Investment strategies for credit-based P2P communities
| Autor: | Capota, M., Andrade, N., Pouwelse, J.A., Epema, D.H.J., Kilpatrick, P., Milligan, P., Stotzka, R. |
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| Přispěvatelé: | Mathematics and Computer Science, Interconnected Resource-aware Intelligent Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
business.industry
Computer science Investment strategy Download Internet privacy 020206 networking & telecommunications 02 engineering and technology computer.file_format Investment (macroeconomics) Computer security computer.software_genre 01 natural sciences 010104 statistics & probability Upload Investment decisions Return on investment 0202 electrical engineering electronic engineering information engineering Cache 0101 mathematics business computer BitTorrent |
| Zdroj: | 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2013, Belfast, United Kingdom, February 27-March 1, 2013), 437-443 STARTPAGE=437;ENDPAGE=443;TITLE=21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2013, Belfast, United Kingdom, February 27-March 1, 2013) PDP |
| DOI: | 10.1109/PDP.2013.70 |
| Popis: | P2P communities that use credits to incentivize their members to contribute have emerged over the last few years. In particular, private BitTorrent communities keep track of the total upload and download of each member and impose a minimum threshold for their upload/download ratio, which is known as their sharing ratio. It has been shown that these private communities have significantly better download performance than public communities. However, this performance is based on oversupply, and it has also been shown that it is hard for users to maintain a good sharing ratio to avoid being expelled from the community. In this paper, we address this problem by introducing a speculative download mechanism to automatically manage user contribution in BitTorrent private communities. This mechanism, when integrated in a BitTorrent client, identifies the swarms that have the biggest upload potential, and automatically downloads and seeds them. In other words, it tries to invests the bandwidth of the user in a profitable way. In order to accurately asses the upload potential of swarms we analyze a private BitTorrent community and derive through multiple regression a predictor for the upload potential based on simple parameters accessible to each peer. The speculative download mechanism uses the predictor to build a cache of profitable swarms to which the peer can contribute. Our results show that 75 % of investment decisions result in an increase in upload bandwidth utilization, with a median 207 % return on investment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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