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Consistent on-line classification of dbs workload events

Published: 02 November 2009 Publication History

Abstract

An important goal of self-managing databases is the autonomic adaptation of the database configuration to evolving workloads. However, the diversity of SQL statements in real-world workloads typically causes the required analysis overhead to be prohibitive for a continuous workload analysis. The workload classification presented in this paper reduces the workload analysis overhead by grouping similar workload events into classes. Our approach employs clustering techniques based upon a general distance function for DBS workload events. To be applicable for a continuous workload analysis, our workload classification specifically addresses a stream-based, lightweight operation, a controllable loss of quality, and self-management.

References

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S. Chaudhuri et al. Compressing SQL workloads. In Proc. of the SIGMOD Int. Conf. on Management of Data. ACM Press, 2002.
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Y. Chen and L. Tu. Density-Based Clustering for Real-Time Stream Data. In Proc. of the 13th SIGKDD Int. Conf. on Knowledge Discovery and Data Mining. ACM Press, 2007.
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S. Elnaffar et al. Automatically Classifying Database Workloads. In Proc. of the 11th Int. Conf. on Information and Knowledge Mgmt. ACM Press, 2008.
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M. Holze and N. Ritter. Autonomic Databases: Detection of Workload Shifts with n-Gram-Models. In Proc. of the 12th East Europ. Conf. on Adv. in Databases and Inf. Syst. Springer-Verlag, 2008.
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M. Ichino and H. Yaguchi. Generalized Minkowski Metrics for Mixed Feature-Type Data Analysis. IEEE Trans. on Systems, Man, and Cybernetics, 24(4), 1994.
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P. Kolaczkowski. Compressing Very Large Database Workloads for Continuous Online Index Selection. In Proc. of the 19th Int. Conf. on Database and Expert Systems Appl. Springer-Verlag, 2008.
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  • (2024)Automatic Index Tuning: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342200636:12(7657-7676)Online publication date: 1-Dec-2024
  • (2021)Workload-Aware Performance Tuning for Autonomous DBMSs2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00267(2365-2368)Online publication date: Apr-2021
  • (2018)Query-based Workload Forecasting for Self-Driving Database Management SystemsProceedings of the 2018 International Conference on Management of Data10.1145/3183713.3196908(631-645)Online publication date: 27-May-2018
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    cover image ACM Conferences
    CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management
    November 2009
    2162 pages
    ISBN:9781605585123
    DOI:10.1145/1645953
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 02 November 2009

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    Author Tags

    1. data stream clustering
    2. self-managing databases
    3. workload classification

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    Cited By

    View all
    • (2024)Automatic Index Tuning: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342200636:12(7657-7676)Online publication date: 1-Dec-2024
    • (2021)Workload-Aware Performance Tuning for Autonomous DBMSs2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00267(2365-2368)Online publication date: Apr-2021
    • (2018)Query-based Workload Forecasting for Self-Driving Database Management SystemsProceedings of the 2018 International Conference on Management of Data10.1145/3183713.3196908(631-645)Online publication date: 27-May-2018
    • (2014)Relational on demand data management for IT-services2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS)10.1109/RCIS.2014.6861078(1-12)Online publication date: May-2014
    • (2013)Heuristics-Based Workload Analysis for Relational DBMSsInformation Systems: Methods, Models, and Applications10.1007/978-3-642-38370-0_3(25-36)Online publication date: 2013
    • (2011)A decision model to select the optimal storage architecture for relational databases2011 FIFTH INTERNATIONAL CONFERENCE ON RESEARCH CHALLENGES IN INFORMATION SCIENCE10.1109/RCIS.2011.6006854(1-11)Online publication date: May-2011
    • (2010)Towards workload-aware self-management: Predicting significant workload shifts2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)10.1109/ICDEW.2010.5452738(111-116)Online publication date: Mar-2010

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