Distributed and Parallel Databases - Special issue: Research topics in distributed and parallel databases
The COMFORT automatic tuning project
Information Systems
Load control for locking: the “half-and-half” approach
PODS '90 Proceedings of the ninth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Scheduling Computer and Manufacturing Processes
Scheduling Computer and Manufacturing Processes
Operating System Concepts
Database tuning: principles, experiments, and troubleshooting techniques
Database tuning: principles, experiments, and troubleshooting techniques
Introduction to Algorithms
Scheduling Real-time Transactions: a Performance Evaluation
VLDB '88 Proceedings of the 14th International Conference on Very Large Data Bases
Predictive Load Control for Flexible Buffer Allocation
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
Priority Mechanisms for OLTP and Transactional Web Applications
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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
SMART: making DB2 (more) autonomic
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Toward a progress indicator for program compilation
Software—Practice & Experience
Self-tuning database systems: a decade of progress
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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
Managing operational business intelligence workloads
ACM SIGOPS Operating Systems Review
Adaptive progress indicator for long running SQL queries
ACS'08 Proceedings of the 8th conference on Applied computer scince
The design of a query monitoring system
ACM Transactions on Database Systems (TODS)
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Runtime Estimations, Reputation and Elections for Top Performing Distributed Query Scheduling
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
ParaTimer: a progress indicator for MapReduce DAGs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Performance prediction for concurrent database workloads
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
A statistical approach towards robust progress estimation
Proceedings of the VLDB Endowment
Managing dynamic mixed workloads for operational business intelligence
DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
Parallel analytics as a service
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Performance and resource modeling in highly-concurrent OLTP workloads
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Workload management for big data analytics
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
PREDIcT: towards predicting the runtime of large scale iterative analytics
Proceedings of the VLDB Endowment
Workload management: a technology perspective with respect to self-* characteristics
Artificial Intelligence Review
| Hi-index | 0.00 |
Recently, progress indicators have been proposed for SQL queries in RDBMSs. All previously proposed progress indicators consider each query in isolation, ignoring the impact simultaneously running queries have on each other’s performance. In this paper, we explore a multi-query progress indicator, which explicitly considers concurrently running queries and even queries predicted to arrive in the future when producing its estimates. We demonstrate that multi-query progress indicators can provide more accurate estimates than single-query progress indicators. Moreover, we extend the use of progress indicators beyond being a GUI tool and show how to apply multi-query progress indicators to workload management. We report on an initial implementation of a multi-query progress indicator in PostgreSQL and experiments with its use both for estimating remaining query execution time and for workload management.