Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
Analysis of the generalized clock buffer replacement scheme for database transaction processing
SIGMETRICS '92/PERFORMANCE '92 Proceedings of the 1992 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Mean-Value Analysis of Closed Multichain Queuing Networks
Journal of the ACM (JACM)
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Kernel independent component analysis
The Journal of Machine Learning Research
Analytical response time estimation in parallel relational database systems
Parallel Computing
Toward a progress indicator for database queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Estimating progress of execution for SQL queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Developing a characterization of business intelligence workloads for sizing new database systems
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Data base system performance prediction using an analytical model (invited paper)
VLDB '81 Proceedings of the seventh international conference on Very Large Data Bases - Volume 7
The design of a query monitoring system
ACM Transactions on Database Systems (TODS)
Application of Queueing Network Models in the Performance Evaluation of Database Designs
Electronic Notes in Theoretical Computer Science (ENTCS)
Adaptive Scheduling of Web Transactions
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Predicting completion times of batch query workloads using interaction-aware models and simulation
Proceedings of the 14th International Conference on Extending Database Technology
Performance prediction for concurrent database workloads
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Interaction-aware scheduling of report-generation workloads
The VLDB Journal — The International Journal on Very Large Data Bases
Learning-based Query Performance Modeling and Prediction
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Robust estimation of resource consumption for SQL queries using statistical techniques
Proceedings of the VLDB Endowment
Predicting query execution time: Are optimizer cost models really unusable?
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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Predicting query execution time is crucial for many database management tasks including admission control, query scheduling, and progress monitoring. While a number of recent papers have explored this problem, the bulk of the existing work either considers prediction for a single query, or prediction for a static workload of concurrent queries, where by "static" we mean that the queries to be run are fixed and known. In this paper, we consider the more general problem of dynamic concurrent workloads. Unlike most previous work on query execution time prediction, our proposed framework is based on analytic modeling rather than machine learning. We first use the optimizer's cost model to estimate the I/O and CPU requirements for each pipeline of each query in isolation, and then use a combination queueing model and buffer pool model that merges the I/O and CPU requests from concurrent queries to predict running times. We compare the proposed approach with a machine-learning based approach that is a variant of previous work. Our experiments show that our analytic-model based approach can lead to competitive and often better prediction accuracy than its machine-learning based counterpart.