Magnet: Practical Subscription Clustering for Internet-Scale Publish/Subscribe. / Girdzijauskas, Sarunas; Chockler, Gregory; Vigfusson, Ymir; Melamed, Roie; Tock, Yoav.

Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems (DEBS '10). Cambridge, UK : ACM, 2010. p. 172-183.

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Abstract

An effective means for building Internet-scale distributed applications, and in particular those involving group-based information sharing, is to deploy peer-to-peer overlay networks. The key pre-requisite for supporting these types of applications on top of the overlays is efficient distribution of messages to multiple subscribers dispersed across numerous multicast groups. In this paper, we introduce Magnet: a peer-to-peer publish/subscribe system which achieves efficient message distribution by dynamically organizing peers with similar subscriptions into dissemination structures which preserve locality in the subscription space. Magnet is able to significantly reduce the message propagation costs by taking advantage of subscription correlations present in many large-scale group-based applications. We evaluate Magnet by comparing its performance against a strawman pub/sub system which does not cluster similar subscriptions by simulation. We find that Magnet outperforms the strawman by a substantial margin on clustered subscription workloads produced using both generative models and real application traces.
Original languageEnglish
Title of host publicationProceedings of the Fourth ACM International Conference on Distributed Event-Based Systems (DEBS '10)
Place of PublicationCambridge, UK
PublisherACM
Pages172-183
Number of pages12
ISBN (Print)978-1-60558-927-5
DOIs
Publication statusPublished - Jul 2010
EventFourth ACM International Conference on Distributed Event-Based Systems - Cambridge, United Kingdom
Duration: 12 Jul 201015 Jul 2010

Conference

ConferenceFourth ACM International Conference on Distributed Event-Based Systems
CountryUnited Kingdom
CityCambridge
Period12/07/1015/07/10
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 10507656