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Class-Based Continuous Query Scheduling in Data Stream Management Systems

Al Moakar, Lory (2013) Class-Based Continuous Query Scheduling in Data Stream Management Systems. Doctoral Dissertation, University of Pittsburgh.

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Abstract

The emergence of Data Stream Management Systems (DSMS) facilitates implementing many types of monitoring applications via continuous queries (CQs). However, different monitoring applications will have different quality-of-service (QoS) requirements for detecting events. For example, the CQs for detecting anomalous events (e.g., fire, flood) have stricter response time requirements over CQs which are for logging and keeping statistical information of interesting physical phenomena. Traditional DSMSs treat all the CQs as being equally important in the system and attempt to optimize their overall performance. In particular, they employ a CQ scheduler to decide the execution order of CQs to achieve a global performance goal and as such perform badly in an environment where CQs have different importance levels.

The hypothesis of this research is that there is a need for a suite of schedulers that optimizes the response time of important CQs while satisfying the requirements of the other, less important classes and taking into consideration the underlying processing environment. Toward this, we first develop the Continuous Query Class (CQC) scheduler for single-core / single-process systems which is assumed by many of the current DSMS prototypes, including our own, AQSIOS. Then, we propose the Adaptive Broadcast Disks scheduler (ABD) which is more suitable for dual-core environments. After that, we extend our work to multi-core environments to take advantage of modern machine architectures and their processing capabilities. We propose the Multi-core Broadcast Disk scheduler (MBD) which optimizes the response time of the critical CQs while maintaining acceptable performance for less-critical classes. In addition, it also utilizes the cores efficiently and provides better performance. We demonstrate the effectiveness of our schedulers through a thorough experimental evaluation using new metrics under AQSIOS, our prototype DSMS, and SimAQSIOS, a simulator that closely mimics AQSIOS.


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Details

Item Type: University of Pittsburgh ETD
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Al Moakar, LoryLJAlMoakar@gcc.edu
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairLabrinidis, Alexandroslabrinid@cs.pitt.eduLABRINID
Committee CoChairChrysanthis, Panos Kpanos@cs.pitt.eduPANOS
Committee MemberPruhs, Kirkkirk@cs.pitt.eduKRP2
Committee MemberSharaf, Mohamedm.sharaf@uq.edu.au
Date: 1 October 2013
Date Type: Publication
Defense Date: 28 May 2013
Approval Date: 1 October 2013
Submission Date: 15 August 2013
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 132
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Big Data, Database, Data Management, User-centric, DSMS, Data Streams
Date Deposited: 01 Oct 2013 13:03
Last Modified: 15 Nov 2016 14:12
URI: http://d-scholarship.pitt.edu/id/eprint/18799

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