The Community for Technology Leaders
Parallel and Distributed Processing Symposium, International (2012)
Shanghai, China China
May 21, 2012 to May 25, 2012
ISSN: 1530-2075
ISBN: 978-1-4673-0975-2
pp: 983-994
ABSTRACT
There is an increasing demand for efficient and robust systems able to cope with today's global needs for intensive data dissemination, e.g., media content or news feeds. Unfortunately, traditional approaches tend to focus on one end of the efficiency/robustness design spectrum, by either leveraging rigid structures such as trees to achieve efficient distribution, or using loosely-coupled epidemic protocols to obtain robustness. In this paper we present BRISA, a hybrid approach combining the robustness of epidemic-based dissemination with the efficiency of tree-based structured approaches. This is achieved by having dissemination structures such as trees implicitly emerge from an underlying epidemic substrate by a judicious selection of links. These links are chosen with local knowledge only and in such a way that the completeness of data dissemination is not compromised, i.e., the resulting structure covers all nodes. Failures are treated as an integral part of the system as the dissemination structures can be promptly compensated and repaired thanks to the underlying epidemic substrate. Besides presenting the protocol design, we conduct an extensive evaluation in a real environment, analyzing the effectiveness of the structure creation mechanism and its robustness under faults and churn. Results confirm BRISA as an efficient and robust approach to data dissemination in the large scale.
INDEX TERMS
Peer to peer computing, Robustness, Protocols, Joining processes, Maintenance engineering, Bandwidth, Substrates
CITATION
Miguel Matos, Valerio Schiavoni, Pascal Felber, Rui Oliveira, Etienne Riviere, "BRISA: Combining Efficiency and Reliability in Epidemic Data Dissemination", Parallel and Distributed Processing Symposium, International, vol. 00, no. , pp. 983-994, 2012, doi:10.1109/IPDPS.2012.92
181 ms
(Ver 3.3 (11022016))