Contender

A resource modeling approach for concurrent query performance prediction

Jennie Duggan, Olga Papaemmanouil, Ugur Cetintemel, Eli Upfal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Predicting query performance under concurrency is a difficult task that has many applications in capacity planning, cloud computing, and batch scheduling. We introduce Contender, a new resourcemodeling approach for predicting the concurrent query performance of analytical workloads. Contender's unique feature is that it can generate effective predictions for both static as well as adhoc or dynamic workloads with low training requirements. These characteristics make Contender a practical solution for real-world deployment. Contender relies on models of hardware resource contention to predict concurrent query performance. It introduces two key metrics, Concurrent Query Intensity (CQI) and Query Sensitivity (QS), to characterize the impact of resource contention on query interactions. CQI models how aggressively concurrent queries will use the shared resources. QS defines how a query's performance changes as a function of the scarcity of resources. Contender integrates these two metrics to effectively estimate a query's concurrent execution latency using only linear time sampling of the query mixes. Contender learns from sample query executions (based on known query templates) and uses query plan characteristics to generate latency estimates for previously unseen templates. Our experimental results, obtained from PostgreSQL/TPC-DS, show that Contender's predictions have an error of 19% for known templates and 25% for new templates, which is competitive with the state-ofthe-art while requiring considerably less training time.

Original languageEnglish (US)
Title of host publicationAdvances in Database Technology - EDBT 2014
Subtitle of host publication17th International Conference on Extending Database Technology, Proceedings
EditorsVincent Leroy, Vassilis Christophides, Vassilis Christophides, Stratos Idreos, Anastasios Kementsietsidis, Minos Garofalakis, Sihem Amer-Yahia
PublisherOpenProceedings.org, University of Konstanz, University Library
Pages109-120
Number of pages12
ISBN (Electronic)9783893180653
DOIs
StatePublished - Jan 1 2014
Event17th International Conference on Extending Database Technology, EDBT 2014 - Athens, Greece
Duration: Mar 24 2014Mar 28 2014

Publication series

NameAdvances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings

Other

Other17th International Conference on Extending Database Technology, EDBT 2014
CountryGreece
CityAthens
Period3/24/143/28/14

Fingerprint

Cloud computing
Scheduling
Sampling
Hardware
Planning

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Software

Cite this

Duggan, J., Papaemmanouil, O., Cetintemel, U., & Upfal, E. (2014). Contender: A resource modeling approach for concurrent query performance prediction. In V. Leroy, V. Christophides, V. Christophides, S. Idreos, A. Kementsietsidis, M. Garofalakis, & S. Amer-Yahia (Eds.), Advances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings (pp. 109-120). (Advances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings). OpenProceedings.org, University of Konstanz, University Library. https://doi.org/10.5441/002/edbt.2014.11
Duggan, Jennie ; Papaemmanouil, Olga ; Cetintemel, Ugur ; Upfal, Eli. / Contender : A resource modeling approach for concurrent query performance prediction. Advances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings. editor / Vincent Leroy ; Vassilis Christophides ; Vassilis Christophides ; Stratos Idreos ; Anastasios Kementsietsidis ; Minos Garofalakis ; Sihem Amer-Yahia. OpenProceedings.org, University of Konstanz, University Library, 2014. pp. 109-120 (Advances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings).
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abstract = "Predicting query performance under concurrency is a difficult task that has many applications in capacity planning, cloud computing, and batch scheduling. We introduce Contender, a new resourcemodeling approach for predicting the concurrent query performance of analytical workloads. Contender's unique feature is that it can generate effective predictions for both static as well as adhoc or dynamic workloads with low training requirements. These characteristics make Contender a practical solution for real-world deployment. Contender relies on models of hardware resource contention to predict concurrent query performance. It introduces two key metrics, Concurrent Query Intensity (CQI) and Query Sensitivity (QS), to characterize the impact of resource contention on query interactions. CQI models how aggressively concurrent queries will use the shared resources. QS defines how a query's performance changes as a function of the scarcity of resources. Contender integrates these two metrics to effectively estimate a query's concurrent execution latency using only linear time sampling of the query mixes. Contender learns from sample query executions (based on known query templates) and uses query plan characteristics to generate latency estimates for previously unseen templates. Our experimental results, obtained from PostgreSQL/TPC-DS, show that Contender's predictions have an error of 19{\%} for known templates and 25{\%} for new templates, which is competitive with the state-ofthe-art while requiring considerably less training time.",
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Duggan, J, Papaemmanouil, O, Cetintemel, U & Upfal, E 2014, Contender: A resource modeling approach for concurrent query performance prediction. in V Leroy, V Christophides, V Christophides, S Idreos, A Kementsietsidis, M Garofalakis & S Amer-Yahia (eds), Advances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings. Advances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings, OpenProceedings.org, University of Konstanz, University Library, pp. 109-120, 17th International Conference on Extending Database Technology, EDBT 2014, Athens, Greece, 3/24/14. https://doi.org/10.5441/002/edbt.2014.11

Contender : A resource modeling approach for concurrent query performance prediction. / Duggan, Jennie; Papaemmanouil, Olga; Cetintemel, Ugur; Upfal, Eli.

Advances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings. ed. / Vincent Leroy; Vassilis Christophides; Vassilis Christophides; Stratos Idreos; Anastasios Kementsietsidis; Minos Garofalakis; Sihem Amer-Yahia. OpenProceedings.org, University of Konstanz, University Library, 2014. p. 109-120 (Advances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Contender

T2 - A resource modeling approach for concurrent query performance prediction

AU - Duggan, Jennie

AU - Papaemmanouil, Olga

AU - Cetintemel, Ugur

AU - Upfal, Eli

PY - 2014/1/1

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N2 - Predicting query performance under concurrency is a difficult task that has many applications in capacity planning, cloud computing, and batch scheduling. We introduce Contender, a new resourcemodeling approach for predicting the concurrent query performance of analytical workloads. Contender's unique feature is that it can generate effective predictions for both static as well as adhoc or dynamic workloads with low training requirements. These characteristics make Contender a practical solution for real-world deployment. Contender relies on models of hardware resource contention to predict concurrent query performance. It introduces two key metrics, Concurrent Query Intensity (CQI) and Query Sensitivity (QS), to characterize the impact of resource contention on query interactions. CQI models how aggressively concurrent queries will use the shared resources. QS defines how a query's performance changes as a function of the scarcity of resources. Contender integrates these two metrics to effectively estimate a query's concurrent execution latency using only linear time sampling of the query mixes. Contender learns from sample query executions (based on known query templates) and uses query plan characteristics to generate latency estimates for previously unseen templates. Our experimental results, obtained from PostgreSQL/TPC-DS, show that Contender's predictions have an error of 19% for known templates and 25% for new templates, which is competitive with the state-ofthe-art while requiring considerably less training time.

AB - Predicting query performance under concurrency is a difficult task that has many applications in capacity planning, cloud computing, and batch scheduling. We introduce Contender, a new resourcemodeling approach for predicting the concurrent query performance of analytical workloads. Contender's unique feature is that it can generate effective predictions for both static as well as adhoc or dynamic workloads with low training requirements. These characteristics make Contender a practical solution for real-world deployment. Contender relies on models of hardware resource contention to predict concurrent query performance. It introduces two key metrics, Concurrent Query Intensity (CQI) and Query Sensitivity (QS), to characterize the impact of resource contention on query interactions. CQI models how aggressively concurrent queries will use the shared resources. QS defines how a query's performance changes as a function of the scarcity of resources. Contender integrates these two metrics to effectively estimate a query's concurrent execution latency using only linear time sampling of the query mixes. Contender learns from sample query executions (based on known query templates) and uses query plan characteristics to generate latency estimates for previously unseen templates. Our experimental results, obtained from PostgreSQL/TPC-DS, show that Contender's predictions have an error of 19% for known templates and 25% for new templates, which is competitive with the state-ofthe-art while requiring considerably less training time.

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M3 - Conference contribution

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Duggan J, Papaemmanouil O, Cetintemel U, Upfal E. Contender: A resource modeling approach for concurrent query performance prediction. In Leroy V, Christophides V, Christophides V, Idreos S, Kementsietsidis A, Garofalakis M, Amer-Yahia S, editors, Advances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings. OpenProceedings.org, University of Konstanz, University Library. 2014. p. 109-120. (Advances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings). https://doi.org/10.5441/002/edbt.2014.11