DocumentCode
737856
Title
Scaling Up Publish/Subscribe Overlays Using Interest Correlation for Link Sharing
Author
Matos, Miguel ; Felber, Pascal ; Oliveira, Renato ; Pereira, Jose Orlando ; Riviere, Etienne
Author_Institution
IHASLab - HighAssurance Software Lab., Univ. do Minho, Braga, Portugal
Volume
24
Issue
12
fYear
2013
Firstpage
2462
Lastpage
2471
Abstract
Topic-based publish/subscribe is at the core of many distributed systems, ranging from application integration middleware to news dissemination. Therefore, much research was dedicated to publish/subscribe architectures and protocols, and in particular to the design of overlay networks for decentralized topic-based routing and efficient message dissemination. Nonetheless, existing systems fail to take full advantage of shared interests when disseminating information, hence suffering from high maintenance and traffic costs, or construct overlays that cope poorly with the scale and dynamism of large networks. In this paper, we present StaN, a decentralized protocol that optimizes the properties of gossip-based overlay networks for topic-based publish/subscribe by sharing a large number of physical connections without disrupting its logical properties. StaN relies only on local knowledge and operates by leveraging common interests among participants to improve global resource usage and promote topic and event scalability. The experimental evaluation under two real workloads, both via a real deployment and through simulation, shows that StaN provides an attractive infrastructure for scalable topic-based publish/subscribe.
Keywords
Internet; information dissemination; message passing; middleware; overlay networks; protocols; StaN; application integration middleware; decentralized protocol; gossip-based overlay networks; information dissemination; link sharing; overlay networks design; publish-subscribe architectures; topic-based publish-subscribe; Clustering methods; Scalability; Subscriptions; Publish and subscribe; interest correlation; link sharing; scalability; subscription clustering; topic-based;
fLanguage
English
Journal_Title
Parallel and Distributed Systems, IEEE Transactions on
Publisher
ieee
ISSN
1045-9219
Type
jour
DOI
10.1109/TPDS.2013.6
Filename
6409838
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