Updating the inverse of a matrix
SIAM Review
LEO: An autonomic query optimizer for DB2
IBM Systems Journal
Continuous resource monitoring for self-predicting DBMS
MASCOTS '05 Proceedings of the 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Towards self-predicting systems: What if you could ask ‘what-if’?
The Knowledge Engineering Review
A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
PQR: Predicting Query Execution Times for Autonomous Workload Management
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
iTuned: a tool for configuring and visualizing database parameters
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Probabilistic performance modeling of virtualized resource allocation
Proceedings of the 7th international conference on Autonomic computing
Predicting completion times of batch query workloads using interaction-aware models and simulation
Proceedings of the 14th International Conference on Extending Database Technology
Interaction-aware scheduling of report-generation workloads
The VLDB Journal — The International Journal on Very Large Data Bases
Executing Data-Intensive Workloads in a Cloud
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Characterizing tenant behavior for placement and crisis mitigation in multitenant DBMSs
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
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In order to meet service level agreements (SLAs) and to maintain peak performance for database management systems (DBMS), database administrators (DBAs) need to implement policies for effective workload scheduling, admission control, and resource provisioning. Accurately predicting response times of DBMS queries is necessary for a DBA to effectively achieve these goals. This task is particularly challenging due to the fact that a database workload typically consists of many concurrently running queries and an accurate model needs to capture their interactions. Additional challenges are introduced when DBMSes are run in dynamic cloud computing environments, where workload, data, and physical resources can change frequently, on-the-fly. Building an efficient and highly accurate online DBMS performance model that is robust in the face of changing workloads, data evolution, and physical resource allocations is still an unsolved problem. In this work, our goal is to build such an online performance model for database appliances using an experiment-driven modeling approach. We use a Bayesian approach and build novel Gaussian models that take into account the interaction among concurrently executing queries and predict response times of individual DBMS queries. A key feature of our modeling approach is that the models can be updated online in response to new queries or data, or changing resource allocations. We experimentally demonstrate that our models are accurate and effective -- our best models have an average prediction error of 16.3% in the worst case.