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Automatic Tuning of the MULTIPROGRAMMING LEVEL in Sybase SQL Anywhere. Mohammed Abouzour , Kenneth Salem , Peter Bumbulis Presentation by Mohammed Abouzour SMDB2010. Introduction to Problem. Database servers have two main architectures: Worker-per-connection: (Oracle, Sybase ASE)

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Automatic Tuning of the MULTIPROGRAMMING LEVEL in Sybase SQL Anywhere

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    1. Automatic Tuning of the MULTIPROGRAMMING LEVEL in Sybase SQL Anywhere Mohammed Abouzour, Kenneth Salem, Peter Bumbulis Presentation by Mohammed Abouzour SMDB2010

    2. Introduction to Problem • Database servers have two main architectures: • Worker-per-connection:(Oracle, Sybase ASE) • A worker is dedicated for each connection for the life of the connection • Worker-per-request:(Oracle, Microsoft SQL Server, Sybase ASE, Sybase SQL Anywhere) • A worker pool and a request queue: each worker picks and complete one request at a time. No guarantee that the same worker will service the same connection.

    3. Worker-Per-Request Architecture: • Efficient in resource use and handling of large number of connections(1) • How to choose the size of the worker pool? • A large worker pool: • Increases the concurrency level of the server • Increases contention on server resources • Increases working set size of server • A smaller worker pool: • Under utilization of hardware resources • Limit concurrency level of workload • Possibility of a server hang due to no workers available to handle outstanding requests

    4. Our Solution • Dynamically adjust the size of the worker pool (the multiprogramming level of the server) • Benefits of dynamic MPL: • One less parameter for DBAs to worry about • One of the design goals of Sybase SQL Anywhere • Improve server performance for different workloads • Better handling of changes in workload transaction mix • We used throughput as a way to detect degraded server performance

    5. Outline • Introduction to Problem • Controller Design and Tuning Algorithms • Experimental Study • Results • Future Work

    6. Controller design

    7. Controller design

    8. Tuning algorithms • Tuning algorithms are based on a paper by Heiss and Wagner(2): • Incremental Steps algorithm (IS) • Parabola Approximation (PA) • Original algorithms were studied in simulation • Our contribution to the original algorithms(3): • We have extended the IS algorithm • We have used a commercial database system and an industry standard benchmark (TPC-C) • We have studied the behaviour of the above two algorithms under different workloads

    9. Notation

    10. Hill Climbing Algorithm • Original IS algorithm did not handle a throughput curve that flattens out • Modified the IS algorithm as follows: • Follow the throughput curve up as long as percentage of change in throughput is greater than C times the percentage of change in number of threads • Follow the throughput curve down as long as throughput increases • Built-in bias to keep the number of threads as low as possible

    11. Global Parabola Approximation Algorithm • Model the throughput curve as a parabolic function: • Collect two points from the last two measurements and use the origin • Only make adjustments if the computed parabolic curve is concave down, otherwise just make a random adjustment

    12. Experimental study: • Used the TPC-C workload for our benchmark • Used two workloads variations: • TPCC1: Standard TPC-C transaction mix • TPCC2: Modified TPC-C transaction mix (lighter on I/O and higher CPU component) • Two different buffer pool configurations: • BIGMEM: 1.0GB • SMALLMEM: 0.5GB • Three different workload configurations: • TPCC1-BIGMEM • TPCC1-SMALLMEM • TPCC2-BIGMEM

    13. Experimental Study:Methodology • Workload characterization experiments: • Ran each workload under different fixed MPL setting and generated a throughput curve for each workload • Experiments: • Auto-tuning experiments: • We ran each of the workloads and allowed the auto tuning algorithms to adjust MPL dynamically • Changing workloads experiments: • We start with one workload and half way in the experiment we switch to a different workload

    14. Workload characteristics: TPCC1-BIGMEM TPCC1-SMALLMEM

    15. TPCC1-BIGMEM Global Parabola Approximation Hill Climbing

    16. TPCC1-SMALLMEM Global Parabola Approximation Hill Climbing

    17. TPCC1-BIGMEM to TPCC1-SMALLMEM Global Parabola Approximation Hill Climbing

    18. lessons learned • Not all workloads in database systems have hill-shaped throughput curve • Both algorithms were able to react appropriately to workloads changes • The Hill Climbing algorithm: • Smoother in making adjustments (more desirable) • Takes longer to reach optimal values • Can get stuck in a local minima • The Global Parabola approximation algorithm: • Takes variable step sizes and hence reacts faster to changes • Very sensitive to normal minor workload fluctuations

    19. Practical Experience with SQL Anywhere 12 • A hybrid controller: • Uses the Hill Climbing algorithm for a number of steps and collects data • Uses the data collected with the Parabola Approximation algorithm • We reduced the control interval length • Hill Climbing modifications: • Take dynamic step sizes to speed up algorithm • C parameter reduced to make algorithm more aggressive • Parabola Approximation modifications: • Use least-square method to do curve fitting on collected data • Results: • 90-95% of optimal tpmC compared to a hand tuned version • In other internal CPU-bound benchmark was able to improve performance by 32%

    20. Conclusion and Future Work • Dynamically adjusting the MPL: • Improves server performance in some workloads compared to a fixed MPL • Handles changes to workload conditions effectively • Brings us one step closer to a self managing database system • Algorithm parameters can affect the performance of the algorithms: • Control interval length • Hill Climbing: • Step sizes • C parameter • The algorithms did not behave good for the following workloads: • Bursty workloads: busy periods followed by idle periods followed by busy periods • Long running requests: e.g. creating an index • Use response time to help the auto tuning algorithm

    21. References • B. Schroeder, M. Harchol-Balter, A. Iyengar, E. Nahum, and A. Wierman, “How to determine a good multi-programming level for external scheduling,” in Proceedings of the 22nd International Conference on Data Engineering. Washington, DC, USA: IEEE Computer Society, 2006, p. 60. • H.-U. Heiss and R. Wagner. “Adaptive load control in transaction processing systems,” in VLDB ‘91: Proceedings of the 17th International Conference on Very Large Data Bases. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1991, pp. 47-54. • M. Abouzour, “Automatically tuning database server multiprogramming level,” Master’s thesis, University of Waterloo, 2007

    22. Questions

    23. TPC-C configuration • 150 Warehouses • 10 clients per warehouse • 500 connections • C = 0.4, Delta = 10 • Gamma=0.333, Delta = 10 • Windows 2003 32-bit • Dual 1.80Ghz Intel Xeon • 48 Spindles 10K RPM SCSI drives