Honolulu, HI, USA USA
June 24, 2012 to June 29, 2012
We propose a novel admission control policy for database queries. Our methodology uses system measurements of CPU utilization and query backlogs to determine interference between queries in execution on the same database server. Query interference may arise due to the concurrent access of hardware and software resources and can affect performance in positive and negative ways. Specifically our admission control considers the mix of jobs in service and prioritizes the query classes consuming CPU resources more efficiently. The policy ignores I/O subsystems and is therefore highly appropriate for in-memory databases. We validate our approach in trace-driven simulation and show performance increases of query slowdowns and throughputs compared to first-come first-served and shortest expected processing time first scheduling. Simulation experiments are parameterized from system traces of a SAP HANA in-memory database installation with TPC-H type workloads.
Databases, Admission control, Interference, Instruction sets, Delay, Computational modeling, scheduling, performance, database management, query interference, admission control
Stephan Kraft, Giuliano Casale, Alin Jula, Peter Kilpatrick, Des Greer, "WIQ: Work-Intensive Query Scheduling for In-Memory Database Systems", CLOUD, 2012, 2013 IEEE Sixth International Conference on Cloud Computing, 2013 IEEE Sixth International Conference on Cloud Computing 2012, pp. 33-40, doi:10.1109/CLOUD.2012.120