NDLI: Boosting topic-based publish-subscribe systems with dynamic clustering

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Boosting topic-based publish-subscribe systems with dynamic clustering

Content Provider	ACM Digital Library
Author	Verbin, Elad Zur, Tal Milo, Tova
Abstract	We consider in this paper a class of Publish-Subscribe (pub-sub) systems called topic-based systems, where users subscribe to topics and are notified on events that belong to those subscribed topics. With the recent flourishing of RSS news syndication, these systems are regaining popularity and are raising new challenging problems. In most of the modern topics-based systems, the events in each topic are delivered to the subscribers via a supporting, distributed, data structure (typically a multicast tree). Since peers in the network may come and go frequently, this supporting structure must be continuously maintained so that "holes" do not disrupt the events delivery. The dissemination of events in each topic thus incurs two main costs: (1) the actual transmission cost for the topic events,and (2) the maintenance cost for its supporting structure. This maintenance overhead becomes particularly dominating when a pub-sub system supports a large number of topics with moderate event frequency; a typical scenario in nowadays news syndication scene. The goal of this paper is to devise a method for reducing this maintenance overhead to the minimum. Our aim is not to invent yet another topic-based pub-sub system, but rather to develop a generic technique for better utilization of existing platforms. Our solution is based on a novel distributed clustering algorithm that utilizes correlations between user subscriptions to dynamically group topics together, into virtual topics (called topic-clusters), andt hereby unifies their supporting structures and reduces costs. Our technique continuously adapts the topic-clusters and the user subscriptions to the system state, and incurs only very minimal overhead. We have implemented our solution in the Tamara pub-sub system. Our experimental study shows this approach to be extremely effective, improving the performance by an order of magnitude.
Starting Page	749
Ending Page	760
Page Count	12
File Format	PDF
ISBN	9781595936868
DOI	10.1145/1247480.1247563
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-06-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Publish-subscribe Peer-to-peer Dynamic clustering
Content Type	Text
Resource Type	Article

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