We propose a BitTorrent-like protocol based on an online learning (reinforcement learning) mechanism, which can replace the peer selection mechanisms in the regular BitTorrent protocol. We model the peers' interactions in the BitTorrent-like network as a repeated stochastic game, where the strategic behaviors of the peers are explicitly considered. A peer that applies the reinforcement learning (RL)-based mechanism uses the observations on the associated peers' statistical reciprocal behaviors to determine its best responses and estimate the corresponding impact on its expected utility. The policy determines the peer's resource reciprocations such that the peer can maximize its long-term performance. We have implemented the proposed mechanism and incorporated it into an existing BitTorrent client. Our experiments performed on a controlled Planetlab testbed confirm that the proposed protocol 1) promotes fairness and provides incentives to contributed resources, i.e., high capacity peers improve their download completion time by up to 33 percent, 2) improves the system stability and robustness, i.e., reduces the peer selection fluctuations by 57 percent, and (3) discourages free-riding, i.e., peers reduce their uploads to free-riders by 64 percent as compared to the regular BitTorrent protocol.

In this paper, we propose a BitTorrent-like protocol that replaces the peer selection mechanisms in the regular BitTorrent protocol with a novel reinforcement learning based mechanism. The inherent operation of P2P systems, which involves repeated interactions among peers over a long time period, allows peers to efficiently identify free-riders as well as desirable collaborators by learning the behavior of their associated peers. Thus, it can help peers improve their download rates and discourage free-riding (FR), while improving fairness. We model the peers' interactions in the BitTorrent-like network as a repeated interaction game, where we explicitly consider the strategic behavior of the peers. A peer that applies the reinforcement learning based mechanism uses a partial history of the observations on associated peers' statistical reciprocal behaviors to determine its best responses and estimate the corresponding impact on its expected utility. The policy determines the peer's resource reciprocations with other peers, which would maximize the peer's long-term performance.

Although the popular BitTorrent protocol strives to limit free-riding via its tit-for-tat incentives, recent research efforts have shown that it does not strictly enforce fairness. Free-riding opportunities indeed exist, mainly via optimistic unchokes, a BitTorrent mechanism that facilitates the continuous discovery of better peers to interact with. Our results in this work also show that increasing numbers of free-riders can considerably hurt the performance of compliant peers. In an effort to address this problem, this paper proposes a new BitTorrent-like protocol that dynamically organizes peers of similar upload bandwidth in teams-groups of peers collaborating for mutual benefit. Team members mostly satisfy their data download needs inside their team and only perform optimistic unchokes when absolutely necessary. As a result, this team-based protocol improves peer performance via explicit cooperation. At the same time, it limits bandwidth spent on optimistic unchokes, thereby rendering the system more robust against free-riders. We have modified an existing BitTorrent implementation to implement the team protocol and have evaluated its impact by running real experiments on a controlled PlanetLab testbed. Our results show that the protocol enables slightly improved performance for compliant peers, while free-riders are unable to sustain high download rates, as compared to regular BitTorrent. In addition, we observe a higher degree of robustness: increasing numbers of free-riders in the system have a significantly lower negative impact on the performance of contributing peers.

A number of previous measurement studies [10, 12, 17] have shown the existence of path exploration and slow convergence in the global Internet routing system, and a number of protocol enhancements have been proposed to remedy the problem [21, 15, 4, 20, 5]. However all the previous measurements were conducted over a small number of testing prefixes. There has been no systematic study to quantify the pervasiveness of BGP slow convergence in the operational Internet, nor there is any known effort to deploy any of the proposed solutions.In this paper we present our measurement results from identifying BGP slow convergence events across the entire global routing table. Our data shows that the severity of path exploration and slow convergence varies depending on where prefixes are originated and where the observations are made in the Internet routing hierarchy. In general, routers in tier-1 ISPs observe less path exploration, hence shorter convergence delays than routers in edge ASes, and prefixes originated from tier-1 ISPs also experience less path exploration than those originated from edge ASes. Our data also shows that the convergence time of route fail-over events is similar to that of new route announcements, and significantly shorter than that of route failures, which confirms our earlier analytical results [19]. In addition, we also developed a usage-time based path preference inference method which can be used by future studies of BGP dynamics.

We conduct a systematic study on the pervasiveness and persistency of one specific phenomenon in the global routing system: a small set of highly active prefixes accounts for a large number of routing updates. Our data analysis shows that this phenomenon is commonly observed from monitors in many different ISPs, and exists throughout our 3-year study period. The analysis further shows that the majority of these prefixes are highly active for only one or a few days, while a small number of them are persistently active over long period of time. Case studies demonstrate that the causes of these high routing activity include topological failures, BGP path exploration, protocol defects, and the failure of turning on protection mechanisms.