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USENIX, The Advanced Computing Systems Association

OSDI '06 Paper

Pp. 191–204 of the Proceedings

BAR Gossip

Harry C. Li, Allen Clement, Edmund L. Wong, Jeff Napper, Indrajit Roy,
Lorenzo Alvisi, Michael Dahlin
Laboratory for Advanced Systems Research (LASR),
Dept. of Computer Sciences,
The University of Texas at Austin

We present the first peer-to-peer data streaming application that guarantees predictable throughput and low latency in the BAR (Byzantine/Altruistic/Rational) model, in which non-altruistic nodes can behave in ways that are self-serving (rational) or arbitrarily malicious (Byzantine). At the core of our solution is a BAR-tolerant version of gossip, a well-known technique for scalable and reliable data dissemination. BAR Gossip relies on verifiable pseudo-random partner selection to eliminate non-determinism that can be used to game the system while maintaining the robustness and rapid convergence of traditional gossip. A novel fair enough exchange primitive entices cooperation among selfish nodes on short timescales, avoiding the need for long-term node reputations. Our initial experience provides evidence for BAR Gossip's robustness. Our BAR-tolerant streaming application provides over 99% convergence for broadcast updates when all clients are selfish but not colluding, and over 95% convergence when up to 40% of clients collude while the rest follow the protocol. BAR Gossip also performs well when the client population consists of both selfish and Byzantine nodes, achieving over 93% convergence even when 20% of the nodes are Byzantine.

1 Introduction

Streaming media is an increasingly useful application at several scales of deployment. For example, the 2006 NCAA tournament had peak participation of 150k+ users for their live streaming services. At smaller scales, such as academic conferences like OSDI or artistic events like Austin's SXSW that draw audiences of dozens to hundreds of people, there is interest in providing a live stream, but little existing infrastructure to support it.

At all these scales, a peer-to-peer (p2p) streaming solution appears to be an intriguing alternative to traditional methods. One advantage of p2p systems is their potential to be highly robust, scalable, and adaptive. For example, a p2p architecture could, in principle, absorb the impact of an unexpected flash crowd. Furthermore, large-scale content providers may adopt p2p-based solutions to shift costs (like bandwidth) to clients, and small-scale providers might find it simpler to use a self-organizing p2p network instead of provisioning and maintaining a large dedicated server.

Realizing the promises of p2p in streaming services is non-trivial. First, the service must guarantee highly reliable, stable, and timely throughput of messages despite the presence of faulty, misconfigured, or even malicious peers. Second, the service must be robust against selfish users, who try to catch a free ride by receiving streams without contributing their fair share to other users [12]. The free-rider phenomenon in p2p systems is a symptom of a broader issue: any system deployed across multiple administrative domains must be designed for the possibility that nodes will deviate from their specification if doing so is advantageous. File-sharing p2p applications, like BitTorrent [7], have recognized this issue and introduced a set of heuristics to incentivize faithful participation--these heuristics, however, optimize for bulk file transfer, not for the timely delivery of a series of small frames required by a streaming service.

This paper presents the first p2p streaming media application designed for a system model (BAR [1]) in which altruistic nodes (who follow the protocol assigned to them) coexist with both arbitrarily malicious (Byzantine) and self-serving ( rational) nodes. Our BAR-tolerant solution, based on gossip protocols [4,8,19,35], provides a scalable mechanism for information dissemination that ensures predictable throughput even if all of the nodes act selfishly and the remainder act maliciously or malfunction in arbitrary ways.

The defining characteristic of gossip protocols is that each node exchanges data, or gossips, with randomly selected peers: it is precisely this randomness that gives gossip protocols their enviable robustness. From the perspective of designing BAR-tolerant protocols, however, randomness can be a real headache: in fact, any source of non-determinism is hard to deal with in the BAR model because it gives opportunities for rational users to hide selfish actions in the guise of legitimate, non-deterministic behavior.

We overcome this difficulty by building a BAR-tolerant gossip protocol that uses verifiable pseudo-randomness as the means for peer selection: in particular, we exploit properties of pseudo-random number generators and unique signature schemes to build a verifiable pseudo-random partner selection algorithm. This approach eliminates the main source of non-determinism in traditional gossip--randomness in partner selection--yet maintains the unpredictability and rapid convergence of traditional gossip. Our novel peer selection technique, in combination with a simple fair enough exchange mechanism based on the notion of credible threats [1,9], proves effective in encouraging the fair exchange [17] of one node's updates for another's. We believe that enticing cooperation over short timescales is simpler and more robust than approaches based on long-term reputations [16,36,23] because doing so limits Brutus attacks, in which malicious nodes maximize damage by earning the trust of their victims before striking.

We build a prototype streaming application that uses our BAR Gossip protocol to provide a stable throughput multicast. We show that BAR Gossip is robust to Byzantine and selfish behavior, even when 40% of selfish nodes collude. In an environment in which 20% of clients are Byzantine and the remaining are rational, we demonstrate that if the broadcaster can multicast packets to a constant number of random nodes, then non-Byzantine clients can reliably deliver the stream in a timely manner.

In this paper, we make two main technical contributions:

  • We design BAR Gossip, the first gossip protocol resilient to both Byzantine and selfish behavior.
  • We use BAR gossip to build the first p2p streaming application to guarantee predictable throughput and low latency in the BAR model.

More broadly, by showing that it is possible to derive BAR tolerant versions of a highly non-deterministic protocol such as gossip, we believe this work strengthens the case for BAR tolerance as the right model for reasoning about the dependability of systems deployed across multiple administrative domains.

We introduce the paper in Section 1, which ends with this paragraph outlining the rest of the paper. Section 2 frames our contributions in the context of previous work. The system model is described in Section 3. Section 4 then describes BAR Gossip, followed by Section 5, which supports our claim that BAR Gossip is a robust streaming protocol through a combination of simulations and live experiments.

2 Related Work

BAR Gossip targets streaming of live content among Byzantine, altruistic, and rational nodes. It draws on a broad literature of bulk file transfer systems designed to tolerate node misbehavior as well as a large number of efforts to use gossip for robust data dissemination.

Bulk file transfer:

Several systems have addressed selfish behavior in p2p content distribution. BitTorrent [7] leverages a local reputation scheme for file-sharing in which nodes give preference to those peers who have reliably reciprocated in the past.

Scrivener [27] generalizes BitTorrent by supporting a distributed reputation scheme based on credits that can be earned and redeemed across multiple files: through this mechanism, a Scrivener node that has been a good citizen can enlist the help of its peers even if the file it wants to acquire is unpopular.

Habib and Chuang [15] study p2p streaming of non-live media in a selfish environment. They use distributed reputations in the form of global rankings to deter free-riders while providing good quality streams to cooperative clients.

FOX [18] guarantees optimal download time to all the nodes interested in acquiring the same file under the assumption that all nodes are selfish. This guarantee, however, comes at the expense of robustness: the system's incentive structure depends on the fear of mutual assured destruction, and a single Byzantine node can cause the entire system to collapse.

Splitstream [6] is a tree-based multicast protocol that achieves load balancing by dividing content into multiple stripes, each of which is multicast using a separate tree. Splitstream is vulnerable to freeloaders. Ngan et al. [30] observe that if Splitstream's multicast trees are periodically rebuilt and nodes maintain local reputations regarding nodes that have misbehaved, then upstream nodes in a given tree have an incentive to provide good service to downstream nodes to avoid future retaliation.

BAR Gossip differs from these systems in four key ways. First, these systems work to optimize average download bandwidth over long periods of time and do not attempt to maintain stable throughput over shorter intervals. In contrast, our protocol is designed to disseminate live streams and therefore values the highly reliable, stable, and timely throughput that comes with gossip-style data dissemination. Second, several of these systems are designed to be robust to Byzantine [6] or rational [15,18,27] players but not both. Third, all of these systems transfer a large collection of file blocks. In contrast, BAR Gossip distributes live streams and must cope with having a relatively small window of ``useful'' data in flight at any given time; ensuring timely delivery of a small set of data is one of the key challenges in our protocol. Fourth, most of these systems make use of local [7,30] or distributed [27,15] node reputations in their incentive structure. But given our desire to provide stable throughput over short periods of time, relying on a node's long-term reputation to predict its short-term behavior is problematic. Furthermore, because gossip partners are likely to change in every round, in our protocol it is virtually impossible for a node to build enough good will with specific partners to support a purely local reputation scheme à la BitTorrent. Additionally, it appears challenging to implement a strategy-proof reputation system in an environment with both rational and Byzantine players.


Gossip algorithms were first introduced by Demers et al. [8] to manage replica consistency in the Xerox Clearinghouse Service [31]. Following Birman et al.'s highly influential paper on Bimodal Multicast [4], gossip algorithms have established themselves as one of the leading approaches to achieve reliable and scalable application-level multicast [5,11,14,19,25,40]. Gossip protocols are also at the core of a new generation of scalable distributed protocols for failure detection [35], group communication [13,39], and the monitoring and management of large distributed systems [38].

Experience with the CoolStream implementation of the DONet p2p overlay network [41] makes a strong case for the scalability of gossip-based dissemination of live streams and for its potential to deliver high quality end-to-end user experience in the presence of altruistic nodes.

Secure gossiping aims to prevent Byzantine nodes from spreading false updates. While digital signatures are not necessary to accomplish this goal [20,21,22,26], they considerably simplify protocol design and are assumed in most practical gossip-based systems [5,38,39], including BAR Gossip.

DRUM [2] assumes secure gossip and focuses on fighting denial of service (DoS) attacks through two main techniques: bounding the resources allocated to each gossip operation and directing these operations to random ports.

BAR replication:

Aiyer et al. [1] define a replicated state machine protocol under the BAR model. Although the replication overheads of that approach are too high for live streaming media, we draw on many of the same design principles: predictable communication patterns manifest in our partner selection algorithm (Section 4.2.1), cost balancing manifests in our optimistic push algorithm (4.2.5), credible threats manifest in our key exchange (4.2.4), and ensuring long term benefit manifests in our delayed gratification protocol structure (4.1).

3 Model

We consider the problem of streaming a live event across the Internet where the audience members, which we call clients, help disseminate stream packets.

Clients can be Byzantine, altruistic, or rational. Each rational client follows a strategy that maximizes its utility. Rational clients share a utility function that describes costs and benefits; in this paper, the benefit consists in the ability to play the live stream and the costs are incurred by sending and receiving packets. A rational client deviates from the specification if and only if doing so increases that client's expected utility. We assume that a rational client's benefit from timely acquisition of each stream packet significantly exceeds the communication costs incurred in doing so.

We assume rational clients ignore messages outside of the protocol. We root this assumption in the existing game theory literature, which considers extra-protocol messages to be outside the strategy space. Excluding these messages is reasonable because convincing clients to act on messages outside the protocol specified by the broadcaster requires i) convincing clients to install the new protocol in addition to BAR Gossip and ii) ensuring that rational clients expect the protocol to provide current and future updates in a BAR environment without support from the live event's source. If this assumption is violated - e.g. alternative, cheaper, better source of the update appears - then liveness guarantees may be eroded. With this assumption, we can demonstrate that, absent other avenues for receiving the desired updates, rational clients benefit from running our protocol.

Altruistic clients follow the protocol as given regardless of costs, similar to correct clients in the traditional fault-tolerance literature. Byzantine clients behave arbitrarily or according to some unspecified utility function.

Non-Byzantine clients maintain clocks synchronized within $ \delta$ seconds of each other and communicate over point-to-point, synchronous, and unreliable links using both TCP and UDP. When messages are exchanged using UDP, a node that does not receive an expected message assumes that the link dropped it.

We assume that clients subscribe to the live broadcast prior to its start and that non-Byzantine clients remain in the system for the duration of the broadcast. Future work is needed to extend the protocol to accommodate dynamic membership. Given our protocol's reliance on short-term incentives rather than long-term reputations, we are optimistic that such extensions will be feasible.

We also assume that each participant is limited to one identity. To mitigate the threat of Sybil attacks [10], it must be hard for participants to collect multiple identities. There exist sophisticated techniques to combat Sybil attacks [37,41]. In our prototype, we take the simple approach of limiting each IP address to one identity.

We make the standard assumption that cryptographic primitives, like digital signatures, symmetric key encryption, and one-way functions, cannot be subverted. Our protocol also requires that each private key generates unique signatures: for a given $ m$, there must exist exactly one valid signature of $ m$ by $ i$. Although a number of standard signature algorithms fail to provide this property [29], there exist algorithms that do [3]. We denote a message $ m$ signed by $ i$ as $ \langle m \rangle_i$.

We hold clients accountable for the messages they sign. We define a proof of misbehavior (POM) to be a sequence of signed messages that proves a client sent a message inconsistent with the protocol specification. A POM against a client is sufficient evidence to evict that client from the population. Assuming that rational clients gain benefit from being members of the population, we model the system as an infinite game or one with an unpredictable end time so that rational clients are cautious and do not risk eviction by sending POMs. Additional work is needed to determine if there are significant end-game effects for known duration events.

Figure 1: Balanced Exchange and Optimistic Push Protocols for node $ A$ contacting node $ B$. During the update exchange, the upd_list is sent in the Balanced Exchange protocol, but omitted for Optimistic Push.

4 BAR Gossip Design

The BAR Gossip protocol describes a method for an altruistic broadcaster to stream a live event to a pool of clients. Streaming a live event requires that BAR Gossip guarantee two properties: i) non-Byzantine clients do not deliver unauthentic stream packets (i.e., packets not generated by the broadcaster) and ii) every altruistic client receives a large fraction of all stream packets in a timely manner. As we later show, although we can provide the first property in all situations, the second property is elusive, and we can only provide it under good network conditions and with a limited number of Byzantine clients.

Before the start time, each client generates a session key pair consisting of a public and private key. Clients sign up for the event by divulging both keys to the broadcaster. The broadcaster then verifies the keys, closes the sign up service, and posts a list that contains each client's identity, address, and public key. Clients sign protocol messages using their private keys to provide authentication, integrity, and non-repudiation of message contents.

During the live event, the broadcaster divides the stream into discrete fixed-size chunks that we call updates. We structure BAR Gossip as a sequence of rounds of duration $ T+\delta$ where updates are sent by the broadcaster and exchanged among clients. $ T$ is a time interval sufficient to complete the per-round message exchanges required by our protocol. Round zero begins when the live stream starts. Each update expires deadline rounds after it was sent by the broadcaster. When an update expires, all clients that possess that update deliver it to their media players. We consider an update delivered by its deadline to be timely.

In each round $ r$, the broadcaster multicasts ups_per_round updates to subsets of clients. Specifically, for each update, the broadcaster selects nSeeds random clients to receive the update, signs the update, and multicasts it to the selected clients. We require nSeeds to be large enough to guarantee that with high probability at least one receiver is non-Byzantine.

A client is unlikely to receive all updates directly from the broadcaster and relies on two protocols to garner the remaining: Balanced Exchange and Optimistic Push. The Balanced Exchange Protocol allows clients to trade updates one-for-one. That is, if client $ S$ has ten updates to offer client $ R$ and $ R$ has only five to offer in return, then $ S$ and $ R$ trade five updates in each direction. Each balanced exchange is incentive-compatible [33]- rational clients are motivated to follow its steps faithfully because no unilateral deviation from the specified protocol can increase a client's expected utility.

Using the Balanced Exchange Protocol alone, however, is insufficient if a client falls behind in obtaining updates (through bad luck or transient network failures) because that client has little to offer in exchanges. Once behind, a client may continue to fall farther behind. The Optimistic Push Protocol provides a safety net. We call this protocol optimistic because an initiator $ S$ is willing to forward useful updates to $ R$ in the hope, rather than the certainty, that $ R$ will return the favor. In this case, an unequal number of updates may be exchanged- if $ R$ has fallen behind, $ S$ helps $ R$ even if $ R$ cannot fully reciprocate.

Although Optimistic Push is not an incentive-compatible protocol, we structure it to encourage rational clients' participation, and our experimental evidence suggests that a rational client will often benefit from active participation in Optimistic Push. In the next sub-sections, we detail both protocols, prove Balanced Exchange's incentive-compatibility and discuss rational deviations within Optimistic Push. For reference, Figure 1 illustrates both Balanced Exchange and Optimistic Push.

4.1 Balanced Exchange

Balanced Exchange provides an incentive-compatible mechanism for rational clients to exchange updates. In balanced exchanges, each party determines the largest number of new updates it can exchange while keeping the trade equal. A client concurrently executes two tasks: i) initiating an exchange with another client and ii) responding to Balanced Exchange requests from other clients. As Figure 1 details, an exchange consists of four phases. In the first, partner selection, a client selects another client with whom to trade. In the second, history exchange, the two parties learn about the unexpired updates the other party holds and determines the largest number $ k$ of updates that can be exchanged on a one-for-one basis. In the third phase, update exchange, each party deterministically generates an encryption key based upon its private key and a per-exchange seed value. Each party then encrypts its $ k$ most recent exchangeable updates and copies the encrypted updates into a briefcase that is sent to the other party. In the fourth phase, key exchange, the parties swap keys and decrypt the contents of received briefcases. A client ends an exchange early in the history exchange phase if that client realizes the exchange will ultimately trade no updates. An exchange completes if both clients execute all four phases or if one of them ends the exchange early as allowed by the protocol. Clients communicate using TCP in the first three phases, and switch to UDP in the fourth: we discuss the reason for this choice in Section 4.2.4, where we consider the incentives that motivate rational clients to exchange keys.

Proofs of misbehavior (POMs) play an important role in BAR Gossip by ensuring that clients who send internally inconsistent messages risk eviction. Each message in our history and update exchanges includes a cryptographic hash of the previous message sent in that balanced exchange: if a client sends a briefcase whose contents differ from the agreed upon updates of the history exchange, then the history exchange messages plus the briefcase constitutes a POM.

We introduce a trusted agent of the broadcaster to audit possible POMs. In every round, this auditor polices the system by ordering a constant fraction of random clients to supply suspected POMs against other clients. Note that the auditor can decrypt the contents of well-formed briefcase messages because the broadcaster supplies the auditor with every client's private key. If a queried client does not have a POM against a peer, then the client must reply with a dummy message. We specify all audit responses to be of equal size, thereby removing a rational client's incentive to cover up POMs. The auditor treats clients that ignore audit requests as it would clients that have provably misbehaved. To reduce false positives because of transient network failures, the auditor allows sufficient time for a client to respond to audit requests. The auditor evicts a misbehaving client by sending a signed eviction notice to the broadcaster, who embeds all eviction notices in every update and stops sending updates to evicted clients. We discuss in Section 4.4 how to bound the overhead of eviction notices.

Our approach to making Balanced Exchange robust to rational deviations follows two principles: restricted choice and delayed gratification. Restricted choice provides Balanced Exchange's safety property: if a rational client decides to participate in an exchange, then that client sends only messages as prescribed by the protocol. Delayed gratification provides Balanced Exchange's liveness property: if links do not drop messages, then two non-Byzantine clients participating in an exchange will complete that exchange. We delay gratification by postponing a rational client's receipt of useful updates until the last phase, key exchange. In the next section, we prove these properties hold under a reasonable set of assumptions.

4.2 Balanced Exchange Properties

We discuss Balanced Exchange's robustness against rational behavior within the framework of Nash equilibria [28]. In a Nash equilibrium, each client has a strategy and no client benefits from changing its strategy while the other clients keep their strategies unchanged. A limitation of Nash equilibria is that they do not consider rational clients that collude to increase collective utility. We empirically explore the impact of colluding rational clients in Section 5.4, but future work is needed to design gossip protocols with provable guarantees against such collusions.

In our analysis, we assume rational clients only consider strategies that maximize the utility of each exchange independent of concurrent or future exchanges. Our experiments demonstrate that this greedy strategy performs well in a streaming environment where there is a limited time to obtain useful updates. It is possible that more sophisticated strategies optimizing over multiple exchanges are superior to this myopic strategy; analyzing these strategies remains future work. The Balanced Exchange Protocol guarantees the following property:

Theorem 1.   If two rational clients participating in a balanced exchange with each other seek to maximize the utility of that exchange independent of concurrent or future exchanges, then following the Balanced Exchange Protocol is a Nash equilibrium.

In the following sections, we show that each phase of a balanced exchange is a Nash equilibrium, implying that a balanced exchange is also a Nash equilibrium.

In each phase, we show that if a rational client assumes it peers are altruistic, then following Balanced Exchange is in that client's best interest. Note that the assumption that remaining clients are altruistic is an artifact of the Nash equilibria proof technique and is not a requirement of our protocol.

We simplify the presentation of the subsequent lemmas and proofs by treating any client that has issued a POM against itself as evicted. We use the following property regarding eviction in later proofs and include it here for reference.

Lemma 1.   A rational client $ S$ i) never issues a POM against itself and ii) expects no benefit from communicating with evicted clients.

Proof Sketch: i) Because rational clients are cautious by nature, $ S$ does not issue a POM against itself for fear of being evicted.

ii) Suppose for contradiction that there exists an evicted client $ R$ for whom $ S$ expects positive benefit. Since $ S$ assumes all other clients are following the protocol correctly and a client can only be evicted by deviating from the protocol, $ R$ must be an altruistic client that deviates from the protocol, which is a contradiction.$ \Box$

4.2.1 Partner Selection

Problem: What if a rational client selects more partners per round than prescribed or biases its selections instead of choosing partners uniformly at random?

Partner selection highlights a fundamental difference between traditional gossip and gossip in the BAR setting. In a traditional gossip protocol, each client periodically selects a partner using a pseudo-random number generator (PRNG) and contacts that partner to request an exchange. Each client also accepts every request it receives. Random partner selection provides robustness against crashed clients, link failures, and targeted attacks. Yet, in a BAR model, the freedom to choose partners allows rational clients to select multiple partners not at random, thereby dissolving gossip's guarantees. In BAR Gossip, a client generates an unpredictable, deterministic seed for the PRNG to select a random partner, who can then verify and accept the selection using the same PRNG or reject it otherwise.

In BAR Gossip, a client $ S$ selects a partner for round $ r$ by seeding a PRNG with the signature $ \langle r, \textrm{BAL} \rangle_S$. $ S$ then deterministically maps numbers generated by the PRNG to client ids until it finds the first partner $ R$ for which it does not have an eviction notice. This partner selection is deterministic, but unpredictable because no client other than $ S$ can generate $ S's$ signature for a seed value. As Figure 1 illustrates, to initiate a gossip request to $ R$, $ S$ includes the seed and all eviction notices for clients that $ S$ could have selected before $ R$. $ R$ then determines whether the seed is valid by verifying that i) the seed is a valid signature, ii) $ r$ is the current round, iii) all included eviction notices are valid, iv) the seeded PRNG generates $ R$ as the first non-evicted client, and v) this is the first time that $ S$ has presented this seed value to $ R$. If all five tests pass, then $ R$ accepts the gossip request from $ S$; otherwise, the seed is considered invalid and $ R$ aborts the exchange. This selection algorithm provides the following guarantee:

Lemma 2.   Rational clients only send gossip requests to and accept gossip requests from clients as prescribed by valid seeds.

Proof Sketch: A rational client $ S$ may communicate with (a) the uniquely defined target sanctioned by the protocol, (b) an evicted node generated by the PRNG before any non-evicted node, or (c) some other node. By Lemma 1, (b) is not an option. Similarly, (c) is not an option either because $ S$ will not be wrongly contacted by an altruistic client nor would expect an altruistic client to engage in an exchange not sanctioned by the protocol. This leaves option (a) as the only feasible choice for a rational node.$ \Box$

We note that the argument against clients participating in unsanctioned exchanges is buttressed by the specific tangible concern that such exchanges would be done without the recourse of sending a POM to the auditor if either client in an exchange (quite rationally) were to cheat its partner by sending a different briefcase than the one agreed upon.

4.2.2 History Exchanges

Problem: What if a rational client lies about having (or not having) an update?

After a client $ S$ selects a partner $ R$, they exchange histories--a history defines a set of update ids--using three messages. As Figure 1 illustrates, $ S$ provides in the first message a hash of its history $ H_S$ and the PRNG seed value (as discussed earlier) to $ R$; the hash is a verifiable promise to send a given history. After verifying that $ S$ is entitled to communicate with $ R$, $ R$ returns its current history $ H_R$. In the final message, $ S$ divulges its actual history, $ H_S$, to $ R$ who checks that the previously sent hash is consistent with the divulged history. Note that each client sends a history before learning its partner's history: $ S$ does so by sending a unique hash first and $ R$ by sending its actual history while possessing only an irreversible hash. This design promotes equal sharing, as neither client can tailor a history to its partner's. In particular, this design makes it difficult for a Byzantine client to maximize network traffic during the update exchange by sending a history that is the exact complement of its partner's.

Lemma 3.   A rational client $ S$ does not divulge a history that does not match the original hash. Similarly, $ S$ terminates any exchange in which the divulged history does not match the original hash.

Proof Sketch: If $ S$ were to sign messages indicating different histories for the same exchange, then these messages constitute a POM and lead to eviction. By Lemma 1, $ S$, being rational, would not do so.

Lemma 1 also ensures that $ S$ will not communicate with a client that divulges a history not matching the original hash because such actions would result in a POM.$ \Box$

Because update ids are public knowledge, a rational client $ S$ may still consider reporting a history $ H_S^\prime \neq H_S$ to increase the expected utility in an exchange. Applying the principle of balanced cost [1], we define all histories to be of fixed size, thereby removing any incentive $ S$ may have to save a few bytes by sending smaller histories. Therefore, the only way for $ S$ to obtain greater utility is by increasing the number of useful updates $ S$ receives in each exchange. We show that rational clients cannot increase expected utility by lying about histories.

Lemma 4.   Consider a rational client $ S$ and an unexpired update $ U \notin H_S$. If $ S$ participates in an exchange, then $ S$ reports a history $ H_S^\prime$ such that $ U \notin H_S^\prime$.

Proof Sketch: Assume $ S$ deviates from the protocol by falsely claiming $ U \in
H_S^\prime$. $ S$ does so if and only if it expects greater utility without risking eviction. In order to obtain this greater utility by falsely claiming $ U \in H_s^\prime$, $ S$ must send a briefcase message that claims to contain $ U$. Since $ U \notin H_S$, such a briefcase message is a POM. So according to Lemma 1, $ S$ does not send it.$ \Box$

Lemma 5.   Consider a rational client $ S$ and an unexpired update $ U \in H_S$. If $ S$ participates in an exchange, then $ S$ reports a history $ H_S^\prime$ such that $ U \in

Proof Sketch: Claiming $ U \notin H_S^\prime$ decreases the expected number of useful updates to be exchanged. Since a rational client deviates if and only if doing so increases expected utility, $ S$ would not claim $ U \notin H_S^\prime$.$ \Box$

4.2.3 Update Exchange

Problem: What if a rational client places fake or garbage data in briefcase messages?

After the history exchange commits $ S$ and $ R$ to sending the $ k$ most recent updates each possesses but the other lacks, $ S$ and $ R$ send the corresponding updates contained in signed briefcases. Each briefcase message contains i) the seed identifying this exchange, ii) a plaintext description of $ k$ update ids, and iii) the corresponding $ k$ updates encrypted with the hash of both the sender's private key and the exchange's seed value. The sender signs the briefcase, promising that the encrypted contents match the description. If either the received briefcase's seed value does not match the seed identifying this exchange or the briefcase's update list does not match the $ k$ expected updates, the receiver aborts the exchange without sending its decryption key.

Lemma 6.   If a rational client $ S$ sends a briefcase message, then $ S$ includes the appropriate seed value and plaintext description for that exchange.

Proof Sketch: By Lemma 1, $ S$ will not include an inappropriate plaintext description, because the resulting briefcase message and history exchange messages constitute a POM. $ S$ will also include the appropriate seed value and signature because its partner $ R$ is unwilling to accept a briefcase message for which $ S$ does not fear being audited.$ \Box$

$ S$ and $ R$ exchange decryption keys in the next phase. If a client receives a briefcase but not the corresponding decryption key, then the client includes the briefcase as a suspected POM for a future audit response.

Lemma 7.   If a rational client $ S$ sends a briefcase message, then the encrypted contents correspond to the briefcase's plaintext description.

Proof Sketch: A briefcase whose contents differ from the plaintext description is a POM, which according to Lemma 1, $ S$ would never send.$ \Box$

4.2.4 Key Exchange

Problem: What if a rational node chooses not to send the key or sends an invalid key?

A client who is satisfied with its partner's briefcase enters the key exchange phase. In this phase, the client sends via UDP a key request containing the exchange seed and responds to key requests (also via UDP) with a signed response that contains i) the seed value and ii) the decryption key corresponding to the briefcase sent in the previous phase.

The deterministic fair exchange of decryption keys is impossible to solve without a trusted third party [32]. We show that in the setting of BAR Gossip, altruistic and rational clients can exchange keys fairly enough without a trusted third party. The linchpin in providing this is to use a credible threat. A client repeatedly sends key requests, up to some constant number of times, until it obtains a key response from its partner. Note that it is possible to tune the size of key requests to offset any asymmetry between download and upload capacity.

Lemma 8.   If a rational client $ S$ responds to a key request, then $ S's$ response contains the appropriate symmetric key.

Proof Sketch: A key response whose contained key does not match the hash of the seed and sender's private key is a POM, which by Lemma 1, $ S$ does not send.$ \Box$

Lemma 9.   If a rational client $ S$ is satisfied with a client $ R's$ briefcase, then $ S$ responds to $ R's$ key requests.

Proof Sketch: Assume $ S$ ignores $ R's$ key requests. $ S$ deviates because it expects greater utility from doing so. However, since $ R$ is following the protocol, then $ R$ quickly erodes $ S's$ increase in utility by making $ S$ receive multiple key requests.$ \Box$

Lemma 10.   If a rational client $ S$ does not receive a key response from a client $ R$, $ S$ will resend its key request.

Proof Sketch: If $ R$ is following the protocol, $ S$ reasons that the unreliability of UDP is responsible for the delay. $ S$ therefore resends the key request because deviating by keeping silent decreases $ S's$ expected utility.$ \Box$

4.2.5 Optimistic Push

The Optimistic Push Protocol provides a safety net for clients who have fallen behind by allowing clients to obtain missing updates without giving back a set of updates of equivalent value. Optimistic pushes follow the same structure as balanced exchanges. Partner selection is nearly identical. In round $ r$, client $ S$ uses $ \langle
r, \textrm{OPT} \rangle_S$ to seed the PRNG and ultimately selects a partner $ R$ in the same way as in the Balanced Exchange protocol.

The main difference between Balanced Exchange and Optimistic Push lies in what the parties disclose to each other during the history exchange and in how they determine the content of their respective briefcases during the update exchange. In particular, for the history exchange $ S$ forwards to $ R$ two lists: a young list, which contains the identifiers of some of the most recent updates $ S$ knows, and an old list, which contains the identifiers of updates that $ S$ is missing and that are about to expire. If $ R$ has nothing to offer from the old list, $ R$ terminates the exchange. Otherwise, $ R$ replies with a want list, which contains the identifiers of $ c$ updates from the young list that $ R$ is actually missing. $ S$ and $ R$ then exchange briefcases. $ S's$ briefcase contains the $ c$ updates from the want list with an appropriate plaintext description of the update ids. $ R's$ briefcase also contains $ c$ updates, but the plaintext description does not identify the particular updates, only that $ c$ items are inside each of which can be either from the old list or junk, and at least one of those updates is from the old list. $ R$ places up to $ c$ updates from $ S's$ old list inside. If $ R$ has $ b \leq c$ updates from $ S's$ old list, then it includes those $ b$ updates and $ c-b$ junk updates. It is this flexibility to exchange deterministically generated junk data for good data that allows a receiver that has fallen behind to catch up.

We emphasize that junk updates are crafted to be larger than real updates. If junk updates were smaller, the Optimistic Push Protocol would encourage rational clients to deviate from the Balanced Exchange Protocol because updates might be had for cheaper in Optimistic Push. If junk updates were the same size as real updates, a rational client may prefer to send junk to maintain the scarcity of updates in that client's possession.

We regulate Optimistic Push with two parameters, push_age and push_size: the young list consists only of updates that have been broadcast within the last push_age rounds and push_size is an upper limit on the number of updates that the Receiver can place in its want list. Larger values of push_size help lagging clients catch up faster; however, they also increase the likelihood that such clients will waste bandwidth by sending junk.

The Optimistic Push Protocol follows nearly the same steps as the Balanced Exchange Protocol. As a result, the above lemmas (except Lemma 5) apply; a rational client may disingenuously claim to not have an update to reduce the expected number of received junk updates. Although restricted choice still limits the messages that a rational client will send in the Optimistic Push Protocol, the extra flexibility makes faithful participation less certain. For example, rational clients may choose to deviate from the Optimistic Push Protocol by simply not participating, never initiating pushes but responding to them, or sending junk updates in lieu of useful updates.

Although we cannot prove that a rational client would faithfully follow the Optimistic Push Protocol, our experimental evidence, in Section 5, suggests that a rational client obtains greater utility from following the protocol than from deviating.

4.3 Designing for Byzantine behavior

Byzantine behavior is a reality of distributed systems. While enticing rational clients to behave correctly in the presence of Byzantine behavior, we must also limit the negative impact of such behavior on good users of the system. In this paper we limit our attention to Byzantine nodes that exploit the messages and behaviors defined by our protocol.

In BAR Gossip, Byzantine nodes cannot subvert the system's safety properties. Because the broadcaster signs each update, a Byzantine node cannot tamper with the contents of any delivered update. With respect to the liveness property stated in Section 4.1, Byzantine nodes can impair progress by sending two types of messages: non-protocol messages and protocol messages. We regard generic DoS attacks based on non-protocol messages (e.g. bandwidth or connection flooding) as outside the scope of this protocol.

BAR Gossip is designed to be robust against protocol-based attacks on liveness even if initiated by a significant number of Byzantine clients. First, BAR Gossip's peer selection protocol limits the number of nodes that one can contact in a round--unlike traditional gossip, where a Byzantine node could potentially contact an unlimited number of nodes and involve them in useless exchanges. Second, Byzantine clients can inflict limited damage in the exchanges in which they participate. A Byzantine client can remain silent during an exchange to slow the spread of updates, but fortunately, gossip protocols are naturally resilient to crash failures. One remaining concern is that a Byzantine client could impact liveness by luring its partners into expensive message exchanges that do not eventually result in the dissemination of useful updates. We explore this kind of attack in Section 5.5, where we show that altruistic clients still deliver over 93% of updates in a timely manner even when 20% of the clients are Byzantine.

4.4 Optimizations

To increase the practicality of BAR Gossip, we incorporate four optimizations. First, to prevent a client from being overwhelmed by valid gossip requests in a round, we use the standard heuristic that each client accepts requests up to some per round maximum and ignores further requests that round [4,8]. Second, to prevent spikes in used bandwidth, each client in a balanced exchange limits the number of updates that are actually swapped, similar to the Round Retransmission Limit optimization of [4], by including this limit in history exchanges. Third, the broadcaster embeds each eviction notice into a constant number of updates, thereby bounding the overhead of each eviction. With high probability, every client learns of an eviction within deadline rounds. Fourth, clients elide eviction notices that are older than deadline rounds from gossip requests.

5 Evaluation

In this section, we show that BAR Gossip is a robust p2p streaming protocol capable of providing stable and reliable throughput. We evaluate BAR Gossip through experiments and simulations - we denote figures derived from simulation data with "[sim]". Our evaluation demonstrates that BAR Gossip:

  1. Outperforms traditional gossip in the presence of rational clients
  2. Prevents unilateral rational deviation
  3. Is stable in the presence of significant collusion
  4. Tolerates up to $ 20\%$ of the clients being Byzantine

5.1 Methodology

Several parameters regulate the Balanced Exchange and Optimistic Push Protocols. The broadcaster multicasts ups_per_round updates per round and sends each update to nSeeds random clients. Each update expires deadline rounds after it was multicast. In optimistic pushes, push_age denotes the maximum age of updates sent in the young list, while push_size is the maximum length of the want list. The ratio of junk update size to real update size is junk_cost$ >1$. Table 1 provides the values for these parameters for our simulation and prototype experiments. We use lower parameter settings for the simulation, so that our simulator would terminate in a reasonable amount of time. Note that we maintain approximately the same ratio of push_size to ups_per_round in both settings.

Protocol Parameter Simulation Prototype
ups_per_round(updates) 10 98-101
nSeeds(clients) 25 3
deadline(rounds) 10 10
push_size(updates) 2 20
push_age(updates) 3 3
junk_cost 2 1.39
# clients 250 45
Table 1: Parameter settings used in simulations and prototype experiments.

For our prototype evaluations, we implement BAR Gossip in Python to stream an MPEG-4 video [34]. We recorded a 200 Kbps UDP video stream at 30 frames per second using Quicktime Broadcaster with one key frame every 60 frames. Quicktime Broadcaster generates UDP datagrams for the broadcast with an average size of 179 bytes ( $ \sigma = 62$), resulting in 116-131 datagrams per second.

Our broadcaster, auditor, and clients are a mix of 45 600 MHz and 850 MHz Emulab machines sharing a 100 Mbps Ethernet subnet, configured with a 100ms end-to-end latency and 1% probability of any packet being dropped. The broadcaster reads the recorded video from disk, encapsulates on average three UDP datagrams into an update, pads every update to the same size (640 bytes), and unicasts each update using UDP to a random five clients. Clients then exchange updates as in Figure 1. A client delivers an update by extracting the contained datagrams and sending them to the local Quicktime client that displays the video content. We use MD5 to compute cryptographic hashes, RSA with full domain hashing [3] to create unique signatures and the Mersenne Twister algorithm [24] to generate pseudo-random numbers. Each client used on average 299 Kbps of upload bandwidth.

In the following sections we measure the reliability (expressed as the percentage of updates received by the deadline), jitter (measured as the percentage of rounds in which any update missed its deadline), and bandwidth characteristics of BAR Gossip. Unless otherwise noted, measurements in simulations are averaged over 1000 rounds and using the prototype are averaged over 180 rounds across 15 trials. Error bars are small in our data and elided from graphs for clarity.

5.2 Traditional Gossip

We now compare BAR Gossip against a traditional push-pull gossip protocol [8], where each client following the protocol randomly selects one partner per round, exchanges histories, and then exchanges missing updates. We demonstrate that this traditional protocol is ill-suited for a BAR environment even when no client is Byzantine and every rational client initiates only one exchange per round. The intuition here is that a rational client maximizes its utility by not sending updates. Altruistic clients, therefore, do all the work of disseminating information in the system.

Figures 2 and 3 plot the reliability seen and bandwidth used, respectively, by an altruistic client as the proportion of rational clients in the system increases. While BAR Gossip's lines remain relatively constant in both graphs, traditional gossip's degrades noticeably.

Figure 2: [sim] Reliability experienced by an altruistic client using traditional gossip versus BAR Gossip.

We include two lines for BAR Gossip because although we can prove a rational client will faithfully participate in balanced exchanges, we do not have a similar proof for optimistic pushes. The ``BAR Gossip, expected'' line plots the best reliability an altruistic client can see using BAR Gossip, corresponding to the case where rational clients faithfully participate in optimistic pushes. We label the line ``expected'' because our empirical data suggests rational clients obtain greatest utility by actually following the Optimistic Push Protocol. The ``BAR Gossip, worst'' line represents the case where rational clients never initiate optimistic pushes and send as much junk in briefcases as the protocol allows. Among the strategies we consider (see Section 5.3), this second strategy yields the worst reliability for an altruistic client.

Figure 2 shows that a client following BAR Gossip receives almost all updates even when all other clients are rational. In contrast, altruistic clients in traditional gossip experience significantly lower reliability once the number of rational clients exceeds $ 50\%$. When all clients but one are rational, traditional gossip provides only the reliability that the broadcaster can guarantee alone.

Figure 3: [sim] Send bandwidth used by an altruistic client using traditional gossip versus BAR Gossip.

Figure 3 illustrates that in BAR Gossip an altruistic client's consumed bandwidth is nearly independent of the proportion of rational clients. Traditional gossip, on the other hand, requires altruistic clients to shoulder the entire burden of spreading updates, with bandwidth spiking sharply when rational clients account for 70% of the system, before tumbling down to almost nothing. This dramatic fall coincides with the sharp decline in reliability in Figure 2. In these areas of the graphs, rational clients receive the majority of multicast updates and decline to spread them, reducing both reliability and bandwidth devoted to gossiped data.

5.3 Unilateral Rational Deviation

We now examine deviant strategies that a rational client might pursue. In our experiments, a rational client pursues these strategies while the remaining clients continue to follow the protocol as specified. Note that this is the experimental analog to the standard Nash equilibrium proof technique.

In this analysis, we make two simplifying assumptions. First, a rational client's primary concern is to improve the delivered stream's quality by maximizing reliability and minimizing jitter; minimizing consumed bandwidth is a subordinate goal. Second, a rational client missing one or more updates always expects positive utility from participating in a balanced exchange. Under this second assumption, rational clients faithfully execute the Balanced Exchange Protocol. We now consider the choices available to a rational client with respect to Optimistic Push.

Table 2: Six strategies a rational client may follow with regards to the Optimistic Push Protocol.
Strategy Accepts OP Initiates OP Returns
Proactive/Data Yes Yes Data
Proactive/Junk Yes Yes Junk
Proactive/Decline No Yes None
Passive/Data Yes No Data
Passive/Junk Yes No Junk
Passive/Decline No No None

Table 3: Rational client's experienced jitter pursuing different strategies.
Strategy Avg. Jitter Std. Deviation  
Proactive/Data $ 0.48$% $ 1.16$%  
Proactive/Junk $ 0.32$% $ 0.78$%  
Proactive/Decline $ 11.59$% $ 6.22$%  
Passive/Data $ 18.10$% $ 6.08$%  
Passive/Junk $ 14.76$% $ 9.44$%  
Passive/Decline $ 47.94$% $ 7.52$%  

Table 2 lists the five strategies we consider that a rational client may pursue to deviate from the Optimistic Push Protocol. Proactive strategies dictate that a rational client initiates optimistic pushes as specified by the Optimistic Push Protocol. In contrast, passive strategies specify to never initiate optimistic pushes. Data, junk, and decline strategies prescribe that rational clients responding to an optimistic push send useful updates (when possible), send as much junk as allowed, or decline the exchange, respectively. Note that following the Optimistic Push Protocol corresponds to the the Proactive/Data strategy.

Figure 4: Probability of a rational client missing an update for different strategies.

Figure 4 shows for each of the six strategies the probability that the rational client will miss an update, where lower lines correspond to better reliability. Table 3 provides the corresponding jitter for each strategy. When taken together, Figure 4 and Table 3 imply that rational clients will follow either proactive/data or proactive/junk strategies. This is perhaps not surprising, given that proactive strategies perform additional exchanges that are likely to result in more deliverable updates than passive strategies.

Figure 5: Rational client's consumed bandwidth for different strategies.

The tie breaker between the top two strategies comes from Figure 5, in which proactive/data uses an average of 300 Kbps of upload bandwidth compared against proactive/junk's 317 Kbps. This is not an accident: we have designed BAR Gossip with junk_cost$ >1$ so that rational clients prefer filling their briefcases with valuable updates, rather than junk, whenever possible.

The conclusion we draw from this set of experiments is that a rational client, when surrounded by other clients that follow BAR Gossip, has no obvious incentive for deviation--in fact, quite the contrary. While our experiments clearly fall short of proving that BAR Gossip as a whole (Balanced Exchange plus Optimistic Push) constitutes a Nash equilibrium, it does suggest that a Nash equilibrium is likely to be found at or near the strategy that corresponds to BAR Gossip. For instance, while we are unable to prove that there are no beneficial hybrid strategies that, depending on the environment, switch between two or more of the the six strategies we have considered, it appears that the benefit of a proactive strategy derives from consistently participating in more exchanges, making it unlikely that switching occasionally to a passive strategy would provide a net gain. As for switching among proactive strategies, it yields no change in benefit while changing bandwidth costs, also providing little room for improvement.

Overall, we believe that the expected and worst case lines in Figures 2 and 3 provide a reasonable bound on the actual behavior of rational clients in BAR Gossip, and that the likely behavior is near the expected line.

5.4 Rational Collusion

We now explore the effect of multiple rational clients coordinating their actions to maximize their collective utility. We perform a series of simulations to assess the impact such a group may have on clients following the protocol. A complete analysis that defines optimal collusion strategies is future work.

We assume that colluding and non-colluding rational clients share a utility function. We also assume that colluding clients run a private protocol to disseminate updates among themselves. This protocol may be an alternative BAR protocol or it may be a non-BAR protocol bolstered by a high level of trust among colluding clients. We simulate a perfect collusion scenario in which every colluding client immediately broadcasts new updates within the group at no cost. This source of updates reduces the incentive to fully participate in the BAR Gossip protocol. In particular, colluding clients only run the Balanced Exchange protocol.

Figure 6 shows how the size of a perfect collusion group affects the quality of the stream seen by a client following BAR Gossip. The intuition for the degraded performance is i) a non-colluding client trades little when participating in a balanced exchange with a colluding client and ii) colluding clients do not participate in optimistic pushes. In perfect collusion groups, colluding clients get most of their updates for free from other colluding clients, reducing their contributions to the rest of the system.

We find that when the collusion group size reaches 50% of the participants, altruistic clients see an average convergence of 93% for an update, resulting in an unusable stream. Although near-perfect collusion among small groups seems plausible, it is unclear that collusion on a large scale is a significant threat. As the colluding group grows, so do the challenges of coordinating and trusting clients. Ironically, as a colluding group grows, it might require BAR Gossip to distribute updates internally as trust begins to break down among members.

Figure 6: [sim] Effect of collusion on an altruistic client's reliability when colluding clients are following the passive/decline strategy.

5.5 Byzantine Deviation

Rational players behave according to a well-known utility function and represent most of the clients in a p2p system across multiple administrative domains. A few clients, however, may possess unknown utility functions or behave arbitrarily due to ill-will or malfunction, possibly affecting rational behavior [1]. Note that these Byzantine clients may be disinterested in stream packets and may prefer maximizing damage irrespective of consumed bandwidth, allowing strategies like DoS attacks.

To assess BAR Gossip's robustness to Byzantine participation, we explore one malicious goal that a Byzantine client could possess. We assume that this goal is the inverse of any rational player's: to increase the cost and decrease the benefit of all rational clients. We consider Byzantine behavior within the limits of the BAR Gossip protocol, which, as discussed in Section 4.3, limits Byzantine clients to degrading performance only through their own exchanges.

In the following experiments, a Byzantine client provides a history during a balanced exchange that is the complement of its partner's to induce the other client to exchange the maximum number of updates. During an optimistic exchange, a Byzantine client always announces a complete young list and empty old list if initiating, and requests the entire young list if receiving. A Byzantine client never enters the update or key exchange phases, so as not to generate a POM and risk eviction, but still inducing its partner to devote significant bandwidth to the exchange without receiving any benefit. The presence of Byzantine clients can be viewed as an increase in the overhead associated with the environment as the costs associated with Byzantine clients depends upon the probability of entering an exchange with a Byzantine client. To show worst case behavior under this attack, non-Byzantine clients in our experiments ignore previous unproductive exchanges. We elide proactive/decline and passive/decline strategies in which rational clients decline to participate in optimistic pushes.

Figure 7: Rational client's reliability for different strategies.

Figure 8: Rational client's consumed bandwidth for different strategies.

Figures 7 and 8 show the reliability seen and bandwidth used, respectively, by a rational client pursuing each strategy in the presence of different proportions of Byzantine clients. The remaining non-Byzantine clients are altruistic. The choice of strategies is similar to Section 5.3 where we considered unilateral deviation with no Byzantine clients. Passive and proactive strategies deliver unwatchable video streams when the proportion of Byzantine clients reaches 10% and 30%, respectively.

We conclude that among the strategies available, a rational client should follow the protocol (proactive/data) regardless of the presence of Byzantine clients. If all non-Byzantine clients are following the protocol, with a system comprised of 20% Byzantine clients, the bandwidth costs remain relatively constant while the convergence suffers by less than 7%.

6 Conclusion

We present the first peer-to-peer data streaming application with predictable throughput and low latency in the presence of correct, selfish, and malicious nodes. At the core of our application is BAR Gossip, the first gossip protocol defined under the BAR model. We leverage a unique signature scheme to generate verifiable pseudo-random numbers, allowing us to eliminate the opportunity for rational nodes to hide behind nondeterminism without sacrificing the benefits of the random communication pattern of gossip. Our experiments and simulations show that our protocol provides good convergence properties as long as no more than 20% of the nodes are Byzantine or no more than 40% of the nodes collude. In both cases, nodes following the protocol receive more than 93% of updates in a timely manner.

7 Acknowledgements

The authors would like to thank the anonymous reviewers and Brad Chen for shepherding our paper. We would also like to thank Jean-Phillipe Martin, Vitaly Shmatikov and Peter Stone for helpful discussions about game theory, signature schemes, and fair exchange.

This work was supported in part by NSF award CNS 0509338 and NSF CyberTrust award 0430510.


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