Bittorrent is one of the most popular and successful applications in the current Internet. However, we still have little knowledge about the topology of real Bittorrent swarms, how dynamic the topology is, and how it affects overall behavior. This paper describes an experimental study of the overlay topologies of real-world Bittorrent networks, focusing on the activity of the nodes of its P2P topology and especially their dynamic relationships. Peer Exchange Protocol (PEX) messages are analyzed to infer topologies and their properties, capturing the variations of their behavior. Our measurements, verified using the Kolmogorov-Smirnov goodness of fit test and the likelihood ratio test and confirmed via simulation, show that a power-law with exponential cutoff is a more plausible model than a pure power-law distribution. We also found that the average clustering coefficient is very low, supporting this observation. Bittorrent swarms are far more dynamic than has been recognized previously, potentially impacting attempts to optimize the performance of the system as well as the accuracy of simulations and analyses.

This paper investigates modified random timers based on uniform and exponentially distributed timers for feedback scalability for large groups. We observe the widely-used probability distribution functions and propose new ones that are aware of network delays. The awareness of network delays of our proposed modified p.d.fs proves to be able to achieve lower expected number of messages compared to the original ones given that the parameters are optimized for the network variables: the number of receivers, and the network delay. In our analysis we derive an equation to estimate the optimized parameter based on these network variables. We also simulate the p.d.fs for heterogenous network delays and find that each receiver only needs to be aware of its network delay.

This paper presents a method to monitor information of a large-sized multicast group that can follow the group's dynamics in real-time while avoiding feedback implosion by using probabilistic polling. In particular, this paper improves the probabilistic-polling-based approach by deriving a reference mean value as the reference control value for the number of expected feedback from the properties of a binomial estimation model. As a result, our method adaptively changes its estimation parameters depending on the feedback from receivers in order to achieve a fast estimate time with high accuracy, while preventing the possible occurrence of feedback implosion. Our experimental implementation and evaluation on PlanetLab showed that the proposed method effectively controls the number of feedback and accurately estimates the size of a dynamic multicast group.