Peer-to-Peer Networks – Protocols, Cooperation and ..., Slides of Network Technologies and TCP/IP

Download Peer-to-Peer Networks – Protocols, Cooperation and ... and more Slides Network Technologies and TCP/IP in PDF only on Docsity! Peer-to-Peer Networks – Protocols, Cooperation and Competition Hyunggon Park Signal Processing Laboratory (LTS4), Institute of Electrical Engineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland Rafit Izhak Ratzin Computer Science Department, University of California, Los Angeles (UCLA), Los Angeles, USA Mihaela van der Schaar Multimedia Communications and Systems Laboratory, Electrical Engineering Department, University of California, Los Angeles (UCLA), Los Angeles, USA 1. INTRODUCTION Peer-to-peer (P2P) networks connect many end-hosts (also referred to as peers) in an ad-hoc manner. P2P networks have been typically used for file sharing applications, which enable peers to share digitized content such as general documents, audio, video, electronic books, etc. Recently, more advanced applications such as real-time conferences, online gaming, and media streaming have also been deployed over such networks. Unlike traditional client-server networks, where servers only provide content, and clients only consume content, in P2P networks, each peer is both a client and a server. It has been observed that P2P file sharing applications dominate Internet traffic usage. In fact, a wide range of measurements, which were performed in 8 different geographic regions during the years of 2008-2009, show that P2P networks generated most of the traffic in all monitored regions, ranging from 43% in Northern Africa to 70% in Eastern Europe (http://www.ipoque.com/). The same study also identified that BitTorrent (Cohen, 2003) is the most popular protocol on the Internet, generating most of the traffic in 7 out of 8 regions ranging from 32% in South Africa to 57% in Eastern Europe. The details of the BitTorrent protocol will be discussed in Section 3. Recently, media delivery and streaming services over the Internet such as YouTube (http://www.youtube.com/), PPLive (http://www.pplive.com), and Internet video broadcasting (e.g., AOL broadcast, MSNBC, CBS, etc.) have emerged. These services have become very popular, as they can deliver video to a large number of receivers simultaneously at any given time. In order to reduce infrastructure, maintenance, and service costs, and provide more reliable services, the content providers often implement their services using P2P network. While several designs for P2P systems have been successfully deployed for file sharing and real-time media streaming, key challenges such as the design of optimal resource reciprocation strategies among self-interested peers still remain largely unaddressed. For example, pull-based techniques (Cohen, 2003; Pai et al., 2005; Zhang et al., 2005) are designed assuming that peers are altruistic and are willing to provide their available data chunks (pieces) whenever requested. However, such assumptions may be undesirable from the perspective of a self-interested peer, which aims to maximize its own utility. Thus, efficient resource reciprocation strategies need to be deployed, which can also provide incentives to the peers for their contributions. In BitTorrent systems, incentive strategies are based on the so-called tit-for-tat (TFT) strategy, where a peer selects some of its associated peers (i.e., leechers), which are currently uploading at the highest rates, and provides them its content for downloading (Cohen, 2003). This simple strategy is currently implemented in BitTorrent systems, and provides good performance. However, a key disadvantage of this resource reciprocation strategy is that peers decide how to determine their resource reciprocation based on only the current upload rates that it receives from its associated peers, and does not consider how this reciprocation will impact their upload rates in the future. In other words, the resource reciprocation based on the TFT is myopic. Since peers in P2P networks are generally involved in repeated and long-term interactions, such myopic resource reciprocation strategy can result in a suboptimal performance for the involved peers. More advanced resource reciprocation strategies have been recently proposed in (Park & van der Schaar, 2009), where the resource reciprocation among the interested peers is modeled as a stochastic game (Fudenberg & Tirole, 1991). In this framework, peers determine their resource distributions by explicitly considering the probabilistic behaviors (reciprocation) of their associated peers. Unlike existing resource reciprocation strategies, which focus on myopic decisions, it formalizes the resource reciprocation game as a Markov Decision Process (MDP) (Bertsekas, 1976) to enable peers to make foresighted decisions on their resource distribution in a way that maximizes their cumulative utilities, i.e., the sum of their immediate and future utilities. Thus, this strategy can improve the performance of the peers, which are generally involved in long-term and repeated interactions. When the foresighted strategies are deployed in practice, the peers’ bounded rationality should be considered, because perfectly rational decisions are often infeasible in practice due to their memory and computational constraints. Peers can only have limited knowledge of the other players’ behavior and limited ability to analyze their environment. Therefore, it is essential to study the impact of the peers’ bounded rationality on 1) the performance degradation of the proposed resource reciprocation strategy and 2) their repeated interactions (resource reciprocation). This chapter is organized as follows. In Section 2, we overview various P2P network structures and discuss their advantages and disadvantages. In Section 3, we discuss the BitTorrent system, which is one of the most popular file sharing protocols. We also discuss the limitations of BitTorrent systems. In Section 4, we overview existing P2P-based media streaming applications, and discuss how several mechanisms have been developed to support real-time media streaming requirements. In Section 5, we show the recently proposed foresighted resource reciprocation strategies, which can improve the performance of P2P-based applications. In Section 6, we discuss new directions for game-theoretic approaches to incentive design in P2P networks. Conclusions of this chapter are drawn in Section 7. 2. OVERVIEW OF P2P SYSTEM STRUCTURES 3.1 System Description BitTorrent is a centralized unstructured system, which consists of two interacting units: 1) A “control” level describes control methods such as the required file sharing preparation, which takes place prior to sharing the actual content, and the coordination among end hosts, which is performed by a central entity during the downloading process. 2) A “reciprocation” level describes the actual data exchange among the end hosts. These two design levels are described in detail next. 3.1.1 The BitTorrent System Design – Control Level The BitTorrent content distribution system consists of the following components:  Data content  An original content provider  The metainfo file  The tracker  The end hosts or peers or clients A torrent, or swarm, is a collection of end hosts (or peers) participating in the download of content, where content may refer to one (e.g. the Linux operating system) or multiple (e.g. several video or audio) files. The tracker is a server that coordinates and assists the peers in the swarm. It maintains the list of peers that are currently in the swarm, as well as statistics about the peers. The tracker listens on a BitTorrent TCP port for coming client requests. While the default BitTorrent port is port 6969, several trackers may use different ports. Prior to the content distribution, a content provider divides the content into multiple pieces, where each piece is typically 256KB. Each piece is further divided into multiple subpieces with a typical size of 16KB. The content provider then creates a metainfo file. The metainfo file contains information that is necessary for initiating and maintaining the download process. For example, the metainfo file contains the URL of the tracker, the name of the data file (files), the length of the data file (files), and the length of a piece. The metainfo file may also contain information related to multiple data files, and optional information such as creation date, author’s comments, name and version of the .torrent creator, etc. The metainfo file also contains a special string, which is a concatenation of 20-byte encoded hash values. Each value is a SHA-1 hash of a piece at the corresponding data content, which is used for data integrity. Tracker Original Content Provider Divide into pieces Creates .torrent file Upl oa d .to rre nt U P load .torrent Peer li stAnnounce Data Content End-user Peer .torrent Metainfo file Web page links to .torrent Figure 1: BitTorrent System - Prior to File Sharing The metainfo file is usually uploaded to a website, thereby making it accessible to peers that are willing to download content by joining the swarm. A peer that is willing to join the swarm first retrieves the out-of-band metainfo file. Then, this peer contacts the tracker by sending an “announce” HTTP GET request. The request in general may include necessary information such as the total amount uploaded, the total amount downloaded, the number of bytes the peer still has to download, an event description such as started – if it is the first request to the tracker, completed — if the peer shut down gracefully, stopped – if the download completed. This information helps the tracker keep overall statistics about the torrent (e.g., number of seeds, number of leechers, life time of a seed, etc). The tracker responds back with a "text/plain" document, which includes a randomly selected set of peers that are currently online. A typical size of a peer set is 50. The random peer set may include both seeds and leechers. Seeds are the peers who already have the entire content and are sharing it with others. Leechers are the peers who are still in the process of downloading (i.e. they do not possess the entire file). The new peer can then initiate new connections with the peers in the swarm and start to exchange data content pieces. The maximum number of connections that a peer can open is limited in BitTorrent to 80 in order to avoid performance degradation due to competition among concurrent TCP flows. In addition, the new peer is also limited to establish a fixed number of outgoing connections, typically 40, in order to ensure that some connection slots are kept available for new peers that will join at a later time. Figure 1 portrays the preliminary steps that need to be performed before starting to distribute the data content. A peer that has already begun downloading may contact the tracker and ask for more peers if its peer set falls below a given threshold, which is typically set to 20 peers. Moreover, usually there is a minimum interval between two consecutive peer requests to avoid overwhelming the tracker. In addition, peers contact the tracker periodically, typically once every 30 minutes, to indicate that they are still present in the network. If a peer does not contact the tracker for more than 45 minutes, the tracker assumes that the peer has left the system and will remove the peer from the torrent list. 3.1.2 The BitTorrent System Design – Reciprocation Level The connection between two peers starts with a handshake message followed by control and data message exchanges. The control messages between peers in the swarm as well as data messages are transferred over the TCP protocol. The range of TCP ports, which is used by BitTorrent clients is 6881-6999. The connection between peers is symmetric and the control messages in both directions have the same format. The data can flow in either direction. The messages that are used in a connection between two peers are:  Handshake: the “handshake” message is a required message that ensures connections from both sides and must be the first message that is sent by the peer.  Bitfield: the “bitfield” message is an optional message and may only be sent after the handshaking sequence is completed, and before any other messages are sent. A peer can choose not to send this message if it has no pieces. Using a single bit for every piece, bits that are set indicate valid and available pieces, which can be shared with other peers, and bits that are cleared indicate missing pieces, which must be downloaded from other peers.  Interested: an “interested” message is a notification that the sender is interested in some of the receiver’s data pieces.  Not-interested: a “not-interested” message is a notification that the sender is not interested in any of the receiver’s data pieces.  Choke: the term choke is commonly used in BitTorrent as a verb that describes a temporary refusal to upload. A “choke” message is a notification that the sender will not upload data to the receiver until unchoking happens.  Unchoke: An “unchoke” message is a notification that the sender peer will upload data to the receiver if the receiver is interested in some of the sender’s data pieces.  Request: a “request” message is used to request a subpiece.  Piece: a “piece” message is sent in response to a request message, and contains the requested subpiece.  Have: a “have” message describes the index of a piece that has been downloaded and verified via the SHA-1 hash function.  Keep-alive: Peers may close the TCP connection if they have not received any messages for a given period of time, generally 2 minutes. Thus, the “keep-alive” message is sent to keep the connection between two peers alive, if no message has been sent in a given period of time.  Cancel: a “cancel” message is used to cancel subpiece requests. It is mostly sent towards the end of the download process (see more details in Section 3.3.3). A peer A in the swarm maintains a 2-bits connection state for every associated peer B that it is connected to. The first bit is the choking/unchoking bit. The second bit is the interested/not- interested bit. The connection state is initialized to choked and not interested. Peer B transfers data to peer A only if the state of the connection with A is unchoked and interested. Peer B responds to peer A’s “request” messages with encapsulated subpieces in “piece” messages. After peer A finishes downloading a piece, it verifies that the piece is uncorrupted. It calculates the SHA-1 value of the downloaded piece and compares this value with the encrypted reference value pieces that are ready to be shared (C is a constant that may vary in different BitTorrent client implementations), the leecher switches to the rarest piece first selection algorithm. 3.2.3 End Game Piece Selection The end game piece selection algorithm is performed after a peer has requested all the subpieces of the content. In this phase, a peer sends a request to all of its associated peers for all of the pending subpieces (i.e., those subpieces that have not been received yet). This step is performed in order to avoid potential delays at the end of the content download, which can occur if a request has been sent to a peer having a very slow upload rate instead of a peer having a fast upload rate. Since multiple requests for the same subpieces are sent out, once a subpiece is downloaded in the end game phase, the peer sends “cancel” messages to its associated peers so they do not waste upload bandwidth by sending redundant data. The end game is performed at the very end of the process, and thus, it may have only a small impact on the downloading process. 3.3 Peer Selection Mechanisms In BitTorrent, peers download from whom they can, and upload simultaneously to a constant number of peers. The number of associated peers, which a peer uploads to, is limited in order to avoid sending data over many connections at once, which may result in poor TCP congestion control behavior. Thus, peers need to make decisions on which peers to unchoke. The default number of peers to unchoke (unchoke slots) is four. However, this number may increase unless a peer’s upload bandwidth is saturated. A peer independently makes the decision regarding whom to unchoke and whom to choke, in every unchoke period which is typically ten seconds. The peer uploads to unchoked peers for the duration of the unchoke period. The peer selection mechanism, which is also referred to as the choking mechanism, can affect the performance of the system. A good choking mechanism should: 1) Motivate peers to contribute and upload data to the network, 2) Utilize all available resources, 3) Be robust against free-riding behaviors where peers only download and do not upload. In BitTorrent, the peer selection (choking) mechanism is applied differently to peers that are leechers and those that are seeds. 3.3.1 Leecher’s Peer Selection Mechanism The leecher’s peer selection mechanism has two parts: The TFT mechanism and the optimistic unchoke mechanism. 3.3.1.1 The TFT Mechanism In the TFT peer selection mechanism, a leecher decides to unchoke peers from which it currently downloads data. It chooses the peers who have the highest upload rate. The idea of TFT is to have several connections that actively transfer data in both directions at any time. In order to avoid wasting of resources due to rapidly choking and unchoking peers, the designer of the protocol sets the rechoke period to 10 seconds, claiming that “Ten seconds is a long enough period of time for TCP to ramp up new transfers to their full capacity” (Cohen, 2003). 3.3.1.2 The Optimistic Unchoke Mechanism BitTorrent applies the optimistic unchoke mechanism in parallel with the TFT mechanism. The goals of the optimistic unchoke mechanism are: 1) to enable a continuous discovery of better peers to reciprocate with, 2) to bootstrap new leechers that do not have any content pieces to download some data and start reciprocate pieces with others. The optimistic unchoke mechanism chooses to unchoke a peer randomly regardless of its current upload rate. Optimistic unchoke is rotated every optimistic unchoke period, when an optimistically unchoked peer is unchoked for the entire optimistic unchoke period. The designer of the protocol chose the optimistic unchoke duration to be 30 seconds, because 30 seconds is enough time for the upload to get to full capacity, for the download to reciprocate, and finally for the download to get to full capacity. Optimistic unchoke is typically applied on a single unchoke slot while TFT is applied on the rest of the unchoke slots. 3.3.1.3 Anti-snubbing If a peer has received no data from a particular peer for a certain period of time, typically 60 seconds, it marks the particular peer’s connection as snubbed. A peer does not upload to an associated snubbed peer through the TFT peer selection mechanism. This may result in more than one simultaneous optimistic unchoke, when the peer is choked by many of its associated peers. In such a case, the peer may experience poor download rates until the optimistic unchoke finds better peers. Thus, increasing number of optimistic unchokes in this scenario is important. 3.3.2 Seed’s Peer Selection Mechanism Seeds, which do not need to download any pieces, follow a different choking mechanism than the leechers. The most common mechanism is based on a round-robin mechanism, which strives to distribute data uniformly. 3.3.3 Modeling the Peer Selection Mechanism Many researchers studied the choking mechanism in BitTorrent by suggesting mathematical and game theoretical models. Qiu & Srikant (2004) studied a fluid analytical model of BitTorrent systems. They analytically studied the choking mechanism and investigated how it affects the peer performance. They showed that the optimistic unchoke mechanism may allow free-riding. Fan et al. (2006) characterized the design space of BitTorrent-like protocols capturing the fundamental tradeoff between performance and fairness. Other works such as (Izhak-Ratzin, 2009; Izhak-Ratzin et al., 2009; Neglia et al., 2007) model the choking mechanisms in BitTorrent as games with strategic peers. Massoulié & VojnoviĆ (2005) introduced a probabilistic model of coupon replication systems, and they argued that performance of file sharing system such as BitTorrent does not depend critically on altruistic behavior or on piece selection strategy (e.g., the rarest first algorithm). Levin et al. (2008) presented an auction based model of the peer selection mechanism. They claimed that the insight behind their model is that BitTorrent uses an auction model to decide which peers to unchoke and not to tit-for-tat as widely believed. 3.4 Limitations of BitTorrent Systems In addition to the analytical models of BitTorrent, some measurement studies have been performed (Guo et al, 2005; Izal et al., 2004; Pouwelse et al., 2005; Piatek et al., 2007; Piatek et al., 2008). Most of these studies were performed on peers that were connected to public torrents. These studies provide interesting results about the overall behavior of deployed BitTorrent systems. The studies also pinpoint several limitations of the BitTorrent protocol. In this section, we discuss two of the limitations of BitTorrent protocol that were shown in these studies: 1) The feasibility of free-riding, 2) The lack of fairness. 3.4.1 Free-Riding in BitTorrent Systems Free riders are the peers who attempt to circumvent the protocol mechanism and download data without uploading data to other peers in the network. Researchers have argued that free- riding in BitTorrent is feasible through the optimistic unchoke mechanism and through seeds. The first to pinpoint that effective free-riding in BitTorrent is feasible was Shneidman et al. (2004). They briefly described a scenario in which peers can attack the tracker while they exploit involved leechers by lying about the pieces they have. Jun et al. (2005) also argued that free- riding is feasible in BitTorrent by investigating the incentives in BitTorrent choking mechanism. Liogkas et al. (2006) showed that free-riding in BitTorrent is feasible by implementing three selfish BitTorrent exploits that allow free-riders to achieve high download rates and evaluate their effectiveness under specific circumstances. Locher et al. (2006) extended these results and presented the BitThief, a free-riding client that combines several attacks. They demonstrated that free-riding is feasible even in the absence of seeds. More recently, Sirivianos et al. (2007) evaluated an exploit based on maintaining a larger-than-normal view of the system, which affords free-riders a much higher probability of receiving data from seeds and optimistic unchokes. They argued that the large view exploit is effective and has the potential for wide adoption. Wide adoption of free-riding strategy can result in a “tragedy of the commons”, where overall performance in the system will decrease. Several works have attacked the ability of free-riders to download data from seeds without uploading in return, intending to considerably hurt free-riders’ performance. Locher et al. (2007) proposed a source-coding scheme. Seeds in this scheme only upload a fixed number of content pieces to each leecher they connect to, thereby placing a hard limit on the data that free-riders can obtain in this manner. Chow et al. (2008) presented an alternative modified seed unchoking algorithm that gives preference to leechers that are either at the beginning or the end of their download. Complementary to these approaches that modify the seed strategy, other works suggested to replace or limit the optimistic unchokes. Izhak-Ratzin et al. (2009) suggested a team mechanism that limits the optimistic unchokes as the collaboration with peers having similar upload rate increases. A foresighted resource reciprocation mechanism was suggested in (Izhak-Ratzin et al., 2009b) to replace the choking mechanism in BitTorrent. Finally, reputation systems such as (Buchegger & Le Boudec, 2004; Xiong & Liu, 2004; Yang et al., 2005) that use a peers’ reputation history to make the choking decision were also suggested in order to help limit the ability to free-ride. 3.4.2 Lack of Fairness in BitTorrent Systems Fairness in BitTorrent is commonly defined as “receive as much as they give.” Fairness among peers participating in content distribution encourages peers to actively collaborate in disseminating content. Thus, fairness is an important factor, which can lead to improved system performance. However, research studies, such as (Guo et al., 2005; Piatek et al., 2007; Bharambe et al., 2006; Legout et al., 2007), show that BitTorrent does not provide fair resource reciprocation, especially in node populations with heterogeneous upload bandwidths. Two of the  the real-time and continuous delivery of content to a large number of participants should be supported in dynamically changing network conditions and groups of interacting peers, and  a higher bandwidth is required in general. Therefore, the media streaming applications require different design rules and approaches that consider such requirements. For example, the objective of the file sharing applications such as BitTorrent is to completely download the content data as fast as possible. Therefore, the timely downloading of data objects is not critical. Rather, downloading data objects that can be rarely found is more important. However, media streaming applications should consider stringent real- time constraints. Thus, a mechanism that enables peers to download data objects that meet the playback deadline is required, such that media streaming is uninterrupted. Second, each data object has an equal importance for file sharing P2P applications, as all the data objects should be completely downloaded. However, for media streaming applications, each data object has different importance based on the coding structure, playback deadline, etc. Thus, data objects that have higher quality impact or that have stringent playback deadline can have higher priority. Therefore, packet prioritization and scheduling mechanisms should be developed in the media streaming applications over P2P networks. Finally, a mechanism that provides graceful quality degradation also needs to be implemented, which enables adaptive and flexible media streaming in dynamic networks where heterogeneous peers having different bandwidth interact with each other. In order to address these challenges, several solutions for media streaming have been proposed in the literature (see e.g., Vlavianos et al., 2006; Zhang et al., 2006; Banerjee et al., 2002; Castro et al., 2003; Deshpande et al., 2001; Trans et al., 2004; Heffeeda et al., 2003; Kostic et al., 2003; Pai et al., 2005; Padmanabhan et al., 2003; Tian et al., 2005; Venkataraman et al., 2006; Zhang et al., 2005b). The implementation of these solutions varies depending on their focuses, which can be broadly classified as 1) peer clustering strategies, 2) overlay constructions and 3) incentive strategies on resource reciprocations. 4.2 Peer Clustering Strategies Several approaches for efficient and reliable delivery of media streams over P2P networks have focused on developing peer clustering strategies. As discussed, the media content is directly shared by peers interacting with each other. Hence, it is important to select good peers and form clusters (or groups) with them. For example, Purandare & Guha (2007) proposed that peers can form groups based on alliance formation process, where this process considers the time and resource constraints. In (Venkataraman et al., 2006), the participating nodes form a graph, where the degree of the graph is determined proportionally to its desired transmission load. In addition, several other strategies such as SpreadIt (Deshpande et al., 2001), NICE (Banerjee et al., 2002), and ZIGZAG (Banerjee et al., 2002) have been proposed, where these algorithms perform hierarchically from clusters such that a minimized transmission delay can be achieved. In order to enhance the efficiency and robustness of the media delivery, various coding structures and packetizing strategies can also be implemented in conjunction with the abovementioned clustering strategies. For instance, Multiple Description Coding, or MDC (Goyal, 2001), is an illustrative example of coding structure for P2P based media delivery, and its deployment for media delivery has been studied in e.g., Padmanabhan et al., 2003; Padmanabhan et al., 2002b; Chu et al., 2004; Li et al., 2004. Alternatively, network coding techniques are also deployed for efficient media delivery (e.g., Nguyen et al., 2007; Wang & Li, 2007; Wang & Li, 2007b; Gkantsidis & Rodriguez, 2005). 4.3 Overlay Constructions: Tree-based and Data-driven Approaches 4.3.1 Tree-based Approach: Description One of design approaches of data dissemination over P2P networks is based on tree structure. In this approach, peers are organized into tree structure, where each peer is participating in disseminating data. In general, peers (or nodes) in the tree are hierarchically organized (i.e., parent – children node relationship) and data is disseminated by typically push-based approach, where the data is forwarded from parent nodes to children nodes. Because the data is disseminated in a structured way, it is important to 1) design optimal tree constructions that provide an efficient performance to each peer, 2) develop approaches for tree construction that are robust to nodes’ (unexpected) joining and leaving trees and provide easy tree repair, and 3) construct non-cyclic trees, i.e., trees that have no loops. Note that one of the major concerns in this approach is the failure of nodes. If a node cannot appropriately perform forwarding data due to node crash or node malfunction, its children nodes in the tree receive no data packets, resulting in significantly poor performance. Moreover, since a significantly large number of nodes are located in the end of the trees, an efficient utilization of outgoing bandwidth of them is challenging. In order to address these issues, several approaches have been proposed (e.g., Chu et al., 2000; Deshpande et al., 2001; Padmanabhan & Sripanidkulchai, 2002; Padmanabhan et al., 2002b; Padmanabhan et al., 2003; Li et al., 2004). In order to successfully implement the tree-based approaches, the following mechanisms have been proposed. 4.3.1.1 Group Management Mechanism To maintain a robust and efficient tree, each peer (or node) may need the information about a set of other nodes and the path from a source. Peers in a tree keep exchanging the information that they have, which may include the group member information, and thus, they can have updated information about the tree. The information exchange can be implemented based on gossip-like protocols (e.g., End System Multicast, ESM, (Chu et al., 2000)). Peers already participating in a tree can switch their parent nodes if the performance (e.g., download rates) achieved in a current position is not satisfied. If a peer newly joins the network (i.e., a tree), it contacts the server, and obtains the (partial) information about the existing peers and the corresponding paths. Then, the peer can determine its parent nodes. Finally, the records of peers that left the tree are simply removed from the group member list. 4.3.1.2 Parent Selection Algorithm As discussed, the way of selecting parents is important for both peers in a tree and newly joining peers. In order to select the parent nodes, a peer can contact a node that is randomly chosen from its group member list. Then, it retrieves the information that includes the currently achieved performance, the number of children nodes, etc. from the contacted node, while estimating the round-trip time. The peer evaluates the contacted node only if the contacted node is not included in its children nodes, and the contacted node has not exceeded its children nodes limit. The evaluation of the contacted node can include the achievable performance and induced delay. Several criteria can be deployed in order to evaluate the node based on goals of applications. For file sharing applications, the performance (i.e., bandwidth) can be one of the most important criteria. For media streaming applications, however, the delay may be the most important criterion for parent selection. Alternatively, the obtained media quality can also be a criterion for parent selection (Park and van der Schaar, 2009d). 4.3.2 Data-driven Approach Alternative approaches for data dissemination over P2P networks are based on data-driven structures (Pai et al., 2005; Zhang et al., 2005). Unlike the tree-based structures, where well- designed trees need to be constructed and continuously maintained for efficient data dissemination in dynamically changing networks, data-driven approaches focus on 1) how to efficiently exchange information about the data availability, 2) how to form groups of peers and share their data, and 3) how to develop group management algorithms and scheduling algorithms. In data-driven approaches, a newly generated data of a peer can be forwarded to its randomly selected neighbor nodes, and the neighbor nodes also forward the data to their neighbor nodes, which can be continued. Finally, the messages can be distributed to all the nodes. This approach can be implemented based on gossip algorithms (Eugster et al., 2004). While such random push approaches enable the P2P systems to be robust to the random node failures, which is one of challenges for tree-based approach, these approaches may result in significant redundancy for receiving nodes (Gkantsidis et al., 2004). In order to reduce the redundancy, pull-based techniques are proposed (Cohen, 2003; Pai et al., 2005; Zhang et al., 2005). In pull-based techniques, peers self-organize into groups and each peer requests necessary data packets from other peers in its group. Thus, each peer can avoid receiving redundant packets from its neighbor peers. The metadata that may include information about the peers can be downloaded from a server (e.g., trackers in BitTorrent systems), and then, each peer forms a group (e.g., swarm) with the other randomly selected peers or joins an existing group. The groups can be maintained by periodically exchanging the information about data availability with the group members. This approach has been actually implemented in BitTorrent (Cohen, 2003), and has shown efficient performance for general file sharing. In this example, a source distributes its content to peer A and peer B, and then, they exchange the content with their group members. Note that peer C also has its own group (which is not shown in this example), which also includes peer A. However, this approach cannot be directly deployed for media streaming applications that require explicit playback deadlines, unless appropriate scheduling algorithms are implemented. Thus, for efficient media streaming over P2P networks based on data-driven approaches, several approaches have been proposed in (Pai et al., 2005; Zhang et al., 2005), where they explicitly 5.1 Challenges of Incentive Design – Cooperation and Competition As discussed in Section 4, one possible solution for providing cooperation incentives is the TFT strategy, which has been actually implemented in practical file sharing P2P systems such as BitTorrent. The TFT strategy is deployed in BitTorrent as a choking algorithm, which effectively encourages peers’ cooperation and penalizes peers’ selfish behaviors (see Section 3.1.2 for more detail). While it has been shown that this strategy performs effectively in various P2P network scenarios, it may not completely eliminate selfish behaviors such as free-riding (Liogkas et al., 2007; Locher et al., 2006; Sirivianos et al., 2007). Moreover, the resource reciprocation based on TFT is myopic, because a peer unchokes some of its associated peers (i.e., leechers) that are currently uploading at the highest rates. Thus, the resource reciprocation decisions based on TFT may not provide optimal solutions that maximize the long-term rewards. Since peers in P2P networks are generally involved in repeated and long-term interactions, such myopic decisions on peer selection and bandwidth allocation can result in a sub-optimal performance for the involved peers. Finally, the TFT strategy in BitTorrent is based on the equal upload bandwidth distribution (Cohen, 2003; Legout et al., 2007) which is neither a fair nor an optimal resource allocation for heterogeneous content and diverse peers with different upload/download requirements (Park & van der Schaar, 2007). To address these challenges, a foresighted resource reciprocation strategy has been recently proposed, which enables the peers to determine their resource reciprocation decisions that maximize a long-term utility. The interactions among the peers are modeled as a stochastic game, where they determine their resource distributions by considering the probabilistically changing future behaviors of their associated peers. The resource reciprocation process of a peer is modeled as a Markov Decision Process (MDP), and the foresighted resource reciprocation strategy is obtained by solving the MDP. It has been shown that the foresighted strategy provides improved performance compared to existing incentive mechanisms in BitTorrent or heuristic algorithms in several media streaming approaches (e.g., Lee et al., 2009). 5.2 Formalization of Resource Reciprocation as Stochastic Games 5.2.1 Group-based Resource Reciprocation Resource reciprocation games in P2P networks are played by the peers interested in each other's media content. A resource reciprocation game is played in a group, where a group consists of a peer and its associated peers. The group-based resource reciprocation has been discussed in the literature, where a group could be formed of swarms (Cohen, 2003; Legout et al., 2006), partnerships (Zhang et al., 2005), or neighbors (Pai et al., 2005). The group members associated with a peer i is denoted by iC , where a peer ik CÎ also has its own group kC which includes peer i . Due to the dynamics introduced by peers joining, leaving, or switching P2P networks, the information about groups needs to be regularly (periodically) updated or it needs to be updated when group dynamics change (Cohen, 2003; Legout et al., 2006). This process is shown in Figure 5. In this diagram, the group dynamics change when group members are changed or their behavior are changed, etc. Moreover, resource reciprocation in Figure 5 may include several modules for its actual implementation, such as an estimation of the state transition probability and a level of accuracy for behavior estimation. These will be discussed in the following sections. Figure 5. Block diagram for group-based resource reciprocation. 5.2.2 Modeling Resource Reciprocations as Stochastic Games The resource reciprocation game in a group iC consists of  a finite set of players (i.e., peers): { }iC iÈ  for each peer { }il C iÎ È , a nonempty set of actions: lA  for each peer { }il C iÎ È , a preference relation (i.e., utility function) of peer l : ()lU ⋅ . To play the resource reciprocation game, a peer can deploy an MDP, defined as follows. For a peer i , an MDP is a tuple , , ,i i i iP RS A , where iS is the state space, iA is the action space, : [0,1]i i i iP ´ ´ S A S is a state transition probability function that maps the state i is Î S at time t , corresponding action i iÎa A and the next state i is ¢ Î S at time 1t + to a real number between 0 and 1, and :i iR S  is a reward function, where ( )i iR s is a reward derived in state i is Î S . This MDP-based resource reciprocation model and its variations in P2P networks have been discussed in (Park & van der Schaar, 2009; Park & van der Schaar, 2009b; Park & van der Schaar, 2009c). We review the details in the following. 5.2.1.1 State Space A state of peer i represents the set of received resources from the peers in iC , which is expressed as { }1( , , ) 0 , Ci i N i ki k ix x x L k C¼ £ £ " Î (1) where kix denotes the provided resources (i.e., rate) by peer k in iC and kL represents the available maximum upload bandwidth of peer k . The total received rates of peer i in iC is thus i ki k C x Î å . Due to the continuity of kix , the cardinality of the set defined in (1) can be infinite. Hence, we assume that peer i has a function iky for peer k , which maps the received resource kix into one of ikn discrete values, i.e., 1( ) { , , }iknik ki ik ik ikx s s sy = Î ¼ . These values are referred to as state descriptions in this chapter. Hence, the state space can be considered to be finite. The state space of peer i can be expressed as { }1( , , ) ( ), , Ci i i i iN ik ik ki is s s s x k Cy= = ¼ = ÎS (2) where l iks denotes the l th segment among kin segments that corresponds to the l th state description of peer i . For simplicity, we assume that each segment represents the uniformly divided total bandwidth, i.e., ( ) l ik ki ikx sy = if ( 1) k k ki ik ik L L x l n n l £ <- for 1 ikl n£ £ . 5.2.1.2 Action Space An action of peer i is its resource allocation to the peers in iC . Hence, the action space of peer i can be expressed as 1{ ( , , ) 0 , 1 , } , C i ii i i i iN ik i C ik ik C a a a L k N a L Î = = ¼ £ £ £ £ £åA a (3) where ik ia AÎ denotes the allocated resources to peer k by peer i in iC . Hence, peer i 's action ika to peer k becomes peer k 's received resources from peer i , i.e., ik ika x= . To consider a finite action space, we assume that the available resources (i.e., upload bandwidth) of peers are decomposed into units of bandwidth (Jain et al., 2007). Thus, the actions represent the number of allocated units of bandwidth to the associated peers in their groups. We define the resource reciprocation as a pair ( )1 1( , ) ( , , ),( , , ) C Ci i i i i iN i iNs a a s s= ¼ ¼a comprising the peer i ’s action, ika , and the corresponding modeled resource reciprocation iks , which is determine as ( )ik ik kis xy= for all ik CÎ . Note that various scheduling schemes can be used in conjunction with the resource allocation (i.e., actions) deployed by peers in order to consider the different priorities of the different data segments (chunks). We assume that the chunks that have higher quality impact on average media quality have higher priority and are transmitted first when each peer takes its actions. However, other scheduling algorithms, such as the rarest first (Cohen, 2003; Legout et al., 2007) method for general file sharing applications or several scheduling methods proposed in e.g., (Zhang et al., 2005) for media streaming applications, can also be adopted. It is important to note that appropriate scheduling schemes need to be deployed in conjunction with our proposed resource reciprocation strategies, depending on the objectives of multimedia applications (e.g. maximizing achieved quality, minimizing the playback delay etc.). 5.2.1.3 State Transition Probability A state transition probability represents the probability that by taking an action, a peer will transit into a new state. We assume that the state transition probability depends on the current state and the action taken by the peer, as peers decide their actions based on their currently received resources (i.e., state). Hence, given a state i is Î S at time t , an action i iÎa A of peer i can lead to another state ' i is Î S at 't ( 't t> ) with probability ' '( , ) Pr( | , ) i i i i i iP s s s s=a a . Hence, for a state 1, )( , Ci i i iNs ss ¼= of peer i in iC , the probability that an action ia leads the state transition from is to ' is can be expressed as ' 1 '( , ) ( , ) Ci i ili i l N a li l iP s s sP s = = a (4) 0 10 20 30 40 50 60 70 7 8 9 10 11 12 13 State Index Immediate Expected Rewards foresighted decision myopic decision TFT decision 0 10 20 30 40 50 60 70 42 44 46 48 50 52 54 State Index Cumulative Discounted Expected Rewards ( =0.7) foresighted decision myopic decision TFT decision Figure 6. Expected rewards achieved by different resource reciprocation strategies 5.4 Bounded Rationality on Resource Reciprocation Conventional stochastic games have been developed based on the implicit assumptions on players’ rationality, where players have the abilities to collect and process relevant information, and select alternative actions among all possible actions. However, peers are often boundedly rational in practice (Simon, 1955; Haruvy, 1999). This is because perfectly rational decisions are often infeasible in practice due to memory and computational constraints. Due to the bounded rationality, peers may have incorrect beliefs on the other players’ behavior and limited ability to analyze their environment. Therefore, it is essential to study the impact of the bounded rationality of peers on 1) the performance degradation of the proposed resource reciprocation strategy and 2) their repeated interactions (resource reciprocation) for practical implementations. We overview several studies, which investigate how the bounded rationality of peers can impact the peers’ interactions and the corresponding performances. 5.4.1 Bounded Rationality: Attitude towards Resource Reciprocations In (Park & van der Schaar, 2009), the bounded rationality is represented by the resource reciprocation attitude of peers, and its impact on resource reciprocation and the corresponding individual peers’ performances is discussed. Peers in the considered P2P networks are characterized based on their attitudes towards the resource reciprocation, which are pessimistic, neutral, or optimistic (Park & van der Schaar, 2009). These characteristics determine how peers can respond to their resource reciprocations. Let ( ),ik ika s be recent resource reciprocation between peer i and peer k , which is a pair of peer i ’s action to peer k and peer k ’s response to peer i . Peer i is referred to as neutral, if it presumes that peer k linearly changes its response (i.e. resource allocation) corresponding to peer i ’s next action ( ' ika ). Peer i is referred to as pessimistic if it presumes that peer k reduces its resource allocation to peer i fast for ' iik ka a£ but increases the resource allocation slowly for ' iik ka a³ . Finally, peer i is optimistic if it presumes that peer k reduces its resource allocation to peer i slowly for ' iik ka a£ but increases the resource allocation fast for ' iik ka a³ . Illustrative examples of these characteristics of resource reciprocation are shown in Figure 7. ika0 X Resource Reciprocation Pessimistic Optimistic iks kL iL Neutral ( , )ik ika s Figure 7. Attitudes for resource reciprocation of boundedly rational peers These types of peers discussed above obviously affect their resource reciprocation strategies. In (Park & van der Schaar, 2009), it has been analytically shown that if a peer has only one attitude for resource reciprocation and makes myopic decisions, it cannot efficiently reciprocate its resources. Thus, it has been concluded that peers can improve their performance by considering various reciprocation attitudes and multiple observations for past resource reciprocations and making foresighted decisions. 5.4.2 Bounded Rationality: Limited Memory and Computation Power In (Park & van der Schaar, 2009b; Park & van der Schaar, 2009c), the bounded rationality of peers induced by their limited memory for storing the resource reciprocation history and limited computation power is discussed. Recall that a peer’s received resources from its associated peers are captured by its state. Boundedly rational peers can have limited ability to characterize their resource reciprocation with other peers (i.e. they can distinguish their received resources using only a limited number of states). This is due to the large complexity requirements associated with their decision making processes. In (Park & van der Schaar, 2009b), it has been investigated how this bounded rationality of peers can impact the accuracy of the long-term expected rewards. It is obvious that using more states (i.e., finer states) enables each peer to compute the actual long-term rewards more accurately. However, increasing the number of states also leads to higher computational complexity to find the optimal resource reciprocation strategy. Therefore, it is important for each peer to find the minimum number of states, while achieving a tolerable accuracy of the actual long-term rewards. In this study, the impact of the number of states on the accuracy of the long- term rewards is analytically quantified, and shows how to determine an optimal number of states that achieves the tolerable accuracy. Alternatively, (Park & van der Schaar, 2009c) has focused on how the heterogeneous peers having different abilities to refine their states can interact with each other, and the corresponding long-term rewards. This study analytically shows that a peer may have multiple actions that are optimal because these actions do not alter its associated peers’ states, and thus, they do not alter the resource reciprocation of these peers. This is because peers cannot differentiate among all possible download rates from their associated peers due to the limited number of state descriptions. It is observed that peers can mutually improve their long-term rewards (i.e., download rates) only if they simultaneously refine their states. It also studies the impact of the heterogeneity of peers on their group formations, and concludes that peers prefer to form groups with other peers, which not only have similar or higher upload rates but also have similar abilities to refine their state descriptions. 5.4.3 Practical Implementation of Foresighted Strategies The foresighted resource reciprocation strategy discussed in Section 5.3 has been actually implemented in BitTorrent-like system. The foresighted strategy replaces the TFT resource reciprocation strategy and the optimistic unchoke mechanism, which have been implemented in the BitTorrent protocol. By deploying the foresighted resource reciprocation strategy, the following advantages against the regular BitTorrent protocol:  It improves the fairness - the peers that contribute more resources (i.e., higher upload capacities) can achieve higher download rates. However, the peers that contribute less resources may achieve limited download rates  It promotes cooperation among high-capacity peers.  It discourages free-riding by limiting the upload to non-cooperative peers.  It improves the system robustness by minimizing the impact of free-riding on contributing peers’ performance. Several illustrative experiment results are shown in the following. The experiments host 54 Planet-Lab nodes, 50 leechers and 4 seeds with combined capacity of 128 KB/s serving a 100 MB file. All peers start the download process simultaneously, which emulate a flash crowd scenario. The initial seeds are stayed connected through the whole experiments. A leecher disconnects immediately after it completes its downloads, and reconnects immediately while requesting the entire file again. This enables our experiments to have the same upload bandwidth distribution during the entire experiment time. Figure 8. Download Completion Time for Leechers Fig. 8 shows the download completion time of leechers. The results show the clear performance difference among high-capacity leechers, which are the fastest 20% leechers, and low-capacity leechers, which are the slowest 80% leechers. High-capacity leechers can significantly improve based file sharing and media streaming applications in detail, and discussed the limitations of their implementations. One of the drawbacks of the existing implementation is induced by a lack of optimal resource reciprocation strategies among self-interested peers. While BitTorrent systems deploy incentive strategies based on TFT strategy, the myopic resource reciprocations based on this strategy provides only a suboptimal performance. More advanced resource reciprocation strategies are discussed, where the resource reciprocation among the interested peers as a stochastic game, and thus, peers can make foresighted decisions on their resource distribution in a way that maximizes their cumulative utilities. This is a desirable property of resource reciprocation strategy, as peers generally are involved in long-term and repeated interactions. Finally, we investigated the impact of the bounded rationality of peers on their resource reciprocation and the corresponding performance. While preliminary results achieved by the foresighted resource reciprocation strategies show that they are promising for file sharing and media streaming, additional modules that can support robust and efficient media streaming should be designed and developed. Then, the foresighted resource reciprocation strategies in conjunction with the supporting modules can ultimately improve the media streaming over P2P networks. Moreover, novel algorithms that can reduce the computational complexity required for deploying the foresighted strategies are still to be developed for real-time media streaming. KEY TERMS AND DEFINITIONS  torrent (or swarm): a collection of end hosts (or peers) participating in the download of content, where content may refer to one or multiple files.  tracker: a server that coordinates and assists the peers in the swarm.  leecher and seed: a peer in the leecher state is still downloading pieces of a content, while a peer in the seed state has a complete set of pieces and is sharing them with other peers.  interested: peer A is interested in peer B if peer B has pieces that peer A does not have and would like to have.  choked: peer A is choked by peer B if peer B decided to provide no pieces to peer A  resource reciprocation: resource reciprocation among peers is a set of resources that they have exchanged. 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