The DOI matches its Crossref record.
A digital fountain approach to reliable distribution of bulk data
Abstract
The proliferation of applications that must reliably distribute bulk data to a large number of autonomous clients motivates the design of new multicast and broadcast protocols. We describe an ideal, fully scalable protocol for these applications that we call a digital fountain. A digital fountain allows any number of heterogeneous clients to acquire bulk data with optimal efficiency at times of their choosing. Moreover, no feedback channels are needed to ensure reliable delivery, even in the face of high loss rates. We develop a protocol that closely approximates a digital fountain using a new class of erasure codes that are orders of magnitude faster than standard erasure codes. We provide performance measurements that demonstrate the feasibility of our approach and discuss the design, implementation and performance of an experimental system. 1 Introduction Software companies that plan to efficiently disseminate new software over the Internet to millions of users simultaneously will ...
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Online selection of discriminative tracking features
Collins, R. T., Liu, Y., & Leordeanu, M. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.205 · Cited by 1,208
Network coding for large scale content distribution
Gkantsidis, C., & Rodriguez, P. R. (2005) · Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies. · doi:10.1109/infcom.2005.1498511 · Cited by 1,051
A digital fountain approach to asynchronous reliable multicast
Byers, J. W., Luby, M., & Mitzenmacher, M. (2002) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2002.803996 · Cited by 580
Self-citationSocially-aware routing for publish-subscribe in delay-tolerant mobile ad hoc networks
Costa, P., Mascolo, C., Musolesi, M., & Picco, G. (2008) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2008.080602 · Cited by 478
Carbon-based archiving: current progress and future prospects of DNA-based data storage.
Ping, Z., Ma, D., Huang, X., Chen, S., Liu, L., Guo, F., Zhu, S. J., & Shen, Y. (2019) · GigaScience · doi:10.1093/gigascience/giz075 · Cited by 55
Metadata
- Authors
- Byers, J. W., Luby, M., Mitzenmacher, M., & Rege, A.
- Venue
- Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
- Publisher
- ACM
- Published
- 1998-10
- Pages
- 56-67
- OpenAlex
- W2161342511
- Cited by
- 1,138
- References
- 0
- FWCI
- 18.41
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
