Tamás Vinkó

Design space analysis for modeling incentives in distributed systems

Distributed systems without a central authority, such as peer-to-peer (P2P) systems, employ incentives to encourage nodes to follow the prescribed protocol. Game theoretic analysis is often used to evaluate incentives in such systems. However, most game-theoretic analyses of distributed systems do not adequately model the repeated interactions of nodes inherent in such systems. We present a game-theoretic analysis of a popular P2P protocol, Bit-Torrent, that models the repeated interactions in such protocols. We also note that an analytical approach for modeling incentives is often infeasible given the complicated nature of most deployed protocols. In order to comprehensively model incentives in complex protocols, we propose a simulation-based method, which we call Design Space Analysis (DSA). DSA provides a tractable analysis of competing protocol variants within a detailed design space. We apply DSA to P2P file swarming systems. With extensive simulations we analyze a wide-range of protocol variants and gain insights into their robustness and performance. To validate these results and to demonstrate the efficacy of DSA, we modify an instrumented BitTorrent client and evaluate protocols discovered using DSA. We show that they yield higher system performance and robustness relative to the reference implementation.

Fast download but eternal seeding: The reward and punishment of Sharing Ratio Enforcement

Many private BitTorrent communities employ Sharing Ratio Enforcement (SRE) schemes to incentivize users to contribute their upload resources. It has been demonstrated that communities that use SRE are greatly oversupplied, i.e., they have much higher seeder-to-leecher ratios than communities in which SRE is not employed. The first order effect of oversupply under SRE is a positive increase in the average downloading speed. However, users are forced to seed for extremely long times to maintain adequate sharing ratios to be able to start new downloads. In this paper, we propose a fluid model to study the effects of oversupply under SRE, which predicts the average downloading speed, the average seeding time, and the average upload capacity utilization for users in communities that employ SRE. We notice that the phenomenon of oversupply has two undesired negative effects: a) Peers are forced to seed for long times, even though their seeding efforts are often not very productive (in terms of low upload capacity utilization); and b) SRE discriminates against peers with low bandwidth capacities and forces them to seed for longer durations than peers with high capacities. To alleviate these problems, we propose four different strategies for SRE, which have been inspired by ideas in social sciences and economics. We evaluate these strategies through simulations. Our results indicate that these new strategies release users from needlessly long seeding durations, while also being fair towards peers with low capacities and maintaining high system-wide downloading speeds.

BitTorrent-like P2P approaches for VoD: A comparative study

The enormous popularity of Video on Demand (VoD) has attracted substantial research attention into the effective use of peer-to-peer (P2P) architectures to provide solutions at large-scale. In particular, the high efficiency of BitTorrent has inspired many P2P protocols for VoD. However, these protocols use different approaches to adapt the design of Bittorrent to VoD, and in most cases their performance has been evaluated separately and in limited scenarios. As a consequence, the research community still lacks a clear understanding of how these protocols compare against each other and how well each of them would work in real world conditions, where, for instance, peers have heterogeneous bandwidths, may freeride or may be located behind NAT/firewall. In this paper, we propose a simulation based methodology which aims at putting forward a common base for comparing the performance of these different protocols under a wide range of conditions. We show that, despite their considerable differences: (i) existing BitTorrent-like VoD approaches all share some characteristics, such as that their bandwidth reciprocity based methods to incentivize cooperation do not always yield an optimal overall performance. Furthermore, we demonstrate that (ii) in these protocols there is a trade-off between QoS and resilience to freeriding and malicious attacks. We also discover that, (iii) when peers doing streaming coexist with peers doing traditional file transfer, the latter actually benefit from this coexistence, at the expenses of the former. Finally, we show that (iv) early departures of peers from the system do not significantly affect the QoS delivered, while jumping to a different position in the file has a bigger negative impact. Overall, our findings provide important implications for both VoD service providers and future system designers. On the one hand, our results can guide VoD service providers in selecting the most appropriate protocol for a given environment. On the other hand, exposing the flaws of current approaches will help researchers in improving them and/or designing better ones.

No more crash or crunch: Sustainable credit dynamics in a P2P community

Many peer-to-peer file sharing communities implement credit policies to incentivise users to contribute upload resources. Such policies implicitly assume a user model - how the user controlling each peer behaves. We show using an agent-based model that credit policies, based on bandwidth contribution, and a selfish user model, can lead to both “crunches” and “crashes” where the system seizes completely due to too little credit or too much credit. We explore the conditions that lead to these system pathologies and present a theoretical analysis that allows us to determine if a community is sustainable or will eventually crunch or crash. Finally we apply the analysis to produce a novel adaptive credit system that automatically adjusts credit policies to maintain sustainability.

Inter-swarm resource allocation in BitTorrent communities

A considerable body of research shows that Bit-Torrent provides very efficient resource allocation inside single swarms. Many BitTorrent clients also allow users to participate in multiple swarms simultaneously, and implement inter-swarm resource-allocation mechanisms that are used by millions of people. However, resource allocation across multiple swarms in BitTorrent has received much less attention. In this paper, we investigate whether currently prevalent inter-swarm resource allocation mechanisms perform acceptably or call for improvements. We use data from two BitTorrent communities and present results from trace-based simulations. Two use-cases for allocation mechanisms drive our evaluation: (1) file-sharing communities, whose objective is maximizing throughput, and (2) video-streaming communities, whose objective is maximizing the number of users receiving sufficient resources for uninterrupted streaming. To put the results from the analyzed mechanisms into perspective, we devise theoretical efficiency bounds for inter-swarm resource allocation, for which we map the resource allocation problem to a graph-theoretical flow network problem. In this formalism, the goal of the file-sharing use-case, throughput maximization, is equivalent to maximizing the flow in the network. The goal of the video-streaming use-case translates into finding a max-min fair allocation for BitTorrent downloading sessions, a problem for which we devise a new algorithm.

Bandwidth allocation in BitTorrent-like VoD systems under flashcrowds

The efficiency of BitTorrent in content distribution has inspired a number of peer-to-peer (P2P) protocols for on-demand video (VoD) streaming systems (henceforth BitTorrent-like VoD systems). However, the fundamental quality-of-service (QoS) requirements of VoD (i.e. providing peers with a smooth playback continuity and a short startup delay) make the design of these systems more challenging than normal file-sharing systems. In particular, the bandwidth allocation strategy is an important aspect in the design of BitTorrent-like VoD systems, which becomes even more crucial in a scenario where a large number of peers joins in a short period of time, a phenomenon known as flashcrowd. In fact, the new joining peers all demand for content while having few or no pieces of content to offer in return yet. An unwise allocation of the limited bandwidth actually available during this phase may cause peers to experience poor QoS. In this work, we analyze the effects of a flashcrowd on the scalability of a BitTorrent-like VoD system and propose a number of mechanisms to make the bandwidth allocation in this phase more effective. In particular, we derive an upper bound for the number of peers that can be admitted in the system over time and we find that there is a trade-off between having the seeders minimize the upload of pieces already injected recently and high peer QoS. Based on the insights gained from our analysis, we devise some flashcrowd-handling algorithms for the allocation of peer bandwidth to improve peer QoS during flashcrowd. We validate the effectiveness of our proposals by means of extensive simulations.

Do BitTorrent-Like VoD Systems Scale under Flash-Crowds?

The efficiency of BitTorrent for file sharing has inspired a number of BitTorrent-based P2P protocols for Video-on-Demand (VoD). It has been shown that these systems are scalable in steady-state: the service quality provided to the users does not depend on the number of users in the system. However, it is not well understood how these systems scale under flash-crowds. In this work, we model a general BitTorrent-like VoD system and we find that under a flash-crowd the quality-of-service (QoS) degrades with the number of users. Also, our analysis shows that, at the very beginning of a flash-crowd, the maximum number of simultaneous users that can obtain a given service level is intrinsically related to two fundamental system parameters, namely the initial service capacity and the efficiency of piece exchange of the underlying P2P protocol. Finally, we illustrate the impact of peers turning into seeders (i.e peers that have finished downloading and remain in the system to upload) on the system scale.

Leveraging node properties in random walks for robust reputations in decentralized networks

Reputation systems are essential to establish trust and to provide incentives for cooperation among users in decentralized networks. In these systems, the most widely used algorithms for computing reputations are based on random walks. However, in decentralized networks where nodes have only a partial view of the system, random walk-based algorithms can be easily exploited by uncooperative and malicious nodes. Traditionally, a random walk only uses information about the adjacency of nodes, and ignores their structural and temporal properties. Nevertheless, the properties of nodes indicate their reliability, and so, random walks using much richer information about the nodes than simple adjacency may achieve higher robustness against malicious exploitations. In this paper, we introduce the properties of nodes that are indicative of their reliability, and we propose a scheme to integrate these properties into the traditional random walks. Particularly, we consider two common malicious exploitations of random walks in decentralized networks, uncooperative nodes and Sybil attacks, and we show that integrating node properties into random walks results in much more robust reputation systems. Our experimental evaluation in synthetic graphs and graphs derived from real-world networks covering a significant number of users, shows the effectiveness of the resulting biased random walks.

Reducing the history in decentralized interaction-based reputation systems

In decentralized interaction-based reputation systems, nodes store information about the past interactions of other nodes. Based on this information, they compute reputations in order to take decisions about future interactions. Computing the reputations with the complete history of interactions is inefficient due to its resource requirements. Furthermore, the complete history of interactions accumulates old information, which may impede the nodes from capturing the dynamic behavior of the system when computing reputations. In this paper, we propose a scheme for reducing the amount of history maintained in decentralized interaction-based reputation systems based on elements such as the age of nodes, and we explore its effect on the computed reputations showing its effectiveness in both synthetic and real-world graphs.

Characterizations of trajectory structure of fitness landscapes based on pairwise transition probabilities of solutions

Characterization of trajectory structure of fitness landscapes is a major problem of evolutionary computation theory. In this paper a hardness measure of fitness landscapes is introduced which is based on statistical properties of trajectories. These properties are approximated with the help of a heuristic based on the transition probabilities between the elements of the search space. This makes it possible to compute the measure for some well-known functions: a ridge function, a long path function, a fully deceptive function and a combinatorial problem: the subset sum problem. Using the same transition probabilities the expected number of evaluations needed to reach the global optimum from any point in the space are approximated and examined for the above problems.