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Scalable and robust stream processing
Descriptive
TitleInfo
Title
Scalable and robust stream processing
Name
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Shkapenyuk
NamePart
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Vladislav
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Vladislav Shkapenyuk
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author
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Muthukrishnan
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Shanmugavelayutham
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Advisory Committee
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Shanmugavelayutham Muthukrishnan
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chair
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Liviu
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Advisory Committee
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Liviu Iftode
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internal member
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Marian
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Amélie
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Advisory Committee
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Amélie Marian
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internal member
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Srivastava
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Divesh
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Advisory Committee
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Divesh Srivastava
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outside member
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Rutgers University
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Graduate School-New Brunswick
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school
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theses
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DateCreated
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2007
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2007
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English
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electronic
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viii, 167 pages
Abstract
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Distributed Data Stream Management Systems (DSMS) are increasingly used for the processing of high-rate data streams in real-time. An effective query optimization mechanism is a critical component that allows DSMS to deal with extreme data rates and large numbers of long-running concurrent queries. This dissertation investigates how to utilize semantic query analysis to perform query optimizations that enable scalable and robust data stream processing. We address three technical challenges faced by streaming system: (1) monitoring and correlating large number of diverse data streams with significant variations in data rates; (2) the ability to remain stable and produce correct answers even under overload conditions, and (3) supporting efficient distributed query processing to easily scale with increases in the number of processing nodes and stream data rates.
First, we propose a heartbeat mechanism to prevent the DSMS from blocking when some of the monitored streams temporarily stall or slow down. By generating special punctuation messages at low-level query nodes and propagating them throughout the entire query execution plan, our heartbeat mechanism effectively unblocks all stalled query nodes.
The second contribution of this dissertation addresses the problem of DSMS robustness when a load on a system increases by orders of magnitude. We introduce a query-aware sampling mechanism for guaranteeing the system's stability and the correctness of its query output under overload conditions. The mechanism is generic and supports arbitrary complex query sets.
Finally, we address the problem of scalable distributed evaluation of streaming queries. The key contribution of the dissertation is a query-aware partitioning mechanism that allows us to scale the performance of the streaming queries in a close to linear fashion. We propose a query analysis framework for determining the optimal partitioning and a partition-aware distributed query optimizer that takes advantage of existing partitions.
In summary, the contributions made by this dissertation in the area of streaming query optimization enable Data Stream Management Systems to scale to extreme data rates, gracefully handle overload conditions and support a large number of diverse input streams, enabling industrial-scale applications of DSMS technology.
First, we propose a heartbeat mechanism to prevent the DSMS from blocking when some of the monitored streams temporarily stall or slow down. By generating special punctuation messages at low-level query nodes and propagating them throughout the entire query execution plan, our heartbeat mechanism effectively unblocks all stalled query nodes.
The second contribution of this dissertation addresses the problem of DSMS robustness when a load on a system increases by orders of magnitude. We introduce a query-aware sampling mechanism for guaranteeing the system's stability and the correctness of its query output under overload conditions. The mechanism is generic and supports arbitrary complex query sets.
Finally, we address the problem of scalable distributed evaluation of streaming queries. The key contribution of the dissertation is a query-aware partitioning mechanism that allows us to scale the performance of the streaming queries in a close to linear fashion. We propose a query analysis framework for determining the optimal partitioning and a partition-aware distributed query optimizer that takes advantage of existing partitions.
In summary, the contributions made by this dissertation in the area of streaming query optimization enable Data Stream Management Systems to scale to extreme data rates, gracefully handle overload conditions and support a large number of diverse input streams, enabling industrial-scale applications of DSMS technology.
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references (p. 156-165).
Subject
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Topic
Computer Science
Subject
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Topic
Computer networks
Subject
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Topic
Data transmission systems
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Graduate School - New Brunswick Electronic Theses and Dissertations
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.13727
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doi:10.7282/T3N58MTZ
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ETD doctoral
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Rights
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Open
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Vladislav Shkapenyuk
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Rutgers University. Graduate School-New Brunswick
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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