Dartmouth College Computer Science
Technical Report series
TR search TR listserv
|By author:||A B C D E F G H I J K L M N O P Q R S T U V W X Y Z|
|By number:||2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, 1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986|
Recent years have witnessed a new class of monitoring applications that need to continuously collect information from remote data sources. Those data sources, such as web click-streams, stock quotes, and sensor data, are often characterized as fast-rate high-volume ``streams''. Distributed stream-processing systems are thus designed to efficiently use system resources to serve the data-acquisition needs of the applications. Most of the state-of-the-art stream-processing systems assume an Ethernet-based network whose bandwidth is abundant, and focus on mechanisms to save computational power and memory. For applications involving wireless networks, particularly multi-hop mesh networks, we recognize that the most limiting factor in efficiently processing streams lies in the network's highly constrained bandwidth. Hence, this dissertation proposes a group-aware stream filtering approach that saves bandwidth at the cost of increased CPU time, for low-bandwidth data-streaming systems.
This approach, used together with multicasting, exploits two overlooked 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. After proving the problem NP-hard, we introduce a suite of heuristics-based algorithms that ensure data quality, specifically data granularity and timeliness, in addition to preserving network bandwidth.
Our framework for group-aware stream filtering is extensible and supports a diverse range of filtering needs of monitoring applications. We evaluate this approach with a prototype system based on real-world data sets. The results show that quality-managed group-aware filtering is effective in trading CPU time for bandwidth savings, compared with self-interested stream filtering. We also evaluate the effect of each algorithm on temporal freshness of the data. Finally, we discuss other application realms that might benefit from group-aware stream filtering.
Ph.D Dissertation. Advisor: David Kotz
Thesis committee: David Kotz, Andrew Campbell, Paul Thompson, Apratim
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
Ming Li, "Group-Aware Stream Filtering." Dartmouth Computer Science Technical Report TR2008-621, May 2008.
Notify me about new tech reports.
Search the technical reports.
To receive paper copy of a report, by mail, send your address and the TR number to reports AT cs.dartmouth.edu
Copyright notice: The documents contained in this server are included by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
Technical reports collection maintained by David Kotz.