ChimeraTL: Transfer Learning in DBMS
with Fewer Samples
Tatsuhiro Nakamori3 , Shohei Matsuura1 , Takashi Miyazaki1 ,Sho Nakazono1 , Taiki Sato1 , Takashi Hoshino2 , Hideyuki Kawashima3
LY Corporation1 , Cybozu Labs2 , Keio University3

Abstract—In the field of database management systems(DBMS), it is essential to build a performance prediction modelwith less data from the target environment, motivating theapplication of transfer learning. While some parameters in
DBMS have similar effects on performance across different
hardware environments, others can have varying effects that
depend on underlying hardware limitations. Previous studies do
not leverage this information to improve the transfer learning. We
propose ChimeraTL, a novel method that accounts for different
parameter types to enhance transfer learning. Our experiments
demonstrate that ChimeraTL needs only 50% of samples thatstate-of-the-art methods require to minimize the prediction error
to under 10%.
Index Terms—Transfer Learning, DBMS

I. I NTRODUCTION
A. Motivation
Database management systems (DBMS) are an integral
component of applications. One aspect of Database management systems (DBMS) that makes it essential in many applications is its adaptivity. DBMS has many configuration parameters (e.g. 190 in MySQL [1] and 170 in PostgreSQL [2],[3]) that users can adjust to optimize to specific settings andmeet the user requirements. Recent studies have emphasizedbuilding performance prediction models instead of relying on
heuristics to find optimal configurations [4].
While existing studies target learning performance prediction models for unseen workloads [3]–[7], they assume that themodel is learned from the same hardware environment where it
is used. In reality, DBMS users can test the performance of the
DBMS with various configurations in a testing environmentand build a performance prediction model to estimate theperformance of the DBMS in the production environment [8].
However, since a testing environment is often a scaled-down
version of a production environment, the model learned fromthe testing environment may not be accurate in predicting theperformance of the production environment. What developers
can do instead is to extract the knowledge of the testing
environment that can be reused to build an accurate modelfor the production environment.
Such an approach to reduce the learning cost by utilizing thedata from a different but related environment is an example ofThis paper is based on results obtained from “Research and Development
Project of the Enhanced Infrastructures for Post-5G Information and Communication Systems” (JPNP20017) and (JPNP16007) commissioned by the
New Energy and Industrial Technology Development Organization (NEDO)
and JSPS KAKENHI Grant Number 22H03596.

transfer learning [9], [10]. Our motivation is to apply transfer
learning techniques to learn a DBMS performance prediction
model using fewer samples from the target environment.
B. Problem
Transfer learning for configurable software systems has
been discussed in previous literature [9]–[12]. While previoustransfer learning approaches [9]–[11] are effective in specificsettings, they must consider the following properties of DBMS
parameters to make the transfer of knowledge more effective.
1. Parameters are not binary: Previous studies focused
their evaluation on cases in which the configuration parameters
were binary, i.e., the parameter is either on or off [9], [11],[12], but the parameters in DBMS often have a wide range ofvalues (e.g. buffer pool size in MySQL [13]). Since there are
numerous parameters with wide value ranges, existing transfer
learning approaches would require more samples than desired
to learn an accurate model for the vast configuration space.
2. Parameter effects can be machine specific: Althoughstudies have shown that the performance functions of many
parameters between two environments have a linear relationship [11], [12], employing a strategy based on the assumptionthat all parameters follow this pattern can cause a significant
bias in the model. Fig. 1 shows the relationship between thethroughput of two environments for varying configurations in
LineairDB [14]. It is evident that the relationship is not linear
because two machines have different saturation points when
the number of threads is varied. This example shows that someparameters are machine specific, having different effects on
performance depending on the hardware limitations.
To build an accurate performance model of DBMS withfewer samples from the target environment, we need to consider the above properties by exploiting the knowledge of parameters that are consistent in their performance impact acrossdiverse hardware environments while avoiding the inaccuracies
caused by transferring the knowledge of parameters that havemachine-specific effects. Nonetheless, to our knowledge, no
existing transfer learning technique considers such a design.
C. Contribution
We present ChimeraTL, a transfer learning pipeline to build
an accurate performance model of DBMS with fewer samples from the target environment. It combines three existing
methods: ModelShift [11], DataReuseTL [10], and L2S [9],in a way that maximizes their advantages and minimizes theirdisadvantages. It consists of three procedures: (1) parameterselection [9] and separation, (2) linear transformation learning[10], [11], and (3) machine-specific parameter learning.
First, ChimeraTL starts by selecting parameters that havesignificant impact on performance and separating them into
universal parameters (parameters that have similar performance effects across different environments) and machinespecific parameters (parameters that have different performance effects depending on the hardware limitations). Whileparameter selection has been a common preprocessing technique in DBMS performance prediction models [1], [4], tothe best of our knowledge, none of the previous studies haveconsidered classifying different types of parameters. Previous
methods have assumed that all parameters have same behavior
in different environments. This assumption causes a negativetransfer to the model learning in the target environment.
Parameter separation in ChimeraTL is a novel technique thatallows the integration of ModelShift, DataReuseTL, and L2S
while minimizing the negative transfer that may occur in eachmethod.
After identifying the universal parameters, ChimeraTL samples data of the universal parameters from the target environment. Using the sampled target data and the source data of
the same parameters, ChimeraTL learns a linear transformation
between the two environments. ChimeraTL then linearly transforms the source data of universal parameters and uses themas additional training data to build a performance prediction
model for the target environment. By linearly transforming
the source data, ChimeraTL can reuse the knowledge of thesource environment while avoiding the bias caused by thedifference between the source and target environments. Once
there is enough data on the universal parameters, ChimeraTL
prioritizes sampling the data of machine-specific parametersfrom the target environment.
Compared with learning the model from scratch in thetarget environment, ChimeraTL minimizes the prediction error
to less than 10% using 70% fewer samples. None of thepreviously noted state-of-the-art transfer learning techniques[9]–[11] can achieve this level of accuracy with the same
number of samples.
II. P RELIMINARIES
A. Objective
The goal of transfer learning is to reduce the learning costin a target environment by utilizing the data from a different
but related environment [9], [10]. In this paper, we regard
the number of samples from the target environment to be theindicator of the cost.
We define a sample as a pair of DBMS parameter settings
and the corresponding measure of DBMS performance. In
order to collect one sample, we set the parameters to desired
values, run a workload for the DBMS to process, and measure
the performance during the workload. The duration of one
sampling iteration depends on the workload. Previous study
has taken the average performance over 5 minutes as one

sample [4]. By using this sample as a training data, performance prediction models can learn the relationship between
the parameters and the performance.
B. Transfer Learning Methods
In this paper, we compare the transfer learning methods forthe performance prediction of configurable software systems.
ModelShift [11] is a type of transfer learning where theperformance model learned from the source environment islinearly transformed to predict the performance of the target
environment as in Fig. 1. The previous study has shown that
ModelShift requires less than 10 samples from the target environment to learn appropriate linear transformation coefficients[11].
DataReuseTL [10] uses the source environment data anddata samples from the target environment to build a prediction
model. DataReuseTL works well even with a few samples
from the target environment when the source and target
environments have minor differences.
L2S [9], [12] identifies important parameters likely to
be shared between the source and target environments andsamples the data of those parameters to build a model forthe target environment. Because this method does not use thesource environment data for model construction, there are noconcerns about the bias caused by the difference between thesource and target environments.
C. Universal and Machine-specific Parameters
Parameters in DBMS (and other configurable softwaresystems) can be categorized into two types: universal andmachine-specific. Universal parameters affect the performanceof a DBMS similarly in different environments. Formally, aparameter pi is universal if it satisfies the following equation:
ftgt (pi ) = β×fsrc (pi ) + β0(1)where ftgt (pi ) represents the performance function in a target
environment, and β×fsrc (pi ) + β0 denotes a linear transformation of the performance function in a source environment.
Fig. 2a shows an example of a universal parameter in LineairDB. The figure shows that the performance function of theparameter has similar shapes in different environments and thatthe performance in one environment can be approximated bylinearly transforming the performance in another. Consideringthat it takes less than 10 samples from the target environment
to learn the linear transformation [11], the data of universal
parameters from the source environment should be exploited
to build a model using fewer samples.
The remaining parameters are machine-specific parametersthat behave differently in different environments. Fig. 2bshows an example of such a parameter. The three environmentsin the figure have varying trends for the same parametervalue. Using the data of machine-specific parameters from thesource environment to build a model for the target environmentis problematic because the data from the source may notreflect the actual relationship between the parameter values
and performance in the target.

8c_16g Throughput

Fig. 1: Association of LineairDB [14]throughput between two environments and alinear regression model – Each point shows aparameter value. 8c_16g represents 8 core,
16 GB RAM machine.

26000

4c6g
8c12g
16c24g

24000

Throughput

Throughput

16c_24g Throughput

14
18
22
24
20
16
000
12
225 00 r-squared = 0.852
0 n = 1300
10
2 00 label: number of threads08
175 00
0
0
6
5
1 00
0
4
125 000
100 00
2
750 0
0 1
500 0
0
250
00
00
00
00
00
00
400 600 800 1000 1200 1400

22000
20000
18000
1

10
20
checkpoint_interval

30(a) Universal parameter

225000200000175000150000125000100000
75000
50000

4c6g
8c12g
16c24g05

1015
clients

2025(b) Machine-specific parameter

Fig. 2: Performance functions of universal and machine-specific parameters – Each line represents the performance function of a parameterin a different environment. For example, 4c6g represents the performance
function of a parameter in an environment with 4 CPU cores and 6 GB ofmemory.
1. Parameter Separation

D. Limitations of Existing Methods
Each method described in Section II-B does not consider thedifference between universal and machine-specific parameters,
which leads to disadvantages in different ways. ModelShift and
DataReuseTL work under the assumption that all parameters
are universal. Since machine-specific parameters in DBMS
may affect the performance differently depending on theunderlying hardware environment, the resulting model may
suffer from a bias caused by the difference between the source
and target environments. Similarly, L2S does not distinguishbetween machine-specific and universal parameters. Because
L2S does not use the source environment data for model
construction, there are no concerns about the bias. However,it does not exploit the source environment data of universal
parameters, losing on the opportunity to reduce the amountof samples necessary from the target environment to build
a practical model. This is especially problematic in the caseof DBMS. Since the parameter space of DBMS is vast, L2S
has to compensate for the lack of source environment data bycollecting more samples from the target environment.
We need to differentiate between universal and machinespecific parameters to build a high-accuracy DBMS performance prediction model using fewer samples from the target
environment.
III. C HIMERATL
ChimeraTL is a transfer learning method that combines
the elements of ModelShift, DataReuseTL, and L2S in a
way that maximizes their advantages and minimizes theirdisadvantages. It consists of three procedures: (1) parameterselection and separation, (2) linear transformation learning,and (3) machine-specific parameter learning.
A. Parameter Selection and Separation
The first step of ChimeraTL is to select parameters that significantly impact performance and separate them into universal
and machine-specific parameters. In the studies of DBMS
performance models, parameter selection is a common practice

Machinespecific

Universal

Source2. Machine-specificParameter Sampling

Target
3. Linear Transformationfrom Source to Target
4. Fit Model on
Sampled Data &Transformed Data

Fig. 3: ChimeraTL Pipeline
because the number of parameters in DBMS is usually large,
and not all of them significantly impact performance [1], [4].
As in L2S [9], we use stepwise regression [15] to automaticallyselect impactful parameters.
While many studies have incorporated parameter selection
into their approach, to the best of our knowledge, no existing
method separates parameters into universal and machinespecific ones. Therefore, we propose an algorithm that uses
Bhattacharyya distance [16] to separate parameters based on
the difference in their performance functions between two
environments.
Bhattacharyya distance is a measure of the similarity between two probability distributions. We use Bhattacharyya
distance for parameter separation because it considers theentire distribution, accounting for variations beyond mean,variance, or absolute differences [17]. Additionally, Bhattacharyya distance’s symmetric nature allows straightforward
threshold setting, providing consistent interpretation regardlessof the chosen reference distribution.
The parameter separation algorithm is shown in Algorithm 1. The process requires data of performance functions

Algorithm 1 Parameter separation

Algorithm 2 Sampling in the target environment

Require:P : set of parameters
f1 : performance function in primary source environment
f2 : performance function in secondary source environment
T : threshold
Ensure:Puv : set of universal parameters
Pms : set of machine-specific parameters

Require:Puv : set of universal parameters
Pms : set of machine-specific parameters
N : number of samples for priority switchingur: sampling rate of universal parametersfsrc : performance function in the source environment
model: performance prediction model
Ensure:Dtgt : target datatgt
Duv
: target data of universal parameterssrc
Duv: source data of universal parameters

1: Puv ← ∅2: Pms ← ∅3: for pi ∈ P do
4:
p1 (pi ) ← normalize(f1 (pi ))5:
p2 (pi ) ← normalize(f2 (pi ))6:d ← BhattacharyyaDistance(p1 (pi ), p2 (pi ))7:
if d < T then8:
Puv ← Puv ∪ pi
9:
else
10:
Pms ← Pms ∪ pi
11: return Puv , Pms

1: function RUN N EXT I TERATION
2:
sample universal ← Random() < ur3:
if sample universal then4:
SampleUniversal()tgt
5:
if size(Duv) = N then6:
ur ← 1 − ur
7:
else
8:
SampleMachineSpecific()
9:

in primary and secondary source environments. Under theassumption that it is cheap to collect data outside the target
environment, we set up two docker containers with different
resource restrictions in a source environment to obtain distinct
data. The initial step of the algorithm is to convert each performance function of a parameter into a probability distribution(Line 4, 5). Then, the Bhattacharyya distance between the two
probability distributions is calculated (Line 6). If the distanceis smaller than a threshold T , the parameter is considered
universal, and otherwise, it is considered machine-specific(Line 7).
B. Learning in Target Environment
Once the parameters are separated into universal andmachine-specific parameters, ChimeraTL is ready to collectdata from the target environment. For the first few samples,
ChimeraTL focuses on collecting data of universal parameters
to learn the linear transformation between the source and target
environments. With just a few samples, ChimeraTL can learn
the linear transformation and reuse the source environment
data of universal parameters for target model construction. After collecting a set amount of samples for linear transformation
learning, ChimeraTL prioritizes collecting data of machinespecific parameters unavailable in the source environment.
One iteration of the sampling process is shown in Algorithm 2. The sampling rate of universal parameters (ur) andthe number of samples for priority switching (N ) are the twoparameters of ChimeraTL that control the sampling process.
At the end of each iteration, ChimeraTL fits a performance
prediction model using the collected data. We use GaussianProcess (GP) regression [18] as the model because it is known
to be effective in modeling configurable systems [4], [6], [7],[9], [10].
1) Linear Transformation Learning: Learning the linear
transformation between the source and target environments isan essential step that enables ChimeraTL to reuse the data

FitModel(Dtrain )10:
11: function S AMPLE U NIVERSAL
12:pi ← RandomChoice(Puv )
src
src
13:
Duv
← Duv
∪ {fsrc (pi )}
tgt
tgt
14:
Duv ← Duv ∪ {sample(ftgt (pi ))}
src
tgt
15:
regression data ← MergeOnConfig(Duv
, Duv
)
16:
β ← LinearRegression(regression data)
17:
Dtgt ← Dtgt ∪ {ftgt (pi )}18:
19: function S AMPLE M ACHINE S PECIFIC
20:
pi ← RandomChoice(Pms )21:
Dtgt ← Dtgt ∪ {ftgt (pi )}22:
23: function F IT M ODEL
24:
universal data ← {fsrc (pi ) | pi ∈ Puv }25:
data ← Transform(universal data, β)26:data ← {d ∈ data | Config(d) ∈/ Config(Dtgt )}27:
data ← data ∪ Dtgt
28:
model.fit(data)of universal parameters from the source environment. Reusing
the linearly transformed source data dramatically reduces thenumber of samples from the target environment required tobuild a model.
Each time a new sample of universal parameters is collectedfrom the target environment, ChimeraTL merges the sample
with the source environment data of the same configurationand fits a linear regression model to the merged data (Line 11).
The linear regression model is used to transform the universal
parameter data in the source environment into an estimationof the performance in the target environment (Line 25). The
transformed data is then combined with the samples from thetarget environment to build a performance prediction model(Line 27).
Unlike ModelShift [11], ChimeraTL does not assume that
all the parameters are universal. Constructing a linear regression model using all the parameters would cause negative transfer because the machine-specific parameters mayTABLE I: Parameters of LineairDB
Parameter

Range

Default value

clients
checkpoint_interval
epoch_durationprefetch_locality
rehash_threshold

1-24
1-30
1-40
0-3
0.1-0.99

1
30
40
3
0.8

not follow the pattern of the universal parameters. Instead,
ChimeraTL only samples the data of universal parameters
selected in Section III-A to learn the linear regression. Similarly, the linear regression model is only used to transform thesource data of universal parameters, and the source data ofmachine-specific parameters are not used in the target model
construction.
2) Machine-specific Parameter Learning:
Initially,
ChimeraTL samples the data of universal parameters more
frequently than the other parameters. However, the benefitof sampling those data diminishes after learning the linear
transformation because the linearly transformed source
data can substitute the target environment data of universal
parameters for model construction. Once there are enoughdata of the universal parameters, ChimeraTL prioritizes
sampling the data of machine-specific parameters from thetarget environment to capture the trends of these parametersthat are not observable in the source environment.
IV. E VALUATION
We compare ChimeraTL with the following four methods:
ModelShift, DataReuseTL, L2S, and L2S+DataReuseTL (acombined approach of DataReuseTL and L2S [9]).
Although the original papers of ModelShift and
DataReuseTL do not conduct parameter selection, we
perform parameter selection using stepwise regression [15]for these methods to achieve the best performance. In
addition, we use the same GP regression model as theperformance prediction model for all the methods to ensure afair comparison of transfer learning approaches.
A. Experimental Setup
For all the experiments, we use LineairDB [14], an opensource transactional storage engine based on Silo [19], togenerate performance data. The parameter space of LineairDB
is shown in Table I.
Table II shows the source and target environments usedin the experiments. We set up a docker container on eachenvironment to control the amount of resources available. We
use two completely different host machines to simulate thecase where developers test their software on a local machineand deploy it on a server.
We measure the performance of LineairDB by running
YCSB-A workload [20]. For each sampling process, thebenchmark is executed for 15 seconds, and the performance isassessed by calculating the average over this duration. In all
the experiments, we assume the workload is the same between
the source and target environments. This assumption is realistic

TABLE II: Source and target environments – The secondary
source environment is used for the parameter separation. The
host machines for the source and target are Apple M3 MAX
laptop and PRIMERGY RX2540 M4 server with Intel(R)Xeon(R) Gold 6130, respectively.
Docker Environment#CoresRAM Size

Primary Source (Laptop)
Secondary Source (Laptop)
Target (Server)

8
4
24

12 GB
6 GB
32 GB

because application developers test their software for expected
workloads before deploying it to the target environment. For
the performance metric, we use the throughput of the database,which is the number of transactions processed per second.
B. Parameter Separation Result
ChimeraTL’s parameter separation algorithm categorizes
clients parameter as machine-specific and the rest as universal. As Fig.2b shows, it is visibly clear that the performance
functions of the parameter do not have a linear relationshipwith each other. The classification results from ChimeraTLalign with our intuitive expectations.
The parameter separation completes in about 14 milliseconds, incurring a negligible cost relative to the overall modeltraining procedure. In contrast, it substantially improves theeffectiveness of ChimeraTL’s transfer learning as discussed in
Section IV-D.
C. Comparison with Existing Methods
Fig. 4 shows the number of samples required to achievecertain level of prediction accuracy for each transfer learning
method. The x-axis represents the mean absolute percentage
error (MAPE) [9], [11], [21] of the performance prediction
model, and the y-axis represents the number of samples fromthe target environment.
While the existing methods require many samples to minimize the prediction error, ChimeraTL can reduce the error to
below 40% with just 10 samples. This result demonstrates
the effectiveness of ChimeraTL’s aim to linearly transformand reuse the source data for the target model construction.
ChimeraTL further reduces the prediction error to under 10%
with 80 samples, while other methods require at least 150
samples to achieve the same accuracy. ChimeraTL’s steady
improvement in prediction accuracy with an increased numberof samples justifies its strategy to prioritize sampling formachine-specific parameters once enough samples are collected for linear transformation learning. With ChimeraTL
achieving the best prediction accuracy for all the number ofsamples, it is clear that ChimeraTL can build an accurate
performance prediction model with fewer samples from thetarget environment.
Contrary to the findings of the previous work that ModelShift needs less than 10 samples to learn the linear transformation [11], ModelShift requires 80 samples to achieve its best
prediction accuracy of just around 40% in our experiments.

L2S
DataReuseTL

L2S+DataReuseTL

400
350
300
250
200
150
100
50
0 70 60 50 40 30 20 10 5
Mean Absolute Percentage Error (%)

Fig. 4: Performance prediction accuracy of transfer learning methods – ModelShift, DataReuseTL, andL2S+DataReuseTL cannot reduce the error below 40% evenwith all the samples from the target environment.
This is because there are machine-specific parameters in LineairDB that interfere with learning the linear transformation.
DataReuseTL and L2S+DataReuseTL display improvementin prediction accuracy, but their prediction accuracy neverreaches below 40% even with all available samples from thetarget environment because there is a huge difference between
the DBMS performance in the source and target environments.
Even though ChimeraTL linearly transforms the source data
and reuses it for the target model construction, the effect ofnegative transfer is minimized because ChimeraTL excludes
the source data of machine-specific parameters in the process.
In contrast to the methods that suffer from the negativetransfer, L2S shows a steady improvement in prediction accuracy as the number of samples increases. Still, L2S generally
requires twice as many samples as ChimeraTL to achieve thesame prediction accuracy. Since L2S does not use the source
environment data at all to train the model, it requires more
samples from the target environment to build a model withthe same accuracy as ChimeraTL.
D. Importance of Parameter Separation
To understand the effect of parameter separation, we compare ChimeraTL with ToyChimera, a variant of ChimeraTL
that does not exclude the data of machine-specific parametersfrom the linear transformation learning and the source datareuse, and samples data randomly instead of giving priority to
specific parameters at different stages of the sampling process.
As shown in Fig. 5, ToyChimera needs much more samples
than ChimeraTL does to learn an accurate model. ToyChimera
requires 400 samples to reduce the error to below 5% when
ChimeraTL can achieve the same accuracy in just 150 samples.
As explained in Section IV-C and shown in Fig. 4, involving machine-specific parameters in the linear transformation
learning and reusing all the source data cause negative transfer
that significantly degrades the prediction accuracy. ChimeraTL
filters out the data of machine-specific parameters for theseprocesses to reuse the source data without suffering from thenegative transfer. Consequently, ChimeraTL can build a more

Proposed Method

Sample Size
(1 sample = 15 sec)

Sample Size
(1 sample = 15 sec)

Proposed Method
ModelShift

ToyChimera

400
350
300
250
200
150
100
50
0 70 60 50 40 30 20 10 5
Mean Absolute Percentage Error (%)

Fig. 5: Performance prediction accuracy of ChimeraTL
and ToyChimera

accurate performance prediction model with fewer samples
from the target environment.
V. R ELATED W ORK
Although performance prediction models are commonlyused to recommend optimal configurations for dynamic workloads [1], [3], [4], [6], few studies, like ResTune [7], leverageknowledge from different hardware environments to build amodel. ResTune employs meta-learning to combine models
from multiple environments. Its main concern is not on theaccuracy itself but on finding optimal configurations for a newworkload, which differs from ChimeraTL’s focus on accuracy.
Several research efforts in transfer learning [11], [12]have aimed to learn models on new hardware environments
efficiently. They showed that software performance in one
environment strongly correlates with that in another. Somework [9], [12] found that impactful parameters are likely
to be shared between the source and target environments.
These results were obtained on configurable systems withbinary parameters, and it was unclear if their approaches wereapplicable to DBMS with non-binary parameters.
In this paper, we found that some parameters in DBMS are
universal and can be transformed linearly, but others are not.
The difference limited the performance of existing transfer
learning techniques that assumed a strong linear relationshipbetween the source and target environments [10], [11].
VI. C ONCLUSION
While some parameters in DBMS have similar effects on
performance across different hardware environments, others
can have varying effects that depend on underlying hardware
limitations. Previous studies do not leverage this information
to improve the effectiveness of transfer learning. We proposed
ChimeraTL, a novel method that accounts for different parameter types to enhance transfer learning in DBMS. We adopted
a novel parameter separation technique which allowed us toemploy different transfer learning methods for each group ofparameters. Our experiments demonstrated that ChimeraTL
needs only 50% of samples that state-of-the-art methods
require to minimize the prediction error to under 10%.

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