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An Analytical Model for Buffer Hit Rate Prediction

An Analytical Model for Buffer Hit Rate Prediction
An Analytical Model for Buffer Hit Rate Prediction

An Analytical Model for Buffer Hit Rate Prediction*

Yongli (Lily) Xi, Patrick Martin and Wendy Powley

Dept. of Computing and Information Science

Queen’s University

Kingston, Ontario

K7L 3N6

Abstract

Of the many tuning parameters available in a database management system (DBMS), one of the most crucial to performance is the buffer pool size. Choosing an appropriate size, however, can be a difficult task. In this paper we present an analytical modeling approach to predicting the buffer pool hit rate that can be used to simplify the process of buffer pool sizing. Since the buffer replacement algorithm determines the buffer hit rate, we model the replacement algorithm which, in the case of DB2/UDB, is a variation of the GCLOCK algorithm. A Markov Chain model of GCLOCK is used to estimate the hit rate for a buffer pool. We evaluate the accuracy of the model’s estimates with experiments carried out on DB2/UDB with the TPC-C benchmark. The model is validated for both single and multiple buffer pool cases.

1Introduction

Today’s database management systems (DBMSs) require database administrators (DBAs) to configure and tune the database to achieve optimal performance. However, since the tasks of configuring and tuning DBMSs are complex and time-consuming, there has been an increasing interest in self-tuning DBMSs, which shift some of the responsibility from the DBA onto the DBMS itself [1][6].

The performance of a DBMS is greatly influenced by the effective use of main memory. A particularly important use of memory is the buffer area, which is a cache for data pages between database applications and the physical database files. It consists of

* This research is supported by IBM Canada Ltd., Communications and Information Technology Ontario (CITO) and the National Science and Engineering Research Council.

one or more buffer pools, and needs a buffer manager to transfer data between buffer pools and physical disk. Efficient use of the buffer area can reduce the number of disk accesses, which improves the system’s performance.

Tuning the size of the buffer pool is therefore crucial to achieving good performance for a DBMS. Several heuristics exist to assist DBAs in this task but often fine tuning becomes a trial and error process. In this case, once the system is configured, the buffer pool hit rate(s) are monitored while the workload is running. If the hit rates are unacceptable, the system is reconfigured and monitored again. This process can be time consuming for a DBA. Our goal is to build an analytical model that can effectively predict buffer pool hit rates.

The buffer hit rate depends on the buffer replacement scheme used by the DBMS. Least Recently Used (LRU) is a popular buffer replacement policy that gives good performance over a wide range of workloads, CLOCK and GCLOCK are also popular policies because of their simplicity and their ability to approximate the performance of LRU [4]. A variation of GCLOCK is used in IBM’s DB2 Universal Database (DB2/UDB) [5].

In this paper, we present a model of the GCLOCK algorithm that is used to predict buffer pool hit rates for DB2/UDB. We extend an approach by Nicola, Dan and Dias [8], which develops an approximate analysis, using a Markov Chain model, for the GCLOCK policy under the Independent Reference Model (IRM). IRM applies to many database transaction processing workloads and assumes that each buffer page access is independent of all previous references. The Markov Chain model of GCLOCK is based

on a partitioning of the database by access frequency. We partition the database using information provided by the DB2/UDB trace facility [5].

The remainder of the paper is organized as follows. Section 2 discusses the GCLOCK algorithm, the development of the Markov Chain Model and its solution. Our method of analyzing DB2/UDB trace data and predicting the buffer pool hit rate is described in Section 3. Section 4 compares our model’s predictions, for both single and multiple buffer pool configurations, with the hit rates achieved by DB2/UDB for the same workload. Section 5 draws conclusions and summarizes the paper.

2Analytical Model

In this section, the main algorithm and relative key issues are discussed. We start by looking at the GCLOCK buffer replacement scheme. Based on this algorithm, we introduce our Markov Chain model, the way we solve it, and the resulting equations used to predict buffer pool hit rates.

2.1 GCLOCK Algorithm

Consider a database consisting of P partitions, such that the access to database pages

p P, and r p be the within a partition is uniform. Let s p be the size of partition p, 1≤≤

probability of each access to a page of partition p. We assume that access to database pages follows the IRM model, that is, each page request is independent of all previous requests.

All pages in the buffer pool are organized in a circular queue and each page has an associated counter. When a page from partition p is first loaded into the buffer its counter is assigned an initial weight, I p. When the system receives a request to access a page it first scans the buffer to find the requested page. If the page is already in the

buffer, that is, there is a buffer page hit, the counter associated with that page is set to the hit weight, H p . If the desired page is not in the buffer and the buffer is full then a buffer page must be selected for replacement. A clock pointer sweeps through the buffer pages,looking to replace the first page with zero count. On a page miss, the clock pointer points to the buffer page immediately following the page that was brought in for the previous miss. If the value of the counter associated with the page is zero then the page is selected for replacement, otherwise the counter is decremented and the next buffer page in sequence is examined. The process goes on until a page with zero count is found and selected for replacement. If the selected page is dirty then it is written out to disk before it is replaced. The new page is read into the buffer location and its counter is set to the initial weight, I p . The clock pointer advances to the buffer page immediately following the page that was replaced. Note that the clock pointer does not move on a buffer pool hit.

We denote the maximum weight of partition p by L p , where L I H p p p =max(,).For simplicity, we assume that hit weight is equal to initial weight, that is, on a hit the counter of the buffer page is reset to the initial weight. In a research report done by Nicola, Dan and Dias, an extensive analysis is done for the case where the initial and the hit weights may not be the same [7].

Suppose the size of the buffer pool is B pages. A buffer page, b i , B i ≤≤1, can be from any database partition p , P p ≤≤1, and will have a counter value in the range from 0 to L p , where L p is the maximum weight for that partition. Since each page in the buffer is a different distance from the clock counter, each page will be in a different state.Thus, if we build an exact Markov model for the GCLOCK policy, it will require

B L p p P

+=∑11 states, which is a very large number. For example, for a buffer size of 1000pages, 4 partitions, and each partition with a maximum weight of 3, an exact model has 1048 states. Therefore, we need to resort to an approximate model. If we do not differentiate between buffer pages, that is, if we ignore the distance difference between buffer pages from the clock pointer, then we can build a simplified Markov model having the number of states equal to ()L p p P

+=∑11[8].

2.2 Markov Chain Model

The objective of the model is to first determine the buffer hit probability h p for each partition, and then to evaluate the overall buffer hit rate. The buffer hit rate for partition p is the probability that pages from partition p that are brought into the buffer at steady state. Let N p be the steady-state average number of pages of partition p in the buffer, and let s p be the size of this partition, in pages. Then the buffer hit probability for this partition is

p p

p s N h =. (1)If we define r p as the probability of accessing the p th partition, the hit probability for the overall database is then

h r h p p p P

==∑1.(2)

2.2.1 Defining the Model

A list of the symbols used in the model is given in Table 1. As stated earlier, our approximate model has ()L p p P

+=∑11 states. The Markov chain for this model represents the states of an arbitrary buffer page at the instant of a random page request. We use (p,

i), where P p ≤≤1, 0≤≤i L p , to represent a state that corresponds to a buffer page of partition p , with a count i . In other words, the steady state probability of being in state (p,i) is the probability that an arbitrary buffer page is of partition p and has a count i . A portion of this Markov chain model, which corresponds to partition p , is shown in Figure

1. The complete Markov chain includes all partitions and, since the partitions are similar,we can extend the analysis for partition p to the Markov chain corresponding to the whole database. The transitions among the parts of the Markov chain (corresponding to different partitions) occur only through the two end states, (p, 0) and (p, L p ), 1≤≤p P ,as shown.System parameters

P Number of data partitions

s p Size of partition p (in pages)

r p Probability of accessing the p th partition

I p Initial weight assigned to partition p

H p Hit weight assigned to partition p

L p max(I p , H p )

B Buffer size for this partition (in pages)

Model variables

h p Hit probability of partition p

m p Miss probability of partition p

h Overall buffer hit probability for this partition

m Overall buffer miss probability for this partition

n p,i Average number of buffer pages that come from partition p

with count i

n 0Average number of buffer pages with count 0

N p Average number of buffer pages that come from partition p

Table 1: List of symbols and their meanings

p /s p

Figure 1: Markov Chain Model for Partition p

Let n p,i be the steady-state average number of buffer pages that come from partition p with count i , 0≤≤i L p . Summing the number of buffer pages of partition p with all possible count values we get N p , which is the average number of buffer pages that all come from partition p . That is

∑==p L i i p p n N 0,, where P

p ≤≤1(3)

We denote n 0 to be the average number of buffer pages with count zero from all partitions at steady state. In a similar manner as with N p , we can derive n 0 by summing all buffer pages from any database partition having zero count. Thus we have

<2>

∑==P p p n n 10

,0(4)

We define a clock cycle as a complete rotation of the clock pointer through all pages in the buffer. From our previous discussion of the GCLOCK model, we can see that at each buffer pool miss, the clock traverses buffer pages until it finds a buffer with count zero. As outlined earlier, when such a buffer miss occurs, the clock points to the buffer page immediately following the last page read from disk. So in a complete clock cycle, the number of buffer misses equals the number of buffer pages with a zero count encountered during that cycle.

2.2.2 Transition Relationships Derivation

We are now ready to discuss the transition relationships between all model states and we justify the probability of transition from one state to another as shown beside the edges in Figure 1. For clarity, we divide the transition relationships into 4 groups, labeled <1>,

<2>, <3> and <4> in Figure 1, and discuss them individually.

In the model, the basic approximation assumes that the average number of misses in a complete cycle of the clock is equal to the average number of buffer pages with zero count at steady state, n 0. This is an approximation because pages with zero count may have been hit and converted to pages with a non-zero count, during the time the clock pointer makes one pass of the buffer. Nicola, Dan and Dias [8] claim that this type of page request is typically a small portion of the total requests. Our experiments also verified that the impact of this assumption is not significant.

Given that the number of misses in a cycle of the buffer is n 0, then the probability that a particular page’s counter is decremented on a miss is 10

n . Therefore, for an

arbitrary page request, the transition probability from state (p, i) to state (p, i-1) is m n 0,0≤≤i L p , where m is the overall buffer miss probability. Note that the probability of a transition out of state (p, 0) due to a miss is also m n 0

, which corresponds to the probability of replacing an arbitrary buffer page having a zero count. This justifies the group <1>transition.

We defined the probability of accessing a partition p to be r p , and the size of partition p to be s p . In other words, there are s p pages in this partition, and they altogether have access probability r p . Thus an arbitrary page of partition p is requested with probability p p

s r . As stated earlier, whenever a page is hit, the desired page has already

been brought into the buffer and its counter is reset to L p . The transition probability from

any state (p, i), 01≤≤?i L p (), to state (p, L p ) is therefore p

p s r . This justifies the group <2> transition.

We now derive the group <3> relationship. For a given buffer page request, the probability that a page will be brought into the buffer is equal to the probability that a page request causes a buffer page miss, that is, m p . Since the probability of accessing the p th partition on a certain request is r p , the probability that a page of partition p is brought into the buffer is r p m p . Also, given that the probability a miss will happen for the whole

database is 10

n , and that once a page is brought in, its count is reset to the maximum weight, the transition probability from each of the states (p, 0), 1≤≤p P , to state (p, L p )

is thus r m n p p

0. Note that a buffer page from any partition with zero count may be replaced by a particular partition page. r m n p p

0 is thus the transition relationship from all other parts

of the Markov chain to the particular partition we are discussing. This justifies group

<3> of transition relationships in the figure, namely the transitions from states (1, 0)through (P, 0) to state (p, L p ).

Likewise, a page from partition p may also be replaced by a page from any other partition. Using the same justification as in group <3> we can easily derive transition probabilities in group <4>, that is, for the set of arrows pointing from state (p, 0) to state (1, L 1) through state (P, L P ), which are 0

n m r i i respectively. (i, L i ) denotes the state of a page which is from partition i with maximum weight L i . This is the probability of a transition out of state (p, 0) to the states related to buffer pages of any partition (including

itself of course) due to a miss. We observe that r m n r m n r m n m n p p 11022000

+++=, which conforms to our earlier justification in group <1> that the transition probability out of state (p, 0) on a buffer page miss is

m n 0

.2.2.3Solving the Model

Given that the buffer size is B , and number of buffer pages from partition p with count i is denoted by n p,i , we define π(p, i), the steady-state probability of a page being in state (p,i) as π(,),p i p i

n B

=

We now write the Kolmogorov’s Forward Equations of the Markov chain,balanced at state (p, i). Referring to the Markov chain model shown in Figure 1, we see that there are two outgoing paths for any inner state (p, i), of the model, 0

p s r respectively. There is also one entering path, with rate m n 0

from state (p, i+1). For some steady state (p, i), the number of buffer pages leaving the state should equal to the number of buffer pages entering the state. The steady state probability of a page being in state (p, i), π(p, i), can be also viewed as the portion of the page’s lifetime that it is in state (p, i). The rate of buffer pages leaving the state can therefore be expressed as ()(,)m n r s p p

p i 0+π. Likewise the rate of buffer pages entering state (p, i) can be expressed as m n p i 01π(,)

+. Thus we have:()(,)(,)m n r s m n p p p i p i 00

1+=+ππUsing the same idea, we can derive the balance equation for the “end-state” (p,L p ). As shown in Figure 1, the outgoing transition from this state has a rate 0n m . There are two sets of incoming transitions into the state. One set has a rate of

0n m r p p , and the other set has a rate of

p p s r . We therefore get the following equation

∑∑?==+=10),(

)0,(10),(0p L i i p p

p

i P i p p

Lp p s r n m r n m πππ, where P

p ≤≤1Thus,

p

p

p p p L p p p

N s r

m r n s r n m p +=+,0)(We can then derive the general relationship

,0

,)1(p i p

p

i p n s r m n n +=(6)and,

m

s r m n n r n p L p

p p p 1

00

0,)1(++=

(7)N p can thus be derived:

))

1(1

1(1

0++?=p L p

p p p s r m n s N , where P

p ≤≤1(8)Since all pages in buffer must sum to the buffer size B, we have

B

N P

p p =∑=1

(9)We finally get a nonlinear equation,

))

1(1

1(11

0=?+?

∑=+B s r m n s P

p L p

p p p (10)

which is the key equation in our approach to estimating buffer hit rate.

We note that the quantities m and n 0 always appear together as the ratio m

n 0, and so can be looked on as one variable. Equation (10) can be solved iteratively for the ratio m

n 0 using the bisection method [2], which according to our experiments, converges very quickly. Once the ratio m

n 0 is determined, we can evaluate N p , P p ≤≤1, from equation (8), and h p , the buffer hit probability for each partition from equation (1). Finally we can compute the overall hit probability for the whole database, using equation (2).3 Method to Predict Hit Rates

From the derivation of the Markov model, we can see that, given a certain buffer size, we need the following input values to estimate the hit rate for a specified buffer area:

P

Number of data partitions s p

Size of partition p (in pages)r p

Probability of accessing the p th partition L p

maximum weight, which is max(I p , H p ) I p

Initial weight assigned to partition p H p Hit weight assigned to partition p

According to our assumptions, L p = I p = H p , so the last three inputs can be looked on as one parameter.

3.1Partitioning Criteria and Approach

We first need to partition the database such that the access frequency to database pages within a partition is uniform. There are various ways to define "uniform". For example,it can mean the same access frequencies or the same access patterns. Dan [3]

distinguishes three types of access patterns, namely, 1) locality within a transaction, 2) random accesses by transactions and 3) sequential accesses by long queries.

Our experimental workload is generated by the TPC-C OLTP benchmark. OLTP workloads in general, and TPC-C specifically, have few sequential accesses. This means that partitioning based purely on access pattern is not suitable. It is also the case that, for multiple buffers, one criteria often used to configure database objects to buffer pools is the access pattern for those objects. A buffer pool may therefore only reflect one type of access pattern. OLTP workloads typically have skewed (non-uniform) data access, that is, we can identify a set of "hot" pages that receive a greater portion of the accesses. This property of OLTP workloads can be adequately modeled by partitions based on access frequency [3].

3.2Disk/Memory Reference Trace

In order to collect detailed information on page access frequencies, we trace each page access by a transaction for a sample run of the workload. OLTP workloads are almost always cyclic in nature [3], so a large enough sample will adequately reflect the complete set of pages accessed by that workload. Since the buffer area sits in the middle of the memory hierarchy, all table accesses have to go through buffer pool. Tracing the buffer pool references thus provides the required information on database references.

We need to trace every read and write access to the buffer pools and to record the physical page number and database object accessed. The DB2/UDB trace facility provides this information. For each buffer pool reference, whether it is a read or a write, we can trace two activities. The system issues a FIX to fix a page before an access, and an UNFIX after the access is completed. From the trace we obtain the object page

number for each page accessed which is the address of the page relative to the start of the object.

3.3Database Partitioning

In our experiments, we observed that the accesses were concentrated on a small number of database pages, and a relatively large part of the database pages get only a few access requests. In a typical experimental run, 3% of the distinct pages account for 80% of the total database accesses. On the other hand, over half of the database pages only get around 3% of the accesses. It is thus straightforward to group database pages based on access frequencies.

We divided the database into four partitions based on access frequency. We call these partitions "hot"-- most frequently accessed pages, "cold" -- rarely accessed pages, "warm" -- pages whose access frequency falls in between and “freezing” – unused database pages. Since the pages in the freezing partition have a 0% probability of being accessed, we ignore these pages in our analysis and only consider the other three partitions.

Three types of counters are maintained. For the single buffer pool case, we count the distinct pages the system has accessed during the testing period, that is, the number of distinct pages (S) that have appeared in the trace file. We also count the number of accesses associated with each distinct page (a i), and the number of overall accesses (A). After partitioning, we can add up the number of distinct pages in each partition to get

s

p the size of each partition. We can also get the number of overall accesses in one partition by using

∑==p

s i i

p a a 1We can then calculate the overall access probabilities for each part as A

a r p p =

Note that ∑==P p p S s 1

, remembering P is the number of the partitions, and S is the overall

database size. The maximum weight for each database partition L p , is assigned the value 2 since DB2/UDB uses weight values of 0, 1, or 2 to indicate the locality of a page.

For the multiple buffer pool case we treat each buffer pool and its associated database objects as an individual small database. In this way, we split the multiple buffer pool case into several single buffer pool problems. We collect the necessary model inputs for, and apply our analytical model to, each of them. Experiments for both the single and multiple buffer pool cases are presented in Section 4.

4 Experiments

All the experiments were run with DB2/UDB Version 7.1 under Windows NT on an IBM Netfinity 5000. The machine was configured with one 400 MHz processor, 1 GB of RAM and 5 disks. The workload used in the experiments is the TPC-C benchmark,which typifies an OLTP workload [9]. The database schema from the TPC-C benchmark is composed of nine relations. TPC-C simulates the activities of a wholesale supplier and includes 5 order-entry type transactions. The database we use has 10 data warehouses,each of which is approximately 100MB bytes in size. Thus the database has about 250,000 4K pages. In our experiments, 40 clients, or simulated “terminal operators”

issue the transactions against the database. The relative frequencies of each transaction are as specified in the benchmark.

4.1System Performance Monitoring

In order to validate and evaluate our buffer hit rate estimates, we collect real buffer hit rates under different buffer configurations to compare with our model estimation. For each configuration, we let the TPC-C driver run for 5 minutes to allow the system to stabilize. Each time we run the TPC-C driver for a specified configuration, we collect "snapshot" data five times. The buffer hit rate for each snapshot is calculated, and the average of the snapshots is used to get the average hit rate for a run. Each snapshot period is 8 minutes long. We use the mean from each configuration as the final system figure in our model comparison studies. A statistical analysis of the results reveals that the system performance is quite stable. A confidence level of 95% is used in the analysis.

Each of our trace files contains about 12,000 trace records. Since we need a trace file as long as possible to ensure reliable and complete data, we collect a long series of trace files, and then concatenate them into a single file of over 600 minutes worth transactions and a size of 136 MB.

Our experiments showed that the number of distinct pages accessed from the 136-MB trace file is not significantly more than that from a 60-MB trace file. Although the first file is almost twice the size of the second, it contains less than 50 more distinct pages than the smaller file, compared to the 3438 pages in total. This means that the access to the database is very concentrated on a part of the pages in TPC-C activities and that the workload is cyclic as assumed.

Our analysis of the trace file showed that there are 3438 distinct pages accessed, and the total number of accesses is 183770. When studying the distribution of access frequencies, we see that 80% of the total page accesses are concentrated on roughly 2% of the distinct pages accessed. Over half of all distinct pages are accessed less than 4 times. We can easily distinguish the "hot" and "cold" partitions, and the pages in between can be treated as the "warm" partition.

We tried various partitioning schemes on the accessed pages, which are shown in Tables 2 through 4, to evaluate the impact of the partitioning criteria on the performance of our approach.

Partition Size

(pages)Percentage of

database

Number of

Total accesses

Access

probability

Hot (>250 requests)820.023*********.8050 Warm

(5~250 requests)

14880.43282300880.16377 Cold (<5 requests)18680.5433357390.03123

Table 2: Partitioning Plan 1

Partition Size

(pages)Percentage of

database

Number of

total accesses

Access

probability

Hot (>250 requests)820.023*********.8050 Warm

(4~250 requests)

25120.73066341840.18606 Cold (<4 requests)8440.2454916430.00894

Table 3: Partitioning Plan 2

Partition Size

(pages)Percentage of

database

Number of

total accesses

Access

probability

Hot (>150 requests)1000.029*********.82474 Warm

(4~150 requests)

24940.72542305650.16632 Cold (<4 requests)8440.2454916430.00894

Table 4: Partitioning Plan 3

When partitioning the database, we attempted to find the smallest partition that was most frequently accessed. In this way, the hottest partition, which is relatively small in size, is almost guaranteed to remain buffered in the memory given that the buffer is large enough. We found experimentally that the estimates produced from the first partitioning plan were closest to the system performance. Therefore we use partitioning plan 1 as the initial input to our analytical model.

4.2 Single Buffer Pool Experiment

Figure 2: Single Buffer Pool Hit Rate

Hit rate values collected from the real system and estimated from the model for a single buffer pool of 400 to 2400 4K pages are shown in Figure 2. We can see that the two curves almost coincide with each other, except for small differences when the buffer size is very large compared to the total amount of the database that is accessed. However, our comparison result shows that the difference between our model result and the real system performance is not significant. On average, the estimated error is around 2%. Thus we

can conclude that our analytical model provides a good approximation for DB2/UDB’s

buffer hit rate for an OLTP workload and a single buffer pool configuration.

4.3 Multiple Buffer Pools Experiment

For the multiple buffer pool experiments, we evaluated our model with two different

buffer pool configurations -- one with three buffer pools and one with four buffer pools.

We present the results of the experiments using three buffer pools here. The four buffer

pool case, which achieved similar results, is described elsewhere [10]. To apply our

model to the multiple buffer pool case, we split the problem into N disjoint single buffer

pool problems, where N is the number of buffer pools. After obtaining the mapping

between database objects and buffer pools, we treat each buffer pool as an individual unit

and estimate the buffer hit rate.

We assign each database object to its own tablespace, so that each database object

can be distinct in the trace data. The tablespaces are assigned to buffer pools as follows:

?BP0 – all data tables

?BP1 – indices for the new_order, orders and history tables

?BP2 – indices for the item, stock, customer and order_line tables In the experiment, we vary the size of one buffer pool while holding the sizes of

the other two constant. We collect data for five snapshots for each buffer pool size, and

take the mean as the real hit rate. We vary each of the buffer pools in turn.

The hit rate comparison for each of the buffer pools are shown in Figures 3

through 5. We can see that the estimated hit rate curves are very close to the real system

performance curves, especially for buffer pool BP1 and BP2 where index tables are

stored. As in the single buffer pool case, we see a slight difference when BP0 is large. In

this case, the hit rate estimated from our analytical model is higher than the real system

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