Synonyms
Continuous query scheduling; Operator scheduling; Scheduling policies
Definition
In a Data Stream Management System (DSMS), data arrives in the form of continuous streams from different data sources, where the arrival of new data triggers the execution of multiple continuous queries (CQs). The order in which CQs are executed in response to the arrival of new data is determined by the CQ scheduler. Thus, one of the main goals in the design of a DSMS is the development of scheduling policies that leverage CQ characteristics to optimize the DSMS performance.
Historical Background
The growing need for monitoring applications [8] has forced an evolution on data processing paradigms, moving from Database Management Systems (DBMSs) to Data Stream Management Systems (DSMSs) [4, 11]. Traditional DBMSs employ a store-and-then-query data processing paradigm, where data are stored in the database and queries are submitted by the users to be answered in full, based on the current snapshot...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Acharya S, Muthukrishnan S. Scheduling on-demand broadcasts: new metrics and algorithms. In: Proceedings of the 4th Annual International Conference on Mobile Computing and Networking; 1998.
Babcock B, Babu S, Datar M, Motwani R. Chain: operator scheduling for memory minimization in data stream systems. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2003.
Babcock B, Babu S, Datar M, Motwani R, Thomas D. Operator scheduling in data stream systems. VLDB J. 2004;13(4):333–53.
Babcock B, Babu S, Datar M, Motwani R, Widom J. Models and issues in data stream systems. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2002.
Babu S, Widom J. Continuous queries over data streams. ACM SIGMOD Rec. 2001;30(3):109–120.
Bansal N, Pruhs K. Server scheduling in the L p norm: a rising tide lifts all boats. In: Proceedings of the 35th Annual ACM Symposium on Theory of Computing; 2003.
Bender MA, Chakrabarti S, Muthukrishnan S. Flow and stretch metrics for scheduling continuous job streams. In: Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms; 1998.
Carney D, Cetintemel U, Cherniack M, Convey C, Lee S, Seidman G, Stonebraker M, Tatbul N, Zdonik S. Monitoring streams: a new class of data management applications. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002.
Carney D, Cetintemel U, Rasin A, Zdonik S, Cherniack M, Stonebraker M. Operator scheduling in a data stream manager. In: Proceedings of the 29th International Conference on Very Large Data Bases; 2003.
Chandrasekaran S, Cooper O, Deshpande A, Franklin MJ, Hellerstein JM, Hong W, Krishnamurthy S, Madden S, Raman V, Reiss F, Shah MA. TelegraphCQ: continuous dataflow processing for an uncertain world. In: Proceedings of the 1st Biennial Conference on Innovative Data Systems Research; 2003.
Golab L, Özsu MT. Issues in data stream management. ACM SIGMOD Rec. 2003;32(2):5–14.
Mehta M, DeWitt DJ. Dynamic memory allocation for multiple-query workloads. In: Proceedings of the 19th International Conference on Very Large Data Bases; 1993.
Muthukrishnan S, Rajaraman R, Shaheen A, Gehrke J.E. Online scheduling to minimize average stretch. In: Proceedings of the 40th Annual Symposium on Foundations of Computer Science; 1999.
Sharaf MA, Chrysanthis PK, Labrinidis A, Pruhs K. Efficient scheduling of heterogeneous continuous queries. In: Proceedings of the 32nd International Conference on Very Large Data Bases; 2006.
Sharaf MA, Labrinidis A, Chrysanthis PK, Pruhs K. Freshness-aware scheduling of continuous queries in the Dynamic Web. In: Proceedings of the 8th International Workshop on the World Wide Web and Database; 2005.
Sutherland T, Pielech B, Zhu Y, Ding L, Rundensteiner EA. An adaptive multi-objective scheduling selection framework for continuous query processing. In: Proceedings of the International Database Engineering and Applications Symposium; 2005.
Urhan T, Franklin M.J. Dynamic pipeline scheduling for improving interactive query performance. In: Proceedings of the 27th International Conference on Very Large Data Bases; 2001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
Cite this entry
Sharaf, M., Labrinidis, A. (2018). Scheduling Strategies for Data Stream Processing. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_321
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8265-9_321
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-8266-6
Online ISBN: 978-1-4614-8265-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering