Multiclass Query Scheduling in Real-Time Database Systems
IEEE Transactions on Knowledge and Data Engineering
Dynamic workload management for very large data warehouses: juggling feathers and bowling balls
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Poster Session: Adapting Mixed Workloads to Meet SLOs in Autonomic DBMSs
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Adaptive query scheduling for mixed database workloads with multiple objectives
Proceedings of the Third International Workshop on Testing Database Systems
Workload management for big data analytics
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Workload management for operational business intelligence (BI) databases is difficult. Queries vary widely in length and objectives. Resource contention is difficult to predict and to control as dynamically-arriving, long, analyst queries compete for resources with ongoing online-transaction processing (OLTP) queries and batch report queries. Currently, administrators struggle to choose workload management policies and set their thresholds manually. The goal of our project is a software framework to make the management of such mixed workloads easier. Our framework includes a policy controller that tunes workload management policies automatically to meet workload objectives. This demonstration of our system illustrates (1) the difficulty of managing a BI database workload and (2) the benefits of tuning policies automatically and individually for each service class of queries in a workload. In addition, our demonstrator is a useful research tool for understanding how policies and a policy controller adapt as the system state changes under a mixed workload. In our demo, the participant plays the administrator and tunes the policies for a variety of difficult-to-manage workloads as they execute. These policies include admission control, scheduling, and execution control policies. We visualize the policies, the user objectives, and the load on the system components (CPUs, memory, disks) during execution, which helps the participant see whether objectives are being met and make appropriate policy decisions. At the end of each workload, the participant is given the opportunity to compare how their policies met workload objectives versus policies determined by our automatic policy controller.