The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Network coding for large scale content distribution
Abstract
We propose a new scheme for content distribution of large files that is based on network coding. With network coding, each node of the distribution network is able to generate and transmit encoded blocks of information. The randomization introduced by the coding process eases the scheduling of block propagation, and, thus, makes the distribution more efficient. This is particularly important in large unstructured overlay networks, where the nodes need to make block forwarding decisions based on local information only. We compare network coding to other schemes that transmit unencoded information (i.e. blocks of the original file) and, also, to schemes in which only the source is allowed to generate and transmit encoded packets. We study the performance of network coding in heterogeneous networks with dynamic node arrival and departure patterns, clustered topologies, and when incentive mechanisms to discourage free-riding are in place. We demonstrate through simulations of scenarios of practical interest that the expected file download time improves by more than 20-30% with network coding compared to coding at the server only and, by more than 2-3 times compared to sending unencoded information. Moreover, we show that network coding improves the robustness of the system and is able to smoothly handle extreme situations where the server and nodes leave the system.
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Cites
Ahlswede, R., Cai, N., Li, S.-Y. R., & Yeung, R. W. (2000) · IEEE Transactions on Information Theory · doi:10.1109/18.850663 · Cited by 7,814
Incentives Build Robustness in Bit-Torrent
Cohen, B. (2003) · Cited by 2,677
A case for end system multicast
Chu, Y.-H., Rao, S. G., Seshan, S., & Zhang, H. (2002) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2002.803066 · Cited by 2,202
Adar, E., & Huberman, B. A. (2000) · First Monday · doi:10.5210/fm.v5i10.792 · Cited by 1,527
Multiple description coding: compression meets the network
Goyal, V. K. (2001) · IEEE Signal Processing Magazine · doi:10.1109/79.952806 · Cited by 1,344
Castro, M., Druschel, P., Kermarrec, A.-M., Nandi, A., Rowstron, A. I. T., & Singh, A. K. (2003) · ACM SIGOPS Operating Systems Review · doi:10.1145/1165389.945474 · Cited by 1,247
Modeling and performance analysis of BitTorrent-like peer-to-peer networks
Qiu, D., & Srikant, R. (2004) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/1015467.1015508 · Cited by 1,201
A digital fountain approach to reliable distribution of bulk data
Byers, J. W., Luby, M., Mitzenmacher, M., & Rege, A. (1998) · Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication · doi:10.1145/285237.285258 · Cited by 1,138
Distributing streaming media content using cooperative networking
Padmanabhan, V. N., Wang, H. J., Chou, P. A., & Sripanidkulchai, K. (2002) · Proceedings of the 12th international workshop on Network and operating systems support for digital audio and video · doi:10.1145/507670.507695 · Cited by 809
Improved Steiner tree approximation in graphs
Robins, G., & Zelikovsky, A. (2000) · doi:10.5555/338219.338638 · Cited by 524
Cited by
Li, S.-Y. R., Yeung, R. W., & Cai, N. (2003) · IEEE Transactions on Information Theory · doi:10.1109/tit.2002.807285 · Cited by 3,378
A survey on content-centric technologies for the current Internet: CDN and P2P solutions
Passarella, A. (2012) · Computer Communications · doi:10.1016/j.comcom.2011.10.005 · Cited by 226
An NDN IoT Content Distribution Model With Network Coding Enhanced Forwarding Strategy for 5G
Lei, K., Zhong, S., Zhu, F., Xu, K., & Zhang, H. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2781372 · Cited by 69
Project Silica: Towards Sustainable Cloud Archival Storage in Glass
Anderson, P., Aranas, E., Assaf, Y., Behrendt, R., Black, R., Caballero, M., Cameron, P., Canakci, B., Chatzieleftheriou, A., Clarke, R., et al. (2025) · ACM Transactions on Storage · doi:10.1145/3708996 · Cited by 13
Self-citation
Metadata
- Authors
- Gkantsidis, C., & Rodriguez, P. R.
- Venue
- Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies.
- Publisher
- IEEE
- Published
- 2005-08-24
- Volume
- 4
- Pages
- 2235-2245
- OpenAlex
- W2107520978
- Cited by
- 1,051
- References
- 18
- FWCI
- 57.16
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Gkantsidis, C., & Rodriguez, P. R. (2005). Network coding for large scale content distribution. Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies. https://doi.org/10.1109/infcom.2005.1498511
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