Preemptive scheduling under time and resource constraints
IEEE Transactions on Computers - Special Issue on Real-Time Systems
Simple and integrated heuristic algorithms for scheduling tasks with time and resource constraints
Journal of Systems and Software
The COMFORT automatic tuning project
Information Systems
Dynamic resource brokering for multi-user query execution
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Proceedings of the Fifth International Conference on Data Engineering
Dynamic Memory Allocation for Multiple-Query Workloads
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Toward a progress indicator for database queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Estimating progress of execution for SQL queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Increasing the Accuracy and Coverage of SQL Progress Indicators
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
When can we trust progress estimators for SQL queries?
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Achieving Class-Based QoS for Transactional Workloads
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
Meeting performance goals with the HP-UX workload manager
WIESS'00 Proceedings of the 1st conference on Industrial Experiences with Systems Software - Volume 1
Quality of service enabled database applications
ICSOC'06 Proceedings of the 4th international conference on Service-Oriented Computing
Multi-query SQL progress indicators
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Managing operational business intelligence workloads
ACM SIGOPS Operating Systems Review
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A testbed for managing dynamic mixed workloads
Proceedings of the VLDB Endowment
Declarative scheduling in highly scalable systems
Proceedings of the 2010 EDBT/ICDT Workshops
Proceedings of the VLDB Endowment
Live business intelligence for the real-time enterprise
From active data management to event-based systems and more
Managing dynamic mixed workloads for operational business intelligence
DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
Interactive analytical processing in big data systems: a cross-industry study of MapReduce workloads
Proceedings of the VLDB Endowment
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Workload management for business intelligence (BI) queries poses different challenges than those addressed in the online transaction processing (OLTP) context. The fundamental problem is that the execution times of BI queries can range from milliseconds to hours, and it is difficult to estimate these times accurately. Key challenges raised by this problem are how to identify queries that are not performing properly and what to do about them. We propose here a workload management system for controlling the execution of individual queries based on realistic customer service level objectives. In order to validate our proposal, we have implemented an experimental system that includes a dynamic execution controller that leverages fuzzy logic. We present results from a number of experiments that we ran using workloads based on actual industrial workloads and customer objectives that we gathered by interviewing industry practitioners. Our experiments show that even a handful of moderately mis-behaving problem queries can have a significant impact on a workload consisting of thousands of queries. We were surprised when our experiments also demonstrated that false positives -- incorrectly identifying a normal query as a problem -- can also have significant consequences. For those reasons, it is very important that an execution controller be as accurate as possible -- avoiding both false positives and false negatives. Our experiments also validate that our execution controller can markedly improve the execution of a workload that includes problem queries.