Self-optimizing Hybrid Routing in Publish/Subscribe Systems

Schröter, Arnd; Graff, Daniel; Mühl, Gero; Richling, Jan; Parzyjegla, Helge

doi:10.1007/978-3-642-04989-7_9

Cited by 6 publications

(2 citation statements)

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“…Finally, the models will be extended to support more sophisticated routing algorithms, like hybrid routing [22] or algorithms based on advertisements. While hybrid routing allows to apply different routing algorithms for different overlay links in the same publish/subscribe topology, advertisements allow the propagation of subscriptions to be restricted to those parts of the overlay network where matching notifications can potentially be produced.…”

Section: Future Directionsmentioning

confidence: 99%

Stochastic performance analysis and capacity planning of publish/subscribe systems

Schröter

Mühl

Kounev

et al. 2010

Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems

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Publish/subscribe systems are used increasingly often as a communication mechanism in loosely-coupled distributed applications. With their gradual adoption in mission critical areas, it is essential that systems are subjected to a rigorous performance analysis before they are put into production. However, existing approaches to performance modeling and analysis of publish/subscribe systems suffer from many limitations that seriously constrain their practical applicability. In this paper, we present a set of generalized and comprehensive analytical models of publish/subscribe systems employing different peer-to-peer and hierarchical routing schemes. The proposed analytical models address the major limitations underlying existing work in this area and are the first to consider all major performance-relevant system metrics including the expected broker and link utilization, the expected notification delay, the expected time required for new subscriptions to become fully active, as well as the expected routing table sizes and message rates. To illustrate our approach and demonstrate its effectiveness and practicality, we present a case study showing how our models can be exploited for capacity planning and performance prediction in a realistic scenario.

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Section: Future Directionsmentioning

confidence: 99%

Stochastic performance analysis and capacity planning of publish/subscribe systems

Schröter

Mühl

Kounev

et al. 2010

Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems

Self Cite

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“…Optimizing the communication cost in presence of these challenges requires an adaptive solution. While adaptation techniques have been proposed for pub/sub in the context of efficient routing [30] and overload management [20], the need for an adaptive solution for aggregation is a unique challenge of our work.…”

Section: Related Workmentioning

confidence: 99%

Distributed event aggregation for content-based publish/subscribe systems

Pandey

Zhang

Weiss

et al. 2014

Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems

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Modern data-intensive applications handling massive event streams such as real-time traffic monitoring require support for both rich data filtering and aggregation. While the pub/sub communication paradigm provides an effective solution for the sought semantic diversity of event filtering, the event processing capabilities of existing pub/sub systems are restricted to singular event matching without support for stream aggregation, which so far can be accommodated only at the subscriber edge brokers.In this paper, we propose the first systematic solution for supporting distributed aggregation over a range of time-based aggregation window semantics in a content-based pub/sub system. In order to eschew the need to disseminate a large number of publications to subscribers, we strive to distribute the aggregation computation within the pub/sub overlay network. By enriching the pub/sub language with aggregation semantics, we allow pub/sub brokers to aggregate incoming publications and forward only results to the next broker downstream. We show that our baseline solutions, one which aggregates early (at the publisher edge) and another which aggregates late (at the subscriber edge), are not optimal strategies for minimizing bandwidth consumption. We then propose an adaptive rate-based heuristic solution which determines which brokers should aggregate publications. Using real datasets extracted from our traffic monitoring use case, we show that this adaptive solution leads to improved performance compared to that of our baseline solutions.

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