Authors: Ming Li, David Kotz
Addresses: Department of Computer Science, Institute of Security Technology Studies, Dartmouth College, Hanover, NH 03755, USA. ' Department of Computer Science, Institute of Security Technology Studies, Dartmouth College, Hanover, NH 03755, USA
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 collaborative data-reduction mechanism, |group-aware stream filtering|, used together with multicast, to select a small set of necessary data that satisfy the needs of a group of subscribers simultaneously. We turn data-compressing filters into group-aware filters by exploiting two overlooked, yet important, properties of monitoring applications: 1) many of them can tolerate some degree of |slack| in their data quality requirements; 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 maximise the data overlap within the group to best benefit from multicasting. We provide a general framework that treats the group-aware stream filtering problem completely; we prove the problem NP-hard and thus provide a suite of heuristic algorithms that ensure data quality (specifically, granularity and timeliness) while collaboratively reducing data. The framework is extensible and supports a diverse range of filters. Our prototype-based evaluation shows that group-aware stream filtering is effective in trading CPU time for data reduction, compared with self-interested filtering.
Keywords: stream processing; collaborative data reduction; application-level multicasting; group-aware filters; networked systems; wireless mesh networks; monitoring applications; bandwidth efficiency; data quality.
International Journal of Communication Networks and Distributed Systems, 2009 Vol.2 No.4, pp.375 - 400
Available online: 19 Jun 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article