Interaction-aware scheduling of report-generation workloads

Abstract The typical workload in a database system con-sists of a mix of multiple queries of different types that run concurrently. Interactions among the different queries in a query mix can have a significant impact on database per-formance. Hence, optimizing database performance requires reasoning about query mixes rather than considering queries individually. Current database systems lack the ability to do such reasoning. We propose a new approach based on plan-ning experiments and statistical modeling to capture the im-pact of query interactions. Our approach requires no prior assumptions about the internal workings of the database sys-tem or the nature and cause of query interactions; making it portable across systems. To demonstrate the potential of modeling and exploiting query interactions, we have developed a novel interaction-aware query scheduler for report-generation workloads. Our scheduler, called QShuffler, uses two query scheduling algo-rithms that leverage models of query interactions. The first algorithm is optimized for workloads where queries are sub-mitted in large batches. The second algorithm targets work-loads where queries arrive continuously, and scheduling de-cisions have to be made on-line. We report an experimental evaluation of QShuffler using TPC-H workloads running on IBM DB2. The evaluation shows that QShuffler, by model-ing and exploiting query interactions, can consistently out