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Issue No. 07 - July (2009 vol. 20)
ISSN: 1045-9219
pp: 968-982
Thomas Repantis , University of California, Riverside, Riverside
Xiaohui Gu , North Carolina State University, Raleigh
Vana Kalogeraki , University of California, Riverside, Riverside
Many emerging online data analysis applications require applying continuous query operations such as correlation, aggregation, and filtering to data streams in real time. Distributed stream processing systems allow in-network stream processing to achieve better scalability and quality-of-service (QoS) provision. In this paper, we present Synergy, a novel distributed stream processing middleware that provides automatic sharing-aware component composition capability. Synergy enables efficient reuse of both result streams and processing components, while composing distributed stream processing applications with QoS demands. It provides a set of fully distributed algorithms to discover and evaluate the reusability of available result streams and processing components when instantiating new stream applications. Specifically, Synergy performs QoS impact projection to examine whether the shared processing can cause QoS violations on currently running applications. The QoS impact projection algorithm can handle different types of streams including both regular traffic and bursty traffic. If no existing processing components can be reused, Synergy dynamically deploys new components at strategic locations to satisfy new application requests. We have implemented a prototype of the Synergy middleware and evaluated its performance on both PlanetLab and simulation testbeds. The experimental results show that Synergy can achieve much better resource utilization and QoS provisioning than previously proposed schemes, by judiciously sharing streams and components during application composition.
Distributed stream processing, component composition, shared processing, QoS, resource management.

T. Repantis, V. Kalogeraki and X. Gu, "QoS-Aware Shared Component Composition for Distributed Stream Processing Systems," in IEEE Transactions on Parallel & Distributed Systems, vol. 20, no. , pp. 968-982, 2008.
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