FLEXIBLE CONTROL OF PERFORMANCE AND EXPENSES FOR

DATABASE APPLICATIONS IN A CLOUD ENVIRONMENT

Shoubin Kong, Yuanping Li

Department of CS&T, Tsinghua University, Beijing 100084, China

Ling Feng

Department of CS&T, Tsinghua University, Beijing 100084, China

Keywords: Cloud computing, Infrastructure-As-A-Service (IaaS), Virtual machine (VM), Resource configuration,

Performance, Expenses, Multiple objective optimization.

Abstract: IaaS is a popular cloud computing service paradigm based on virtualization technology. In an IaaS cloud

environment, the service provider configures VMs with physical computing resources (e.g., CPU and

memory) and leases them to IaaS customers to run their applications. The customers pay for the resources

they use. Such a pay-as-you-go charging mode brings about a few critical concerns about the expenses paid

and the performance received. From the standpoint of cloud customers, such concerns as minimizing the

expenses while ensuring the performance, optimizing the performance within the budget limit,

compromising the expenses and performance, or balancing performance of applications running on different

VMs, etc. thus arise. For the IaaS provider, how to reasonably configure VMs so as to meet various

requirements from different customers becomes a challenge, whose solution influences the acceptance of

IaaS in the future. In this paper, we address this problem and present a weighted multiple objective

optimization approach for flexible control of expenses and performance in an IaaS cloud environment. We

focus on database applications, consisting of various queries to be executed on different VMs. A genetic

algorithm is implemented based on a fine-grained charging model, as well as a normalized performance

model. Experiments have been conducted to evaluate the effectiveness and efficiency of our approach, using

TPC-H queries and PostgreSQL database in a simulated cloud environment.

1 INTRODUCTION

IaaS is an important cloud computing service

paradigm provided by a few well-known IT

companies such as Amazon, IBM, etc. IaaS depends

largely on virtualization technology, enforcing

simple and flexible management of computing

resources. As a result, customers can get desirable

resources as needed, and the IaaS provider charges

the customers for the resources they use. This is

coined as “pay-as-you-go”. Under such a charging

mode, from the standpoint of IaaS customers, a few

critical concerns about expenses paid for the service

and obtained performance of their applications thus

inevitably arise.

Let’s consider such a scenario. An IaaS customer

wants to run several database applications on a few

VMs in an IaaS cloud environment. How to

configure these VMs with reasonable physical

computing resources is the first issue that the IaaS

provider needs to solve. At the same time, the

customer may also have a number of doubts and

questions about the expenses to be paid for the

resources, as well as the obtained applications’

performance.

1) Is it possible to achieve the best performance

within a budget limit?

2) Is it possible to minimize the expenses while

still guaranteeing the performance?

3) Is it possible to make a compromise between

expenses and performance?

4) Is it possible to achieve a balanced

performance when running different applications on

different VMs under the premise of guaranteeing the

overall performance?

From the perspective of the IaaS provider,

201

Kong S., Li Y. and Feng L..

FLEXIBLE CONTROL OF PERFORMANCE AND EXPENSES FOR DATABASE APPLICATIONS IN A CLOUD ENVIRONMENT.

DOI: 10.5220/0003379802010210

In Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER-2011), pages 201-210

ISBN: 978-989-8425-52-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

proper answers to the above-mentioned questions

apparently determine the acceptance of IaaS by its

potential cloud customers.

The aim of this paper is to provide a solution to

address the above-mentioned questions. Based on a

fine-grained charging model with respect to the

usage of CPU and memory, along with a normalized

performance model, we formalize the above-

mentioned problems into a multiple objective

optimization problem, and solve it by means of a

genetic algorithm.

Considering that a customer may have a variety

of applications or queries from the same application

running on different VMs, a uniform and reasonable

performance model is a challenge here. For example,

an airline ticket database application supports not

only OLTP queries like querying flight schedules,

but also OLAP queries like mining association rules

from the data. In this case, 1 minute may be too long

for OLTP queries, but 1 hour is still acceptable for

OLAP queries. Therefore, using the absolute

execution time of queries to measure the

performance of different types of queries isn’t

reasonable, and may lead to skewed resource

configuration. In this paper, we tackle this issue

through normalization of execution time.

To evaluate the effectiveness and efficiency of

our approach, we conduct some experiments by

running a few typical TPC-H queries in a simulated

IaaS cloud environment. The results are as follows:

1) The performance model with execution time

normalization can effectively avoid skewed

resources configuration.

2) The optimized resources configuration

strategy given by our approach can get 20% even up

to 30% performance improvement over the default

configuration.

3) The multiple objective optimization method

can effectively save the expenses when the increase

of computing resources contributes to little

improvement of performance. It can also help

balance the performance of applications running on

different VMs with little drop of overall

performance.

4) Benefiting from our performance model for

individual queries, our approach is quite adaptable to

resource requirements of different applications.

The contribution of the paper primarily lies in

two aspects. 1) A weighted multiple objective

optimization approach is proposed for flexible

control of expenses and performance in an IaaS

cloud environment, with an aim to meet a variety of

requirements coming from different cloud

customers. 2) A normalized performance model is

built, taking different kinds of applications from a

customer into account.

The remainder of the paper is organized as

follows. In Section 2, we review some closely

related work and highlight the differences of our

work from existing ones. In Section 3, we formalize

the problem and present our solution. We describe

our performance study in Section 4, and conclude

the paper in Section 5.

2 RELATED WORK

Virtualization technology has received lots of

attention in both industry and academia. IaaS is a

typical application of virtualization in industry. In

recent years, issues on resource and performance

management and resource charging in virtualized

environments become hot research topics in

academia.

Performance Management. Performance is a

critical concern in virtualization for cloud customers.

To deliver satisfactory application performance,

tremendous efforts have been made, including

performance management and application behavior

analysis (Xiong et al., 2010), application

performance isolation in a virtualized environment

(Somani and Chaudhary, 2009), power and

performance management in virtualized computing

environments via lookahead control (Kusic et al.,

2009), automated control of multiple virtualized

resources (Padala et al., 2009), proper resource

configuration for virtual machines (Rao et al., 2009,

Bu et al., 2010), balancing power and application

performance for virtualized server clusters (Wang

and Wang, 2009), control of resource allocation and

power management in virtualized data centers

(Urgaonkar et al., 2010). However, none of these

work incorporated the cost or expenses for using

resources in cloud environments, which is an

important focus of this paper.

There has also been some work on improving the

performance of database applications in a virtualized

environment through different methods, including

on-demand provisioning of virtual machines

(Shivam et al., 2007), and resource configuration of

virtual machines (Soror et al., 2008). We also

consider resource configuration in this study.

However, we are oriented at a scalable cloud

environment, while the work of Soror et al. (2008) is

based on a fixed amount of physical resources

shared by multiple VMs and their assumption that

execution time of queries is linear in the inverse of

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resource allocation level is limited to small

adjustments in resource configuration.

Resource Charging. The resource charging of IaaS,

which is another important aspect the cloud

customers care about, has become focus of some

researchers in the last two years.

Florescu and Kossmann (2009) emphasized the

necessity of minimizing cost given performance

requirements, and Henzinger et al. (2010) proposed

a fine-grained charging model in a simulated cloud

environment. The charging model generally refers to

how IaaS providers charge for their services.

Usually IaaS providers set the leasing price for each

kind of computing resource, and customers pay for

the resources configured for their VMs. The current

charging model of IaaS is that providers offer and

charge for fixed configured VMs. For example,

Amazon EC2 offers 10 different kinds of resource

configurations for VMs. If a customer’s application

running on one VM, s/he needs to pay the amount

corresponding to that VM of a fixed resource

configuration. Rogers et al. (2010) further presented

a framework for minimizing the operational cost on

Amazon EC2 within target QoS expectations. But

this work assumed that each VM runs a single type

of queries, and took operational cost as the sole

optimization objective.

Our work differs from the above-mentioned

charging models in the following three aspects. First,

we adopt a pay-as-you-go charging strategy, wherein

customers can ask for a flexible (rather than fixed)

amount of resources (i.e., CPU and memory) for

personalized configuration as needed. Second, we

consider a more complex yet realistic query

workload to be executed by a VM, considering that

one VM may be assigned different types of queries

in some real-world applications. Third, besides

optimizing application performance and expenses,

we also try to balance performance across different

VMs, since one customer may have more than one

VM to run their business. To this end, we set up a

multiple objective optimization model, aiming to

optimize performance, minimize expenses, and

balance application performance across different

VMs.

3 MODELING AND SOLUTION

In this section, we present our approach to formalize

and solve the problems mentioned above. First, we

obtain a performance model for individual queries

by fitting sample data. Then we build a uniform

performance model for workloads through

normalization of execution time. After that, the

problem of performance and expenses control is

turned into a weighted multiple objective

optimization problem. At last, we present some

details of the genetic algorithm implemented to

solve the problem.

3.1 Preliminaries

Suppose an IaaS customer has N query workloads to

be running on the same number of VMs. A workload

on a VM is composed of one or more (i.e. mixed)

types of queries. In another word, one workload

corresponds to one special VM, and may contain

various types of queries. For each workload, assume

the percentage of each type of queries is given,

which can be obtained in practice by sampling and

statistic.

Let W

denote the workload running on the

VM (

1 iN

≤

). To model mixed queries in a

workload, let Q

be the

th type of queries in W

and let p

denote the percentage of Q

in W

(

≤

); let n

be the number of query types in

. To express the resource configuration of VMs,

let c

be the CPU capacity of the

th VM, e.g. 1GHz,

and let m

be the memory size of the

th VM, e.g.

1GB.

3.2 Performance Model for Individual

Queries

To establish relationship between expenses and

performance, we need a mapping function from

computing resources (CPU, memory) to the

execution time of queries, i.e. performance model

for queries. We can obtain the mapping function by

fitting some sample data. And there are two sources

of the sample data; one is the estimates of DBMS

query optimizer (Soror et al., 2008), the other is the

real experiment data. In order to ensure the accuracy

of the data, we have adopted the real experiment

data obtained by running TPC-H queries on VMs

with different resources configuration. By

leveraging nonlinear surface fitting functions of the

numeric analysis software Origin 8.0 over the

sample data, we found the Rational2D function

fitted the data well, with the R

value reaching 0.99.

is an indicator of the goodness of fit, and a R

value close to 1 indicates that the fit is a good one.

The fitting algorithm in Rational2D function is

based on LMA (Levenberg-Marquardt Algorithm

(More, 1978)), a robust iterative algorithm effective

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203

to nonlinear fitting problems. The standard form of

the Rational2D function is as follows:

01 2 3 4

23 2

56 7 89

ij ij i ij i ij i ij i

ij i ij i ij i ij i ij i

AAxAyAyAy

xA x A x A yA y

+×+×+×+×

+×+×+×+×+×

(1)

where

is the execution time of

;

wherein c

is the CPU capacity of the

th VM;

ym=

, wherein m

is the memory size of the

VM;

ijk

(

09k≤≤

) is the coefficient obtained by

fitting the sample data.

Figure 1: The fitted result for Q13.

Figure 2: The fitted result for Q21.

Figure 1 and 2 show the fitted results for two

typical types of query, Q13 and Q21, which are

respectively a CPU-intensive and a memory-

intensive query from TPC-H benchmark. The

execution time of CPU-intensive queries is mainly

influenced by CPU capacity, while that of memory-

intensive queries is mainly influenced by memory

size.

3.3 Performance Model for Workloads

After obtaining the mapping function, we need an

expression to formalize the performance of each

workload. At first, we consider using the expected

execution time of queries in a workload, which is

iijij

Tpt

∑

(2)

where T

is the expected execution time of queries in

, which is taken as the execution time of W

However, sometimes this may lead to unreasonable

resources configuration. For example, the workload

on a VM instance contains two different types of

queries, one is CPU intensive and the other is

memory intensive; the order of magnitude of the

execution time of the former is 1ms, and that of the

later is 1000ms. Besides, the CPU-intensive query

accounts for 90% of the workload, and the memory-

intensive one 10%. Obviously, the former plays a

dominant role in this workload, so the right way to

improve the performance of the workload should be

giving it more CPU capacity. In order to satisfy most

query requests, we hope to pay 90% of our

optimization effort on the CPU-intensive query.

However, if using formula (2), the result of 90%

multiplying by 1, 0.9 is much less than 100, that of

10% multiplying by 1000. In this case, the memory-

intensive query will be mistaken for the dominator

of the workload, which will lead to incorrect

resources configuration. That is to say, difference of

the order of magnitude of the execution time may

lead to undesirable and even wrong configuration

strategies. So it’s necessary to normalize the

execution time of different types of queries.

Formula (3) shows our normalization method,

multiplying the execution time by a normalization

factor

. Formula (4) is the expression of the

normalization factor; the denominator is the average

execution time of the standard type of queries

selected beforehand, and the numerator is that of

other queries. The average execution time of each

type of queries can be obtained from sample data,

and the ratio is taken as the normalization factor to

eliminate the undesirable influence of different order

of magnitude of the execution time. In following

sections, unless otherwise specified, any execution

time is the normalization time with average

execution time ratio.

ij ij ij

′

(3)

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ij s ij

(4)

′

is the normalization result of

(

jn≤≤

);

the normalization factor of

(

jn≤≤

);

t is the

average execution time of the standard type of

queries selected beforehand;

is the average

execution time of

. Then the normalization result

is formed as:

iijijij

Tp t

=××

∑

(5)

3.4 Multiple Objective Optimization

Based on the information above, three objective

functions are formed as:

:min

cpu i mem i

obj price c price m

×+ ×

∑

(6)

111

:min min

iijijij

iij

obj T p t

===

=××

∑∑∑

(7)

:min{max{ }}

min{max{ }}

ij ij ij

obj T

××

∑

(8)

where price

cpu

is the leasing price of CPU, e.g.

0.02$/GHz·hour; price

mem

is the leasing price of

memory, e.g. 0.01$/GB·hour. Formula (6) aims at

minimizing the total expenses per leasing interval.

Formula (7) aims at minimizing the total execution

time of all the workloads, i.e., optimizing overall

performance. And formula (8) minimizes the

maximal execution time of different workloads,

aiming at balancing the performance of different

workloads. And the constraints of the problem are as

follows:

∑

(9)

ij q

t bound≤

(10)

cpu i mem i

price c price m budget

×+ × ≤

∑

(11)

Formula (9) ensures the percentage of each type

of queries in a workload adds up to 1. Formula (10)

ensures the execution time of each type of queries

against exceeding their respective bound, and

formula (11) ensures the total expenses against

exceeding the budget limit.

At last, we transform the multiple objective

optimization problem to a single objective

optimization problem using linear weighting method.

The new objective function is

:min

obj w obj

∑

(12)

where w

13i

≤

, are respectively the weights

of three original objective functions.

3.5 Algorithm

Then we design and implement a genetic algorithm

by C++ language to solve the problem. Primary

algorithm codes are as follows.

Input:

the number of generations: G

the size of population: P

the number of query types: Q

the number of workloads (VMs): N

the percentage of queries in each workload: p

the performance model for queries: Rational2D

the normalization factor of execution time: λ

the weight of each objective function: w

Output:

the resource configuration of each VM: c

, m

Function 1: Main function

Begin

Initialization;

foreach k from 1 to G do

Selection;

Crossover;

Mutation;

Evaluation;

End

Function 2 is used to initializing the population,

and the chromosomes consist of c

and m

randomly

generated.

Function 2: Initialization

Begin

foreach k from 1 to P do

foreach i from 1 to N do

Chrom

=random CPU capacity;

foreach i from N+1 to 2N do

Chrom

=random memory size;

Check constraints;

foreach i from 1 to 2N do

Chrom

=Chrom

;

ObjFunctions;

foreach j from 1 to 3 do

Obj

=Obj

;

End

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Function 3 is used to compute the value of objective

functions so as to evaluate the fitness of

chromosomes.

Function 3: ObjFunctions

Begin

foreach k from 1 to P do

CPU=0; Memory=0;

foreach i from 1 to N do

=1/Chrom

;CPU+=Chrom

;

=1/Chrom

k(i+N)

;Memory+=Chrom

k(i+N)

;

Obj

=price

cpu

*CPU +price

mem

*Memory;

Obj

=0; Obj

=0;

foreach i from 1 to N do

=0;

foreach j from 1 to Q do

if(p

!=0)

=Rational2D

);

*λ

;

Obj

+=T

;

if(T

>Obj

) Obj

;

Obj

*Obj

;

End

Function 4 aims at evaluating the fitness of

chromosomes after crossover and mutation.

Function 4: Evaluation

Begin

ObjFunctions;

foreach k from 1 to P do

n=0; obj

min

=Obj

;

foreach j from k+1 to P do

if(obj

min

>Obj

)

obj

min

=Obj

;

n=j;

if(n!=0)

foreach i from 1 to 2N do

swap Chrom

and Chrom

;

foreach j from 1 to 3 do

swap Obj

and Obj

;

End

Since Selection, Crossover and Mutation are

familiar to traditional genetic algorithm, the codes of

them are not given here. The time complexity of the

algorithm is O(GPNQ). And the results of our

experiments show when P is set to 30 (an empirical

value for genetic algorithm), the algorithm can give

near-optimal configuration strategy with G reaching

two or three orders of magnitude; and when N and Q

are respectively less than 10 and 5, it takes less than

1 second to perform the algorithm.

4 EVALUATION EXPERIMENTS

In this section, we show some experimental results

obtained by running a few typical TPC-H queries in

a simulated IaaS cloud environment. These

experiments aim at evaluating the effectiveness of

the normalized performance model, the performance

improvement of the optimized configuration

strategies given by our approach, the compromise

between performance and expenses, the tradeoff of

workloads on different VMs, and the adaptability to

resource requirements of workloads.

4.1 Experimental Settings

Experimental Environment. We use two identical

computers to construct a simulated cloud

environment, each with one 2.5GHz Intel Xeon

Quad-Core processor and 5GB of memory.

XenServer is used for virtualization. XenServer is a

product based on Xen, a powerful open source

industry standard for virtualization. Amazon EC2 is

exactly based on Xen virtualization solution.

CentOS-5.4-x86_64 is used as the operation system

of VMs.

Charging Model. The absolute leasing prices of

CPU and memory won’t influence the evaluation,

but the ratio between them should be reasonable. In

our experiments, we determine their leasing price

with the ratio corresponding to their actual market

price ratio. We have investigated the market prices

of CPU and memory, and the price ratio is

approximately 2:1. In the following experiments, the

leasing prices of CPU and memory are respectively

set to 0.02$/GHz·hour and 0.01$/GB·hour.

Basic Metric for Performance Improvement.

Generally, execution time can reflect the

performance directly. Therefore, we use the

execution time of a workload to measure its

performance, and the total execution time of all the

workloads to measure the overall performance. Then

we define a default resources configuration strategy,

that is, to average the given budget for all VMs, and

for each instance, the memory size is 1.872 times as

CPU capacity. We obtain this default strategy from

the configuration of the standard VM instances from

Amazon EC2. Table 1 shows the CPU and memory

configuration of three standard VM instances from

Amazon EC2. We run a linear regression on the data

to get the default configuration ratio between CPU

and memory. Figure 3 shows the perfect regression

result with the R

value reaching 0.999. Then

formula (13) shows the basic evaluation equation.

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Table 2: The configuration strategies with and without execution time normalization.

Budget($) Workload

Strategy with execution time

normalization

Strategy without execution time normalization

CPU (GHz) Memory (GB) CPU (GHz) Memory (GB)

0.04

0.7 0.5 0.6 1.3

0.8 0.5 0.5 0.5

0.06

1.2 0.6 1.1 1.9

1.2 0.5 0.7 0.5

0.08

1.6 1.0 1.5 2.1

1.6 0.5 1.1 0.5

default

and T

optimal

are respectively the execution time

under default and optimized configuration.

default optimal

default

improvement

−

(13)

Table 1: The CPU and memory configuration of three

standard VM instances from Amazon EC2.

Type of VM instances CPU (GHz) Memory (GB)

Small 1 1.7

Large 4 7.5

Extra large 8 15

Figure 3: The regression result for the CPU and memory

configuration of VM instances from Amazon EC2.

4.2 Verifying the Necessity and

Effectiveness of Execution Time

Normalization

Since the difference of the order of magnitude of the

execution time may lead to undesirable and even

wrong configuration strategy, we have brought in

the normalization factor to terminate this undesirable

influence. To verify the necessity and effectiveness

of normalization factor, we select three types of

TPC-H queries, Q11, Q21 and Q6 to conduct this

experiment. Q11 and Q21 are respectively CPU

intensive and memory intensive, and Q6 isn’t very

sensitive to CPU or memory. Besides, Q11 and Q6

have the same order of magnitude, and Q21 has

nearly two orders higher than them.

We start two VMs with two different workloads

respectively running on them. W

consists of Q11

and Q21 with 9:1 quantity ratio, and W

is composed

of Q11 and Q6 with 9:1 quantity ratio. So both

workloads are CPU intensive because Q11 plays a

dominant role in them, and the correct configuration

strategy is to give more CPU capacity to both VMs.

Table 2 shows the configuration strategies given

by two approaches with and without execution time

normalization under different budget constraints.

From the table, it can be seen that the strategy with

time normalization is correct, giving priority to CPU

configuration for both VMs. In contrast, the strategy

without time normalization, giving priority to

memory configuration for W

, doesn’t correspond to

the actual demand.

4.3 Evaluating the Basic Performance

of Our Approach

In this section, we focus on evaluating the

effectiveness of our approach to combination of

workloads with different natures. We use three

typical combinations of workloads to test the

improvement of overall performance.

Case1: The experiment in the previous section is a

typical case, for both workloads are CPU intensive.

Figure 4 shows the total execution time respectively

under default configuration strategy mentioned

above and optimized configuration strategy given by

our approach. It can be seen that the improvement is

significant, reaching 20% even up to 30%.

Case2: Then we design another typical case using

Q6, Q21 and Q13. Q6 and Q21 have been

introduced in the previous section, and Q13 is

almost an absolutely CPU-intensive query,

extremely insensitive to memory. Similar to the

previous experiment, we run two different

workloads on two VMs respectively. W

consists of

Q6 and Q21 with 1:9 quantity ratio, and W

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Figure 4: Optimization result for Case1.

composed of Q6 and Q13 with 1:9 quantity ratio. So

is typically memory intensive while W

typically CPU intensive. Figure 5 shows the result of

this experiment. It can be seen that the improvement

is satisfying, over 20% even exceeding 30%.

Figure 5: Optimization result for Case2.

Figure 6: Optimization result for Case3 with different

percentage of Q6 in

Case3: Next, let’s look at the third typical case. We

just make some changes based on Case2, turning the

quantity ratio into 9:1 for W

, and : (10 )nn− for W

from

. As a result, W

isn’t very

sensitive to CPU or memory because Q6 becomes

the dominator, while W

’s sensitivity to CPU

decreases as

increases. Figure 6 shows the result

of this experiment. It can be seen that the

improvement drops significantly as the percentage

of Q6 in W

increases.

From these experiments, it can be concluded that

our approach can lead to significant performance

improvement when the combination of workloads is

sensitive to resources, or else the improvement isn’t

very apparent.

4.4 Verifying the Effectiveness of

Compromising Performance and

Expenses

Formula (12) in section 3 shows us the aggregating

objective function using linear weighting method,

and w

(

13i

≤

), the weights of three original

objective functions, can be adjusted as needed. If the

customers only care about one aspect of them, they

just need to set the weights of other objective

functions to zero. In this experiment, we focus on

the expenses and the overall performance under

different budget constraints.

We start three VMs with three different

workloads respectively running on them. To verify

the applicability of our approach, all the workloads

consist of Q6, Q21 and Q13, three different types of

queries (c.f. section 5.2, 5.3) with random instead of

controlled quantity ratio. For comparison, firstly w

and w

in formula (12) are set to 0, and w

is set to 1.

In this case, the only objective is minimizing the

total execution time of the workloads, i.e. optimizing

the overall performance.

Figure 7: Execution time and expenses when ignoring

cost-performance ratio.

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Figure 7 shows the expenses and total execution

time under different budget constraints, ignoring

cost-performance ratio. It can be seen that, when the

budget increases beyond a certain value, the total

execution time decreases very slowly, but the

expenses increase as usual. This is because when

resources reach a certain level, the performance

nearly reaches saturation point. Under this

circumstance, single objective optimization can’t

give a fine configuration strategy. It’s necessary to

adjust the ratio between the weights of objective

functions to obtain more reasonable strategies.

Figure 8 shows some new results by adjusting w

and w

, and the tradeoff between performance and

expenses is significant. Especially when the budget

is relatively higher, our approach can save the

expenses significantly, with little drop of

performance.

Figure 8: Execution time and expenses when considering

cost-performance ratio. Time1-3 are three execution time

curves under different ratios between

and w

, and

Expenses1-3 are corresponding expenses curves.

4.5 Verifying the Effectiveness of

Balancing the Performance of

Different Workloads

In the previous experiment, the tradeoff between

performance and expenses can be achieved by

adjusting the ratio between w

and w

. Analogously,

if the customers hope to balance the performance of

different workloads, they just need to increase the

value of w

, i.e. the weight of the third objective

function.

We start two VMs with two different workloads

respectively running on them. W

consists of Q21

and Q13 with 1:9 quantity ratio, while W

composed of Q21 and Q13 with 9:1 quantity ratio.

So W

is typically CPU intensive while W

typically memory intensive.

Table 3 shows the experimental results with

different ratios between w

and w

. From the table it

can be seen that, as the value of w

increases, the

performance tradeoff between the two workloads

become more significant. At the same time, the

impact on the overall performance is limited,

because the increase of total execution time is not

apparent.

Table 3: Performance tradeoff of workloads under

different ratios between

and w

Execution time with normalization

Total

0:1 60.05 29.55 89.60

1:1 53.12 39.36 92.48

9:1 49.30 49.29 98.59

4.6 Verifying the Adaptability to

Resource Requirements of

Workloads

The purpose of this experiment is to verify the

adaptability of our approach to resource

requirements of workloads. Q13 and Q21 are

selected to design this experiment, which are

respectively CPU intensive and memory intensive.

We run two workloads on two VMs. As the control

group, W

is composed of Q13 and Q21 with fixed

quantity ratio, 5:5. W

is composed of Q13 and Q21

with varying quantity ratio

:(10 )nn

−

, from

. Therefore, as n increases from 1 to 9, W

changes from a memory-intensive workload to a

CPU-intensive workload, and the correct

configuration strategy is to give it more CPU and

less memory.

Figure 9: CPU and memory configuration strategies for W

corresponding to different percentage of Q13 in

CPU1-3 are three CPU configuration curves under low,

middle and high budget constraints, and Memory1-3 are

corresponding Memory configuration curves.

FLEXIBLE CONTROL OF PERFORMANCE AND EXPENSES FOR DATABASE APPLICATIONS IN A CLOUD

ENVIRONMENT

209

Figure 9 shows the configuration strategies for

given by our approach under low, middle and

high budget constraints. From this figure we can see

that our approach is adaptive enough to resource

requirements of workloads.

5 CONCLUSIONS

In this paper, we propose an approach for flexible

control of performance and expenses in IaaS cloud

environments with different requirements of

customers. We focus on the workloads with mixed

types of queries in database applications. Based on a

fine-grained charging model and a normalized

performance model, we build a model of multiple

objective optimization, which covers different

aspects cloud customers care about, such as

expenses, performance, the compromise between

performance and expenses, the performance tradeoff

of applications on different VMs, etc. Under this

model, these complicated problems are turned into

an optimization problem, which can be addressed by

a genetic algorithm we have implemented. And from

the results of some experiments, it can be seen that

the effectiveness of our approach is significant.

There is also some work to do in the future, such

as building a more comprehensive charging model

considering I/O performance and network bandwidth,

and exploring more delicate performance model

considering database concurrency control.

ACKNOWLEDGEMENTS

The work is funded by National Natural Science

Foundation of China (61073004) and Chinese Major

State Basic Research Development 973 Program

(2011CB302200).

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