In this dissertation, we (the author and her research collaborators) 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 ``group-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.
| Autorius: | Ming Li |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2009 |
| Knygos puslapių skaičius: | 132 |
| ISBN-10: | 3838302893 |
| ISBN-13: | 9783838302898 |
| Formatas: | 220 x 150 x 8 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Group-Aware Stream Filtering: Towards Collaborative Data Reduction in Stream Processing Systems“
