechanism can be used in a monopolistic competition setting. The extended version of the mechanism eliminates the traditional free-riding problems of P2P networks [11] and at the same time it is relatively simple to implement.

2. Dynamic distribution model Distributing digital content over large-scale P2P networks exhibits characteristics of Internet access and data services as well as selling information products. We view our distribution model as a hypbrid from both worlds. Traditionally, media content providers like the music industry have been product-oriented and focused on selling individual copies of content. But in an age where information content is ubiquitous, selling content like music becomes much more service-oriented where pricing depends largely on usage and volume rather than particular content. Hence, two streams of pricing literature are relevant for our pricing mechanism, pricing for network traffic and pricing for information goods. The latter stream of research generally suggests differential pricing as the best strategy for selling information products when the seller can offer different quality versions for different prices to different consumer segments [12, 13]. The literature on data and voice communication services distinguishes between flat-rate and usage-based pricing schemes. It is clear that usagebased pricing is economically most efficient, at least from a theoretical perspective, in achieving optimal bandwidth allocation and reducing network congestion and overuse. [14, 15, 16]] But there is also ample evidence that consumers of telecommunication and Internet-based services prefer simple, flat-rate pricing schemes [17]. As technology

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matures, quality rises, prices decrease, and overall usage increases and generates increased total revenues. The need for market segmentation lessens and simple pricing plans based on flat rates begin to dominate [Anania 1995]. Flat rates encourage usage, but also over-use and waste, and lead to economically inefficient subsidies of heavy users. Usage-based pricing, on the other hand, allows providers to more effectively implement quality of service differentiation and price discrimination, but discourages usage when the unit price is significant. P2P service environments thrive when they have a large user base and high levels of peer participation. Low unit prices1 encourage content exploration and experimentation and thus stimulate demand, while high unit prices2 offer potentially healthy profit margins but lead to very selective purchases that are largely limited to known and familiar offerings. In an effort to balance the needs to provide users with participation incentives and service providers with profitable margins, we suggest the adoption of a usagesensitive pricing mechanism where the unit price is low enough to encourage users’ experimentation with unknown content but at the same time still high enough to compensate, through the increased usage of the system, for the reduced margins. In the case that the distribution network is not controlled by the content provider(s) but by an independent intermediary that resells original content to peer consumers the question of designing licensing agreements arises. In the music industry, for example, there are several forms of licensing in place. Congruent with our concept of music as a service-oriented business, we would envision a licensing agreement for our distribution model that is similar to the public performance deals that the recording industry has with broadcast radio stations and bars and restaurants that play recorded music on their premises to entertain their customers. Unlike when selling recorded music as products in form of CDs, collecting music societies like ASCAP, BMI, and SESAC are collecting very small fees 1

Free file-sharing systems represent extreme cases where low (zero) unit prices created explosive user participation and entirely new forms of music exploration at massive scales. 2 Microsofts MSN music store and Apple’s iTunes, for example, charge a pretty steep 99c per song at their centralized, client-server music distribution site with the consequence that customers only download music they already know they like. As a case in point, a recent study by Jupiter Research (quoted in F. Ahrens, “Microsoft Steps Into The Ring, Washington Post, Sep 4, 2004) found that iPod owners currently carry on average only a mere 34 bought songs on their players that can hold up to 10,000 songs. This illustrates that consumers of digital music do not use pay per-download services that charge high unit prices for exploring new music.

per song from public performance plays and redistribute them to the copyright holders. Charging high royalty fees per song, on the other hand, overly constrains adoption of pay-per download services. While designing a precisely defined licensing agreement for digital distribution is beyond the scope of the present paper, several detailed proposals designed to stimulate usage have been proposed in the law community [19]. More specifically, we propose that the P2P network chooses two base prices Pd and Pu, where Pd is the base price per byte of content for every downloaded file and Pu is the base compensation price per byte for every uploaded file.3 4 The idea to charge for volume of content but not for individual content products emphasizes the concept of content delivery as a service rather than a product-oriented sales business. It resembles, for example, the per-packet pricing scheme that [14] devised for Internet data services. Thus prices vary with the length of the file but do not depend on its type or particular content. For example, a longer song will cost more than a shorter track but songs of same length are charged the same independent of the artist. Conceptually, we need to distinguish between content providers who create and supply original content and network service providers who maintain the distribution network that delivers content to peer consumers and collect the upload and download fees. In the main analysis of this paper, however, we assume that the content provider assumes both roles, that is, it creates original content and controls the distribution network (forward integration). The idea of a metered service that charges a tiny amount per unit (byte) of music delivered resembles the concept of a utility service. Similar to consumers who don’t want to decide exactly which lights, appliances and electric devices to turn on in their homes for possible use, our peer users would not need to decide exactly which songs they are later going to listen to when they download music. Because the price of electricity per kwh is so low that a certain amount of waste is acceptable if it provides the consumer with increased comfort, convenience, or safety, yet the price is still significant enough to prevent people from leaving the lights on all the time. In other words, consumers of a metered music delivery service, where the marginal cost of downloading the next song is negligible, are encouraged to explore 3

In the context of the examples cited in footnotes 1 and 2, our base prices would require peer users to pay for content, yet at significantly cheaper rates than currently available from legitimate, commercial digital music providers. 4 A constant membership fee, one-time or monthly, could be added to our model specification. However, for the sake of simplicity and without loss of generality, we assume free membership.

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and download liberally without losing track of total consumption. Music as a utility also implies that it is delivered content-independent, just as electricity is delivered independent of its eventual use. Only the amount of music, in bytes, is metered. In a world where consumers hold vast amounts of digital content that can be replicated and transmitted at quasi-zero cost, the value of a collection can no longer lie in the size of the music library, as it may have been in the past, but in its quality, which is highly specific to the consumers’ individual tastes and preferences. Hence, the delivery of music is seen as a low-value business, while helping with selecting and finding music a customer really likes to listen adds value to a service that is becoming increasingly important. And peer-to-peer music communities are a low-cost, yet effective way to help consumers with selecting music. To determine the values of our base prices Pd and Pu, we use a dynamic distribution model, where f denotes the size of a content file in bytes and bs denotes the content provider’s server bandwidth. We will first develop a model for a client-server network environment and then compare this with a peer-to-peer network setting. Demand of content files occurs and is fulfilled over time. The distribution of files in both network settings occurs in generations, that is, consumers demand a product in consecutive groups where the first group, or generation, is served before the second one and so on. We borrow the idea of different generations from generation models in economic growth theory [20] but slightly modify its form to fit the specifications of our model. We use the subscript g as the generational index. For the sake of computational convenience, we imagine that the whole population N could be divided into G consumer generations of equal sizes. The generations are equivalent in terms of their value for the file to be downloaded. That is, the value for a file is uniformly distributed on the interval [0, 1] in every generation5. The model represents a single product case and is concerned with the distribution of only one song. The size of each generation is marked by Ng where Ng = N/G for all generations in the present case. Let us first focus our attention on the distribution model in a client-server setting. Suppose that a monopolist owner of a content file with size f is distributing it over a client server network and is charging a price of P dollars per byte6. A user of this distribution network whose value for the file is greater than its price (fP) will decide to download it. The number of people who decide to download the file in 5

We discuss time variant preferences and generations of different sizes in section 3 and in a separate paper [21]. 6 Obviously, P would be a very small amount, like a fraction of a cent.

each generation follows a binomial distribution with probability of success (i.e. the probability that a user wants to purchase a copy of the file) equal to 1-fP for

0≤ P
0 and the peer’s computer resources and bandwidth can not be used for alternative purposes. A peer can therefore receive compensation larger or smaller than fPu. Obviously the amount of maximum compensation for a peer will also depend on his generation. In the first generation, the original owner’s server is the only source for downloading the file. Thus every peer from the first generation who decides to download the file incurs a cost of fPd. Let us keep the notation from the client-server distribution case and use ng to denote the number of users who decide to download the file in generation g. The decision to download the file in the P2P distribution system is more complicated because peers have to evaluate the expected future streams of revenue when they share their copy with future generations. The n2f total demanded bytes by the second generation peers will be supplied by the main server and the first generation peers who are willing to share. The revenue share for each source will depend on its bandwidth. Each first generation peer will contribute

n2 fbc bytes to the peers in the second generation bs + n1bc n2 fbc Pu and his compensation will be ; the original bs + n1bc n2 fbs owner will contribute . Similarly, each firstbs + n1bc generation peer can expect a compensation from the third generation peers equal to

n3 fbc Pu . Note that bs + (n1 + n2 )bc

compensation decreases because the number of available copies increases and individual peers will need to supply progressively smaller shares. The total compensation for the first generation peers from all future generations can G

be expressed as

nb fPu ¦ k kc−1 . Balancing the k=2 bs +bc ¦nr r=1

compensation for sharing and the cost of the file, each first generation peer will eventually be charged an amount equal to fPd − fPu

G

¦ k =2

nk bc k −1

. We can

bs + bc ¦nr r =1

G

Cg = Pu

¦

k = g +1

nk bc k −1

. We should note here that

bs + bc ¦nr r =1

CG = 0 since there will be no more future generations after generation G. A peer in this P2P distribution system will decide to download a file only if his value for the file is greater than the net price that the peer has to pay. This happens when v − fPd + fC g ≥ 0 . Given the value distribution for

the

generation we find that n g = N g (1 − fPd + fC g ) and that the total revenue

for

the

file

in

each

monopolistic

file

provider

is

G

TR = ¦ fN t (1 − fPd + fC g )( Pd − C g ) . g =1

In order to evaluate optimal pricing schemes the model has to include cost analysis as a component. The process of digital content production and distribution has fixed and variable costs. The fixed cost does not change during the distribution process. It comprises of the resources spent into the production of the file as well as the initial investment in the equipment used for file distribution. The fixed cost only serves as a constraint when total revenue is optimized, that is, the optimal revenue has to cover the fixed cost in equilibrium. The variable cost, VC, is a function of the amount of bytes that need to be provided in every client generation. To simplify the analysis we assume that the variable cost function is linear and is expressed by VC g = αfn g

where α is a positive coefficient. The profit of the monopolist in the client server setting is then G

π = ¦ N g (1 − fP)( fP − αf )

and

the

revenue-

g =1

maximizing price is

P=

1 α + provided that the 2f 2

fixed cost is covered. The variable cost becomes more important in the P2P setting and is calculated in a slightly different way. The variable cost in the first generation is VC1 = αfn1 and in every subsequent generation

VC g =

αfn g bs g −1

. The variable costs

bs + bc ¦ n g

generalize this formula to calculate the expected compensation per byte of a peer in any generation g ,

k =1

decreases with every generation and becomes negligibly small as the number of generations goes to infinity. This means that the variable cost is only significant in the

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beginning of the distribution process when the content provider is most actively involved. In later generations, peer users who share content assume most of the distribution load.

3. Implementing different pricing schemes Music and video are experience goods in which the consumer does not know the value of the good until it is used. Different consumers will have different experiences with media content and most likely differing valuations. Hence, uniform pricing that undercharges some customers while overcharging others may not be optimal. For example, [22] found that music should be priced to incorporate the value of music to consumers and also address technological factors that affect sharing. Using our basic model developed above, we now develop two pricing strategies. First, we derive a simple monopolistic pricing scheme that does not allow any price discrimination. Second, we present some auctioning approaches that do incorporate different consumer preferences. In particular, we consider that consumers who value a specific piece of content higher than others may be willing to pay a higher price if that will get them served before others with a lower valuation.

Pug =

G

f

¦b

k = g +1

s

fPd − 0.5 0.5N g bc

1≤ g ≤ G −1

+ 0.5N g (k − 1)bc

Eventually, the monopolist will earn the same revenue in the peer-to-peer model as previously in the client-server model. However, in the P2P model much less time is needed to distribute the file to all generations. It is equal to the expression

t g = 0.5 N g

f . bs + 0.5( g − 1) N g bc

Distribution times are equivalent between the two models only in generation 1. In every other generation the distribution time spans are smaller for the P2P model, and the difference increases with each generation. Hence, P2P distribution is more efficient than clientserver distribution. 3.2 Pricing membership

through

Auctions

for

generation

3.1 Simple Monopolistic Pricing Because of the complex form of Cg, it will be hard to calculate what the optimal Pd and Pu should be. To simplify this task, the monopolist can use the following strategy. He can vary Pu through all the generations in such a way that Cg and ng are kept constant. We will mark the total constant compensation per peer per byte as Pc. Total revenue is then optimized for every Pd and Pc for which Pd – Pc =

1 . The number of served 2f

peers in every generation is ng = 0.5Ng= 0.5N/G. Notice that in this case the compensation per byte the last generation receives is not zero but Pd – optimal variable schedule for Pu should be

1 , and the 2f

The model presented above assumes that demand preferences do not change over time and are constant in each generation. It also assumes that the monopolist has accurate information about the peers’ preferences as well as their bandwidth and the opportunity cost of their computer resources. Clearly the assumption that bandwidth is constant among peers is somewhat unrealistic given the current variety of ways to connect to and use the Internet. Under these circumstances computing the optimal schedule for the variable Put would be quite a burden to the monopolist. Every miscalculation in predicting the correct upload price before each generation appears will most likely result in non-optimal profits and non-optimal distribution patterns. To ease this task the monopolist could create a market, in which he can auction the right for a peer to be in a particular generation. The monopolist has to choose the amount of permits to sell to each generation and a fixed reservation price per permit, which should be equal to the per unit cost of production. Afterwards he can let peers bid for the right to be a member of a certain generation. There are many available multiple unit auction mechanisms with different revenue and efficiency

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properties that could be used to auction off the generation permits. 3.2.1 Complete Price Discrimination through a Vickrey-type Auction. If the monopolist is free to maximize revenue and extract nearly all consumer surplus through price discrimination, he could conduct a version of a simultaneous multiple unit Vickrey auction, in which peers will be allowed to submit exclusive OR bids for permits to be a member of any generation. “$0.39 for a first generation permit OR $0.24 for a second generation permit” could serve as an example of the bids that would be required. The auction is computationally intensive because it calculates a different price for each bidder in the auction. This auction type will likely bring more revenues for the monopolist than in the model described above. 3.2.2 Decentralized Monopolistic Pricing through a Uniform-price Auction A uniform-price sealed bid auction is less computationally intensive than the Vickrey auction because it charges every winner of a permit the same price, but at the same time it decreases the revenue collected by the monopolist. This auction type should bring revenue roughly equal to the one in the model above. In this case, however, the monopolist is not required to know in advance the consumers’ preferences 3.2.3 Ramsey Pricing through a Multi-unit English Auction An auction version that might be best liked by consumers is the multiple unit English auction, in which the price for each generation increases step by step and peers drop out of the auction when they think prices become too high. Clearly initial prices in this auction version should be set to the per unit production cost of the digital file to be distributed. Bids in this auction are open, so consumers have some strategic advantage in deciding the ending prices. Based on previous work in [23], revenues in this auction will be likely below the revenue of the monopolist in the model presented above. But the reservation price guarantees that total costs will be covered. The use of an auction mechanism will shift the computational burden from the monopolist to the individual peer. It will now be up to the peers to figure out how much they should bid based on their bandwidth,

time preference, and expectations about the behaviors of other peers. The profit of every peer in this setup will be equal to v – PP + TC, where PP is the price that a peer paid for the permit and TC is the total compensation that a peer will receive from the total amount of content (in bytes) that he will have shared by the end of the distribution process. Clearly this scheme will be increasingly hard to implement with the presence of many file creators especially when there is some degree of substitution between the digital products being distributed7. In this case we propose a simplified pricing mechanism in which peers are again reimbursed based on their participation in the P2P network. The download and upload prices per byte are fixed and they are applied to every downloaded or uploaded file regardless of ownership and regardless of generation.

4. Implementation example The following is an illustration of how the pricing mechanism discussed above could be applied in practice (with a few simplifying modifications) when multiple creators of original content files use the same P2P network for distribution. Again, we assume the network service provider acts on behalf of the alliance of the content providers that use the network for delivering their products. For a fee, it operates the network and collects the revenues and distributes the participation compensations. Charges are again applied per byte of downloaded or uploaded content. The revenue gathered from file distribution over the network is divided proportionately and passed back to the creators of original content and files and to the network itself. Suppose that a P2P network has three peer nodes (1,2,3). As illustrated in figure 1, Node 1 has created an original content file with size f. The copy of this file has first been downloaded by node 2 from node 1, and then by node 3 from both 1 and 2. We assume equal up bandwidth for peers 1 and 2. Because of the small number of generations Pu will stay constant, or only change negligibly.

7

For example, if a permit to download the latest Britney Spears release becomes to expensive in the auction process, a user may simply opt for an alternative and substitute Britney with Beyonce.

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concerns the revenue splitting among the content creators. Tracing file ownership within a dynamic P2P network will be hard. However, when several competing P2P networks are present there will be an incentive for them to develop and deploy digital rights management technologies that can reliably and accurately trace file usage. Alternatively, tracking data could be used like those compiled by online media measurement firms. For example, the company Big Champagne already provides services that monitor file-swapping activities in filesharing networks. They are using sampling and estimation methods in order to measure the number of music downloads in popular P2P file-sharing networks. Figure 1: Example 1

In this case Node 2 pays f * Pd - 0.5 * f * Pu Node 3 pays f * Pd Node 1 receives 1.5 * f * Pu + a * 2 * f * (Pd - Pu) Network receives (1-a) * 2 * f * (Pd - Pu). The parameter a represents the proportion of network revenue that goes back to the creators of original content files. If the content provider also controls distribution, as in our previous theoretical analysis, he also sets the particular value of a. In the general case of independent content and network providers, the parameter a would be the bargaining parameter between the content provider and the network. It would be a matter of contract negotiation and does not derive from analytical analysis. In an alternative scenario, depicted in figure 2, node 2 refuses to share his copy file and node 3 has download her copy from the original source only. In that case Node 2 pays f * Pd Node 3 pays f * Pd Node 1 receives 2 * f * Pu + a * 2 * f * (Pd - Pu) Network receives (1-a) * 2 * f * (Pd - Pu).

In the case that there are many original content creators, the revenues generated from the network are divided proportionately among them and a part of the revenues is used to compensate actively participating users for helping to distribute content. We augment this pricing mechanism by allowing users to raise Pd for themselves in case of network congestion. When a user increases Pd, the network has to determine and inform the user if the increase will speed up the download [16]. The most contentious part of this pricing scheme

Figure 2: Example 2

5. Conclusions and Possible Model Extensions This paper has described a usage-based pricing scheme for distributing digital content over peer-to-peer networks that rewards peer users who actively participate in the distribution process. This participation incentive creates effective P2P communities and leads to faster content distribution than in equivalent client-server settings while retaining the same profit level. More research needs to be done in order to fully explore the implications of competition among the three types of agents. Strategic options for independent network service providers could be examined. For example, strategic options that an independent network service provider has in response to pricing strategies set by the content providers could be considered. Yet another open question concerns the competition among several independent P2P network service providers for content suppliers and customers, and the inclusion of more dynamic consumer preferences. As described above, we have assumed a number of limiting modeling assumptions in our theoretical analysis in order to retain analytical tractability. In a separate study [21],

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parallel to this research, we have designed a set of economic experiments that allow us to relax some of these modeling assumptions. These include, for example, allowing different levels of consumer bandwidth, variable-length consumer generations, and time-variant consumer preferences.

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