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Published byFrancis Green Modified over 9 years ago
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Goal-Oriented Buffer Management Revisited Kurt P. Brown, Michael J. Carey, Miron Livny Presented by Mike Nie
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Agenda Goal-Oriented Basics Criteria for Success Previous Approaches Class Fencing Experiments and Results Conclusion and Discussion
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Goal-Oriented Basics This paper focus on the control of response time of a specific workload. Knob: Disk buffer memory allocation. K Target DBMS response time goal observed response time memory allocation adjustment
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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
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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.
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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.
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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
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Dynamic Tuning Response time estimator: R est = (1.0-HIT est (M)) * D Hit rate estimator: HIT est =1 - a/M b Buffer allocation mechanism: Partation buffer pool based M calculated above for each class.
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Dynamic Tuning Issues Overshoot Low responsiveness No shared data between workloads
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Fragment Fencing - I Response time estimator: Response time and buffer miss rate are directly proportional. Hit rate estimator: HIT target = 1.0-(M obsv *(R goal /R obsv )) Goal: determine the minimum number of pages that must be in memory to achieve an overall target hit rate for the class.
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Fragment Fencing - II Buffer allocation mechanism Passive allocation Issues: Having trouble when fragment is not uniform. High overhead due to passive allocation mechanism.
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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!
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Class Fencing - Hit Rate Estimator
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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.
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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)
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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
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Conclusion An improvement of the two previous approaches Stable and accurate HIGH responsiveness Low overhead, allow sharing Robust
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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?
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