Dynamic resource brokering for multi-user query execution
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Adaptive Load Control in Transaction Processing Systems
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
Dynamic Memory Allocation for Multiple-Query Workloads
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
How to Determine a Good Multi-Programming Level for External Scheduling
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Workload adaptation in autonomic DBMSs
CASCON '06 Proceedings of the 2006 conference of the Center for Advanced Studies on Collaborative research
Workload adaptation in autonomic database management systems
Workload adaptation in autonomic database management systems
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
A testbed for managing dynamic mixed workloads
Proceedings of the VLDB Endowment
Quality of service enabled database applications
ICSOC'06 Proceedings of the 4th international conference on Service-Oriented Computing
Workload management for big data analytics
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Ideally, a data warehouse would be able to run multiple types of queries concurrently, meeting different performance objectives for each type. However, due to the difficulty of managing mixed workloads, most commercial systems segregate distinct workload components by using strict resource partitioning and/or time multiplexing. This approach avoids unexpected resource contention, but when one workload component does not fully use its allocated resources, those resources may then lie unused even if they could greatly improve the performance of another component. We focus here on adaptively scheduling mixed workloads that have multiple objectives. We use our experimental framework for testing policies to evaluate the extent to which prior approaches to adaptive workload scheduling address mixed workloads. Our experiments demonstrate the difficulty of searching for solutions in the space of scheduling dynamic mixed workloads. We discuss why prior approaches do not address certain scenarios and then demonstrate how leveraging additional knowledge would allow one approach to succeed, if that knowledge were available.