Managing operational business intelligence workloads
ACM SIGOPS Operating Systems Review
Classification with Unknown Classes
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
On the Use of Machine Learning to Predict the Time and Resources Consumed by Applications
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
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
A bayesian approach to online performance modeling for database appliances using gaussian models
Proceedings of the 8th ACM international conference on Autonomic computing
ActiveSLA: a profit-oriented admission control framework for database-as-a-service providers
Proceedings of the 2nd ACM Symposium on Cloud Computing
On predictive modeling for optimizing transaction execution in parallel OLTP systems
Proceedings of the VLDB Endowment
Managing dynamic mixed workloads for operational business intelligence
DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
Optimizing analytic data flows for multiple execution engines
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
What is my program doing? program dynamics in programmer's terms
RV'11 Proceedings of the Second international conference on Runtime verification
Optimizing flows for real time operations management
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Performance and resource modeling in highly-concurrent OLTP workloads
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Towards building performance models for data-intensive workloads in public clouds
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Mantis: automatic performance prediction for smartphone applications
USENIX ATC'13 Proceedings of the 2013 USENIX conference on Annual Technical Conference
Hybrid Analytic Flows-the Case for Optimization
Fundamenta Informaticae - Scalable Workflow Enactment Engines and Technology
Predicting execution time of machine learning tasks for scheduling
International Journal of Hybrid Intelligent Systems
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Modern enterprise data warehouses have complex workloads that are notoriously difficult to manage. One of the key pieces to managing workloads is an estimate of how long a query will take to execute. An accurate estimate of this query execution time is critical to self managing Enterprise Class Data Warehouses.In this paper we study the problem of predicting the execution time of a query on a loaded data warehouse with a dynamically changing workload. We use a machine learning approach that takes the query plan, combines it with the observed load vector of the system and uses the new vector to predict the execution time of the query. The predictions are made as time ranges. We validate our solution using real databases and real workloads. We show experimentally that our machine learning approach works well. This technology is slated for incorporation into a commercial, enterprise class DBMS.