Ming Li and David Kotz. Quality-managed Group-aware Stream Filtering. In Proceedings of the Second International Conference on Distributed Event-Based Systems (DEBS), pages 59-70, July 2008. ACM Press. DOI 10.1145/1385989.1385998.

Abstract: We consider a distributed system that disseminates high-volume event streams to many simultaneous monitoring applications over a low-bandwidth network. For bandwidth efficiency, we propose a \textitgroup-aware stream filtering approach, used together with multicasting, that exploits two overlooked, yet important, properties of monitoring applications: 1) many of them can tolerate some degree of ``slack'' in their data quality requirements, and 2) there may exist multiple subsets of the source data satisfying the quality needs of an application. We can thus choose the ``best alternative'' subset for each application to maximize the data overlap within the group to best benefit from multicasting. Here we provide a general framework for the group-aware stream filtering problem, which we prove is NP-hard. We introduce a suite of heuristics-based algorithms that ensure data quality (specifically, granularity and timeliness) while preserving bandwidth. Our evaluation shows that group-aware stream filtering is effective in trading CPU time for bandwidth savings, compared with self-interested filtering.

Keywords: distributed computing


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See also later version li:ijcnds.