Principles of database buffer management
ACM Transactions on Database Systems (TODS)
Analysis of the generalized clock buffer replacement scheme for database transaction processing
SIGMETRICS '92/PERFORMANCE '92 Proceedings of the 1992 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
An optimality proof of the LRU-K page replacement algorithm
Journal of the ACM (JACM)
Flexible and Adaptable Buffer Management Techniques for Database Management Systems
IEEE Transactions on Computers
Priority-Hints: An Algorithm for Priority-Based Buffer Management
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
Managing Memory to Meet Multiclass Workload Response Time Goals
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
2Q: A Low Overhead High Performance Buffer Management Replacement Algorithm
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Dynamic Reconfiguration Algorithm: Dynamically Tuning Multiple Buffer Pools
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
ARC: A Self-Tuning, Low Overhead Replacement Cache
FAST '03 Proceedings of the 2nd USENIX Conference on File and Storage Technologies
CAR: Clock with Adaptive Replacement
FAST '04 Proceedings of the 3rd USENIX Conference on File and Storage Technologies
Adaptive self-tuning memory in DB2
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A new approach to dynamic self-tuning of database buffers
ACM Transactions on Storage (TOS)
Tuning database configuration parameters with iTuned
Proceedings of the VLDB Endowment
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Effective I/O buffering is a performance-critical task in database management systems. Accordingly, systems usually employ various special-purpose buffers to align, e.g., device speed, page size, and replacement policies with the actual data and workload. However, such partitioning of available buffer memory results in complex optimization problems for database administrators and also in fragile configurations which quickly deteriorate on workload shifts. Reliable forecasts of I/O costs enable a system to evaluate alternative configurations to continuously optimize its buffer memory allocation at runtime. So far, all techniques proposed for the prediction of buffer performance focus solely on hit ratio gains for increased buffer sizes to identify buffers which promise the greatest benefit. These approaches, however, assume that their forecast allows to extrapolate the effect for buffer downsizing, too. As we will show, this comes along with a severe risk of wrong tuning decisions, which may heavily impact system performance. Thus, we emphasize the importance of reliably forecasting the penalty to expect for shrinking buffers in favor of others. We explore the use of lightweight extensions for widely used buffer algorithms to perform on-the-fly simulation of buffer performance of smaller and larger buffer sizes simultaneously. Furthermore, we present a simple cost model and demonstrate how to compose these concepts into a self-tuning component for dynamic buffer reallocation.