Message relayingIncentive mechanismsMulti-level marketing modelsReinforcement learning
ingtocose tulnmiter d
includes this peer. The mechanism allows peers to rationally trade-off communication efciency and reli-ability while maintaining information locality. We provide some analytic insights to the symmetric Nash
ly gainunitiesing disd stortems is
covery is realized via message relaying between peers so that amessage for resource searching is propagated in the system witha word-of-mouth effect.
Traditionally there are two ways to perform distributive peerdiscovery in unstructured peer-to-peer systems (Milojicic et al.2002, Yang and Garcia-Molina 2002): breadth-rst search (BFS,used by Gnutella) and depth-rst search (DFS, used by Freenet).
2003, Shneidman and Parkes 2003a, Dasgupta et al. 1979, Maet al. 2004, Sun and Garcia-Molina 2004). For example, a peermay simply drop a message that is sent from other peers for relay-ing, for the purpose of saving communication bandwidth and en-ergy. Therefore, a message relaying P2P system is vulnerable tothe free-riding problem, i.e., a node relies on others efforts to relayits own messages, but does not cost itself to relay messages forother nodes. Free-riding can cause severe degradation of the sys-tem performance and prevent requesters from nding high qualityproviders efciently (Adar and Huberman 2000, Shneidman andParkes 2003a). It is important to design an incentive mechanism
* Corresponding author.E-mail addresses: Cuihong.Li@uconn.edu (C. Li), firstname.lastname@example.org (B. Yu),
Electronic Commerce Research and Applications 8 (2009) 315326
Contents lists availab
.email@example.com (K. Sycara).provide certain information or services in an efcient way, so thatpeers can exploit the distributed resources owned by other peers inthe system (Milojicic et al. 2002). To ensure the scalability androbustness of the system, as well as to avoid some legal issues, acentralized database of content of each peer usually does not existin a P2P system (an exception is Napster). Instead, a distributedcatalog of content is favored in which each peer only maintains alist of resources/services, and may contain information about theacquaintances and neighbors. In this distributive model peer dis-
pared to BFS (Lv et al. 2002). But the performance based on randomwalks is highly variable, and greatly depends on the network topol-ogy and the number of walkers (Tsoumakos and Roussopoulos2003).
Besides the system efciency, another problem that requiresattention in the protocol design is incentives. A P2P network is ahighly decentralized system and each peer may represent a differ-ent self-interested entity. A peer may manipulate the local infor-mation to take advantage of other peers resources (Ng et al.1. Introduction
Peer-to-peer systems have recentthe academic and industrial commsystem is modeled as a self-organizinformation is highly distributed anOne important challenge for P2P sys1567-4223/$ - see front matter 2009 Elsevier B.V. Adoi:10.1016/j.elerap.2009.04.007equilibrium strategies of this game, and an approximate approach to calculate this equilibrium. Experi-ments show that this incentive mechanism brings a system utility generally higher than breadth-rstsearch and random walks, based on both the estimated utility from our approximate equilibrium andthe utility generated from learning in the incentive mechanism.
2009 Elsevier B.V. All rights reserved.
ed a lot of attention in. A peer-to-peer (P2P)tributed system, whereed by individual peers.how to nd peers that
With BFS the messages are ooded in the system. Therefore, theconsumption of bandwidth is enormous, although results can befound very quickly. With DFS searches can be terminated once a re-sult is found, and therefore use less bandwidth. But the responsetime could be very long and is exponential in the depth limit. Re-cently random walks (RW) have been considered as a method forP2P search that signicantly reduces the number of messages com-Keywords:Peer-to-peer systems
sage propagation process. A peer is rewarded if a service provider is found via a relaying path thatAn incentive mechanism for message rela
Cuihong Li a,*, Bin Yu b, Katia Sycara c
a School of Business, University of Connecticut, Storrs, CT 06269, United StatesbQuantum Leap Innovations, 3 Innovation Way, Suite 100, Newark, DE 19711, United ScRobotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh,
a r t i c l e i n f o
Article history:Received 14 July 2008Received in revised form 11 April 2009Accepted 13 April 2009Available online 22 April 2009
a b s t r a c t
Distributed message relayviders. Existing search procations, cause long responpeers, these systems are vmechanism that not onlyin message relaying for pe
Electronic Commerce R
journal homepage: wwwll rights reserved.ng in unstructured peer-to-peer systems
s5213, United States
is an important function of a peer-to-peer system to discover service pro-ls in unstructured peer-to-peer systems create huge burden on communi-ime, or result in unreliable performance. Moreover, with self-interestederable to the free-riding problem. In this paper we present an incentiveigates the free-riding problem, but also achieves good system efciencyiscovery. In this mechanism promised rewards are passed along the mes-
le at ScienceDirect
earch and Applications
l sevier .com/locate /ecra
archthat motivates each peer to behave rationally, and results in goodsystem efciency.
In this paper, we present an incentive mechanism of messagerelaying for peer discovery that overcomes the ooding problemof BFS search while preserving the quick response property andgood reliability. Although both peer discovery and distributedrouting are related to message relaying, they are different prob-lems (Chandan and Hogendorn 2001). In distributed routing thedestination of a message is known and the routing paths are welldened (Papadimitriou 2001). For each action (dropping or for-warding the message to a node) of a peer, the consequence is wellspecied and the payoff is clearly expected. A peer only needs todecide whether or not to take the action, depending on the incen-tive provided, in ways of a market price, reciprocal rewards, orcontract payment, etc. (see Section 2 for a review of the incentivemechanisms in P2P systems). But in peer discovery the destina-tion of the message is unknown, and hence it is not clear whothe message should be sent to, what action a peer is expectedto take, or what the consequence and payoff of each action willbe. This implies greater uncertainty and less control in messagerelaying for peer discovery than for distributed routing. Therefore,the incentive mechanisms for distributed routing, which requireprior knowledge of routing paths, cannot be applied to our peerdiscovery problem.
Most of the existing study of P2P systems is concerned withcontent sharing or information availability (e.g, Feldman et al.2003, Golle et al. 2001, Figueiredo et al. 2005, Hughes et al. 2008,Bhattacharjee et al. 2005, Arora et al. 2005). Taking a different per-spective, our work aims at improving the search capability, one ofthe major technical issues in content sharing P2P systems (Milog-icic et al. 2002). While content sharing involves the behaviors ofend users (Chen et al. 2008), distributed searching is a techniquethat once implemented in the software, remains largely transpar-ent to end users. Thus our incentive mechanismmay be consideredas a protocol of P2P distributed searching based on an economicmechanism. For this reason, the transaction cost and user behav-iors are not a concern.
In a P2P system there may exist a small portion of altruisticnodes that contribute even without incentives. Due to the exis-tence of such nodes, a content sharing system may be able to sus-tain even if all other nodes are free riders (although free-ridingbehaviors lead to degradation of the system performance). Forexample, in 2005 it was found that 85% of all Gnutella users werefree riders (Hughes et al. 2005). However, we think that it is lesslikely for distributed searching to succeed if relying on a few altru-istic nodes. In order for an information requestor to nd a provider,the message has to reach a provider along a path from the reques-tor. If any node on the path drops the message, the relaying on thatpath cannot succeed. Thus free-riding may have more substantialeffects on distributed searching than on content sharing.
In our mechanism, the source peer sends the query to someneighbors and promises some payment to each receiver if the re-source provider is found via a transmission route that includesthe receiver. Depending on the offer, each receiver decides thenumber of neighbors it relays the message to and also the prom-ised payment to its immediate downstream peers. Each of thenew receivers again makes similar decisions, until the maximumnumber of hops (time-to-live) is reached. One feature of this mech-anism is that it does not price the relaying activities, but insteadprices the relaying result, which inuences the relaying activities.It tackles not only the incentive problem, but also communicationefciency and reliability in a P2P system.
The rest of the paper is organized as follows. In Section 3 we
316 C. Li et al. / Electronic Commerce Resemotivate and present the model of the message relaying mecha-nism in peer-to-peer systems. The equilibrium analysis andapproximation are presented in Section 4. Simulation results areprovided in Section 5. Section 7 concludes the paper with somedirections for future research.
2. Prior work
Peer-to-peer (P2P) systems have received increasing attentionfor benets such as improving scalability, eliminating the needfor costly infrastructure, and enabling resource aggregation(Milogicic et al. 2002, Oram 2001). With all these benets, P2P sys-tems also create challenges in discovering information efcientlyin the network. Some research work is dedicated to the design ofefcient search techniques. Gnutella is a famous protocol designedto facilitate decentralized search and discovery of information in anetwork (Gnutella 2001). This protocol permits a node to post que-ries, forward a query to other nodes, and respond to a query. In thismanner, through the shared use of resources provided by the relay-ing nodes, information can be located and shared between nodes inthe network. However, a shortcoming in Gnutella is the unintelli-gent relaying of queries to other nodes in the network. A nodeupon receipt of a query relays a request (within time-to-live) toall of its neighbors. This results in wastage of shared resources suchas bandwidth without necessarily generating more value to therequestor.
Some other research work for search techniques in unstruc-tured P2P systems aims at reducing the number of nodes that re-ceive and process each query with little sacrice of the quality ofresults. The different approaches include: adaptively deepeningthe search based on the responses (Yang and Garcia-Molina2002), selectively querying neighbors based on their quality orreputation (Yang and Garcia-Molina 2002), building local indicesthat allow nodes to process query on behalf of nodes in a localrange (Yang and Garcia-Molina 2002, Adamic et al. 2001), main-taining hints as to the possible information location by learningfrom the history (Crespo and Garcia-Molina 2002, NeuroGridwww.neurogrid.net), and random walks (Lv et al. 2002). Althoughthese techniques improve the efciency of searching P2P networks,they are based on the assumption that the nodes are cooperativeand can be programmed to follow these protocols.
P2P systems are often composed of nodes governed by self-interested parties, each acting to better its own outcome. The ra-tional behavior of nodes creates the free-riding situations inpeer-to-peer settings. In these situations, nodes consume resources(bandwidth, computation, energy, les, etc.) of others but do notcontribute at the same level of their consumption. Several papershave helped to advance the understanding of disincentives ofcooperation in P2P systems. Feldman et al. (2003) quantify disin-centives in le sharing P2P networks. Christin and Chuang (2005)propose a cost-based model to assess the resources that each over-lay node has to contribute for being part of the overlay, which al-lows to gauge potential disincentives for nodes to collaborate.Shneidman and Parkes (2003b) discuss the notions of rationalityand self-interest in P2P systems. In Shneidman et al. (2003) theyadvocate mechanism design for P2P systems in which peers are ex-pected to be rational and self-interested and may deviate from asuggested protocol. They also discuss some open problems inmechanism design for peer-to-peer systems.
In the following we focus on the review of the incentive mech-anisms that aim at improving the efciency of a P2P system con-sidering the rational behavior of nodes. Generally, the incentivefor a peer to cooperate is induced by some economic mechanism.The economic mechanisms that have appeared in P2P incentive de-sign include micropayment, reciprocity, taxation and contracts. Weshare give a brief review of the research work based on each of
and Applications 8 (2009) 315326these mechanisms.In a micropayment system, a peer is rewarded with virtual cur-
rency or credit for each action by the system or the peer who
archbenets from the action. In a P2P system, the action can be, e.g.,forwarding a message, uploading a le, answering a query, etc.Based on game theoretic analysis Golle et al. (2001) and Figueiredoet al. (2005) evaluate the effectiveness of different micropaymentmechanisms to motivate le sharing in P2P systems. Buttyn andHubaux (2001) and Zhong et al. (2003) apply micropayment mech-anisms to stimulate packet forwarding actions in a P2P network.Rogers et al. (2005) present a distributed mechanism for self-orga-nized routing in a energy constrained sensor network. In theirmechanism, a sensor receives a payment from the server each timeit transmits data to the center, for itself or as a mediator for an-other sensor. The payment scheme is designed to motivate locallyselsh strategies that possess desirable global properties. Mostexisting micropayment schemes require a trusted centralized bro-ker (server), which is responsible to distribute and cash credits.Yang and Garcia-Molina (2003), Micali and Rivest (2002) andGlassman et al. (1995) propose several micropayment mechanismsthat reduce the load of the broker. In these above mentionedmech-anisms, the reward that a peer receives for each action is indepen-dent of the resources, the work load, or the value of the service. Inother words, the payment is based on a static and uniform price. Inour mechanism, the payment a peer promises or receives dependson the value of information and its position (hop number) in thepropagation process. Therefore, it can be regarded as a dynamicpricing mechanism that allows the price to change with the mi-cro...