Conference proceeding
Stable and Efficient Piece-Selection in Multiple Swarm BitTorrent-like Peer-to-Peer Networks
IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS, Vol.2020-, pp.1153-1162
IEEE INFOCOM
01/01/2020
DOI: 10.1109/INFOCOM41043.2020.9155253
Abstract
Recent studies have suggested that the BitTorrent's rarest-first protocol, owing to its work-conserving nature, can become unstable in the presence of non-persistent users. Consequently, in any stable protocol, many peers are at some point endogenously forced to hold off their file-download activity. In this work, we propose a tunable piece-selection policy that minimizes this (undesirable) requisite by combining the (work-conserving) rarest-first protocol with only an appropriate share of the (non-work conserving) mode-suppression protocol. We refer to this policy as "Rarest-First with Probabilistic Mode-Suppression" or simply RFwPMS.
We study RFwPMS under a stochastic model of the BitTorrent network that is general enough to capture multiple swarms of non-persistent users - each swarm having its own altruistic preferences that may or may not overlap with those of other swarms. Using a Lyapunov drift analysis, we show that RFwPMS is provably stable for all kinds of inter-swarm behaviors, and that the use of rarest-first instead of random-selection is indeed more justified. Our numerical results suggest that RFwPMS is scalable in the general multi-swarm setting and offers better performance than the existing stabilizing schemes like mode-suppression.
Details
- Title: Subtitle
- Stable and Efficient Piece-Selection in Multiple Swarm BitTorrent-like Peer-to-Peer Networks
- Creators
- Nouman Khan - University of Michigan–Ann ArborMehrdad Moharrami - University of Michigan–Ann ArborVijay Subramanian - Univ Michigan, ECE Div, EECS Dept, Ann Arbor, MI 48105 USA
- Resource Type
- Conference proceeding
- Publication Details
- IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS, Vol.2020-, pp.1153-1162
- Publisher
- IEEE
- Series
- IEEE INFOCOM
- DOI
- 10.1109/INFOCOM41043.2020.9155253
- ISSN
- 0743-166X
- eISSN
- 2641-9874
- Number of pages
- 10
- Grant note
- 1608361 / NSF via EPCN 1516075 / NSF via EARS Rackham Predoctoral Fellowship from the University of Michigan; University of Michigan System
- Language
- English
- Date published
- 01/01/2020
- Academic Unit
- Computer Science
- Record Identifier
- 9984446549402771
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