The performance of peer-to-peer file replication comes from its piece and peer selection strategies. Two such strategies have been introduced by the BitTorrent protocol: the rarest first and choke algorithms. Whereas it is commonly admitted that BitTorrent performs well, recent studies have proposed the replacement of the rarest first and choke algorithms in order to improve efficiency and fairness. In this paper, we use results from real experiments to advocate that the replacement of the rarest first and choke algorithms cannot be justified in the context of peer-to-peer file replication in the Internet. We instrumented a BitTorrent client and ran experiments on real torrents with different characteristics. Our experimental evaluation is peer oriented, instead of tracker oriented, which allows us to get detailed information on all exchanged messages and protocol events. We go beyond the mere observation of the good efficiency of both algorithms. We show that the rarest first algorithm guarantees close to ideal diversity of the pieces among peers. In particular, on our experiments, replacing the rarest first algorithm with source or network coding solutions cannot be justified. We also show that the choke algorithm in its latest version fosters reciprocation and is robust to free riders. In particular, the choke algorithm is fair and its replacement with a bit level tit-for-tat solution is not appropriate. Finally, we identify new areas of improvements for efficient peer-to-peer file replication protocols.
@cite_3 study the file popularity, file availability, download performance, content lifetime and pollution level on a popular tracker site. This work is orthogonal to ours as they do not study the core algorithms of , but rather focus on the contents distributed using and on the users behavior. The work that is the most closely related to our study was done by @cite_29 . In this paper, the authors provide seminal insights into based on data collected from a log for a yet popular torrent, even if a sketch of a local vision from a local peer perspective is presented. Their results provide information on peers behavior, and show a correlation between uploaded and downloaded amount of data. Our work differs from @cite_29 in that we provide a thorough measurement-based analysis of the rarest first and choke algorithms. We also study a large variety of torrents, which allows us not to be biased toward a particular type of torrent. Moreover, without pretending to answer all possible questions that arise from a simple yet powerful protocol as , we provide new insights into the rarest first and choke algorithms.
@cite_29 @cite_3
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In this paper, a simple mathematical model is presented for studying the performance of the BitTorrent (1) file sharing system. We are especially interested in the distribution of the peers with different states of the download job completedness. With the model we find that in the stable state the distribution of the download peers follows a U-shaped curve, and the parameters such as the departure rate of the seeds and the abort rate of the download peers will influence the peer distribution in different ways notably. We also analyze the file availability and the dying process of the BitTorrent file sharing system. We find that the system's stability deteriorates with the clustering of the peers, and BitTorrent's built-in "tit-for-tat" unchoking strategy could not help to preserve the integrity of the file among the download peers when the size of the community is small. An innovative peer selection strategy which enables more peers to finish the download job and prolongs the system's lifetime is proposed, in which the peers cooperate to improve the stability of the system by making a tradeoff between the current download rate and the future service availability. Finally, experimental results are presented to validate our analysis and findings. The observed performance by individual peers in BitTorrent can be simply measured by their average download rate. While it is often stated that the observed peer-level performance by BitTorrent clients is high, it is difficult to accurately verify this claim due to the large scale, distributed and dynamic nature of this P2P system. To provide a "representative" characterization of peer-level performance in BitTorrent, the following two important questions should be addressed: (i) What is the distribution of observed performance among participating peers in a torrent? (ii) What are the primary peer-or group-level properties that determine observed performance by individual peers? In this paper, we conduct a measurement study to tackle these two questions. Toward this end, we derive observed performance for nearly all participating peers along with their main peer-and (peer-view of) group-level properties in three different torrents. Our results show that the probability of experiencing certain level of performance has a roughly uniform distribution across the entire range of observed values. Furthermore, while the performance of each peer has the highest correlation with its outgoing bandwidth, there is no dominant peer-and group-level property that primarily determines the observed performance by the majority of peers. Existing studies on BitTorrent systems are single-torrent based, while more than 85 of all peers participate in multiple torrents according to our trace analysis. In addition, these studies are not sufficiently insightful and accurate even for single-torrent models, due to some unrealistic assumptions. Our analysis of representative Bit-Torrent traffic provides several new findings regarding the limitations of BitTorrent systems: (1) Due to the exponentially decreasing peer arrival rate in reality, service availability in such systems becomes poor quickly, after which it is difficult for the file to be located and downloaded. (2) Client performance in the BitTorrent-like systems is unstable, and fluctuates widely with the peer population. (3) Existing systems could provide unfair services to peers, where peers with high downloading speed tend to download more and upload less. In this paper, we study these limitations on torrent evolution in realistic environments. Motivated by the analysis and modeling results, we further build a graph based multi-torrent model to study inter-torrent collaboration. Our model quantitatively provides strong motivation for inter-torrent collaboration instead of directly stimulating seeds to stay longer. We also discuss a system design to show the feasibility of multi-torrent collaboration. The evolution of social media popularity exhibits rich temporality, i.e., popularities change over time at various levels of temporal granularity. This is influenced by temporal variations of public attentions or user activities. For example, popularity patterns of street snap on Flickr are observed to depict distinctive fashion styles at specific time scales, such as season-based periodic fluctuations for Trench Coat or one-off peak in days for Evening Dress. However, this fact is often overlooked by existing research of popularity modeling. We present the first study to incorporate multiple time-scale dynamics into predicting online popularity. We propose a novel computational framework in the paper, named Multi-scale Temporalization, for estimating popularity based on multi-scale decomposition and structural reconstruction in a tensor space of user, post, and time by joint low-rank constraints. By considering the noise caused by context inconsistency, we design a data rearrangement step based on context aggregation as preprocessing to enhance contextual relevance of neighboring data in the tensor space. As a result, our approach can leverage multiple levels of temporal characteristics and reduce the noise of data decomposition to improve modeling effectiveness. We evaluate our approach on two large-scale Flickr image datasets with over 1.8 million photos in total, for the task of popularity prediction. The results show that our approach significantly outperforms state-of-the-art popularity prediction techniques, with a relative improvement of 10.9 -47.5 in terms of prediction accuracy.