Modeling and exploiting query interactions in database systems
Proceedings of the 17th ACM conference on Information and knowledge management
Predicting system performance for multi-tenant database workloads
Proceedings of the Fourth International Workshop on Testing Database Systems
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
Workload management: a technology perspective with respect to self-* characteristics
Artificial Intelligence Review
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The typical workload in a database system consists of a mixture of multiple queries of different types, running concurrently and interacting with each other. Hence, optimizing performance requires reasoning about query mixes and their interactions, rather than considering individual queries or query types. In this paper, we use such a reasoning approach to develop a query scheduler. We treat the database system as a black box and experimentally build a model to estimate the performance of different query mixes. Our scheduler uses this model to decide which query mixes to schedule, with the goal of maximizing throughput. We experimentally demonstrate the effectiveness of our scheduler using queries from the TPC-H benchmark on DB2.