Goal-Oriented Buffer Management Revisited

1. Goal-Oriented Buffer Management Revisited Kurt P. Brown, Michael J. Carey, Miron Livny Presented by Mike Nie

2. Agenda • Goal-Oriented Basics • Criteria for Success • Previous Approaches • Class Fencing • Experiments and Results • Conclusion and Discussion

3. Goal-Oriented Basics • This paper focus on the control of response time of a specific workload. • Knob: Disk buffer memory allocation. response time goal Target DBMS memory allocation adjustment observed response time K

4. Goal-Oriented Memory Allocation • Definition: “For each class with an average response time goal, a memory allocation must be found such that its observed response time is as close as possible to its goal.” • How it works? • More buffer memory -> high hit rate • High hit rate -> low response time

5. Criteria for Success - I • Accuracy • Observed average response time should be close to its goal. • Responsiveness • The number of knob adjustment to the goal should be minimum. • Stability • The variance of observed average response time should be small.

6. Criteria for Success - II • Overhead • The controller should minimize the resource it consumes. • Robustness • The system should handle a wide range of workloads. • Practicality • Make fair assumptions about workload and DBMS in general.

7. Goal-Oriented Buffer Allocation Manager Architecture • Response time estimator • Fn: hit rate -> est. response time • Hit rate estimator • Fn: memory allocation -> est. hit rate • Buffer allocation mechanism • A mechanism to divide up memory between workloads. • Inverse the functions to calculate buffer allocation plan. • Goal res. Time -> hit rate -> memory allocation

8. Dynamic Tuning • Response time estimator: • Rest = (1.0-HITest(M)) * D • Hit rate estimator: • HITest =1 - a/Mb • Buffer allocation mechanism: • Partation buffer pool based M calculated above for each class.

9. Dynamic Tuning Issues • Overshoot • Low responsiveness • No shared data between workloads

10. Fragment Fencing - I • Response time estimator: • Response time and buffer miss rate are directly proportional. • Hit rate estimator: • HITtarget = 1.0-(Mobsv*(Rgoal/Robsv)) • Goal: determine the minimum number of pages that must be in memory to achieve an overall target hit rate for the class.

11. Fragment Fencing - II • Buffer allocation mechanism • Passive allocation • Issues: • Having trouble when fragment is not uniform. • High overhead due to passive allocation mechanism.

12. Class Fencing - Hit Rate Concavity • Concavity theorem: • Regardless of the database reference pattern, hit rate as a function of buffer memory allocation is a concave function under an optimal replacement policy. • Empirical study shows that modern page replacement policy are good enough, no knees!

13. Class Fencing - Hit Rate Estimator

14. Class Fencing’s - Memory Allocation • A compromise between 2 previous approaches • Local buffer manager VS global buffer manager, shared disk-page-to-buffer-frame table • poolSize, the max. number of buffer frames, is determined by hit rate estimator for one class. • existing DBMS replacement policy applied when demanding pages.

15. Sharing Between Classes • Sharing: one class can reference a frame outside its fence. • poolSize represents lower bound on the number of frames used by one class, whereas hit rate estimator works with total number of frames used the class. • nonLocal[C] = bufSize – poolSize[C] p = (inUse[C] - numLocal[C]) / nonLocal[C] inUse[C]est = poolSize[C] + △poolSize + p * (nonLocal[C] - △poolSize)

16. Experiments and Results • TPC-C and DBMIN Q2 • Goals for Q2 only • Goals for TPC-C only • Goals for Q2 and TPC-C • DBMIN Q2 and DBMIN Q3 • Goals for Q2 only • Goals for Q2 and Q3

17. Conclusion • An improvement of the two previous approaches • Stable and accurate HIGH responsiveness • Low overhead, allow sharing • Robust

18. Discussion • Why the goal of stability is in conflict of responsiveness? • For class fencing when the hit rate have knees, how do we find the intercept point of HT and hit rate curve? • Is it always possible that the hit rate estimator algorithm can converge to the correct point?