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Towards Automated Performance Tuning For Complex Workloads

Summary: Automates per-class MPL and memory tuning with a feedback-based M&M algorithm to counter inter-class contention. Simulations show near-goal times for mixed workloads (short TXs, long joins) and argue for multi-mechanism control. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
8182
Venue
VLDB
Year
1994
Pagerank
9.6410693e-05
Overall Rank
2,065 | 85.64%
DOI
-

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
9 Implementation Techniques For Main Memory Database Systems 1984 SIGMOD 0.0014279444
1,978 Managing Memory to Meet Multiclass Workload Response Time Goals 1993 VLDB 9.8808322e-05
2,299 Dynamic Memory Allocation for Multiple-Query Workloads 1993 VLDB 9.0697388e-05
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