Credit Mining: An Incentive and Boosting System in a Peer-to-Peer File-sharing Network

Abstract

Since the dawn of BitTorrent technology, free-riding has always been a critical
issue restricting the performance and availability of the BitTorrent network. To solve this problem, BitTorrent involves a tit-for-tat mechanism which does not function satisfactorily against free-riding. Private trackers implement credit systems to eliminate free-riders and award the good-behaving users. However, due to these factors, the community size of private trackers is limited and not even close to that of famous public trackers. Users have to put considerable efforts to maintain a good credit record, making the experience less enjoyable. Moreover, there exists a majority group of light users who do not bother, do not have the capable knowledge or are not aware of the importance of seeding for the community. Even worse, the hardcore seeders still need to manually download much content and waste considerable resources on over-seeded torrents.
In this thesis, we design, implement and evaluate an incentive and boosting
system namely Credit Mining inside Tribler, an open source Peer-to-Peer file sharing program. Credit Mining involves a private-tracker-like incentive mechanism while maintaining good accessibility for every user. Our results show that we have succeeded in creating a profitable swarm selection algorithm that works in the real world. This thesis is a piece of the puzzle towards the long-term goal of Tribler, "a trustful blockchain-based token economy to prevent bandwidth free-riding".