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Adaptive Load Diffusion for Multiway Windowed Stream Joins

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3 Author(s)
Xiaohui Gu ; IBM T. J. Watson Res. Center, Hawthorne, NY, USA ; Yu, P.S. ; Haixun Wang

In this paper, we present an adaptive load diffusion operator to enable scalable processing of multiway windowed stream joins (MWSJs) using a cluster system. The load diffusion is achieved by a set of novel semantics-pre serving tuple routing algorithms. Different from previous work, the load diffusion operator can (1) preserve the MWSJ semantics while spreading tuples to different hosts for parallel join processing; (2) achieve fine-grained load balancing among distributed hosts; and (3) perform semantics-preserving online adaptations to maintain optimal performance in dynamic stream environments. We have implemented a prototype of the distributed MWSJ framework on top of the System S distributed stream processing system. Our experiment results based on both real data streams and synthetic workloads show that the load diffusion algorithms can efficiently scale-up the performance of MWSJ processing with low overhead.

Published in:

Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on

Date of Conference:

15-20 April 2007