faculty of information and communication technology · 2020. 3. 25. · faculty of information and...

Faculty of Information and Communication Technology

ANALYSIS AND MODELING OF DATABASE STORAGE

OPTIMIZATION POWER CONSUMPTION USAGE FOR GREEN

DATA CENTERS

Wan Noor Hamiza Binti Wan Ali

Master of Computer Science in Database Technology

2014

ANALYSIS AND MODELING OF DATABASE STORAGE

OPTIMIZATION POWER CONSUMPTION USAGE FOR GREEN

DATA CENTERS

WAN NOOR HAMIZA BINTI WAN ALI

A thesis submitted in fulfilment of the requirements for the degree of Master of

Computer Science in Database Technology

Faculty of Information and Communication Technology

UNIVERSITI TEKNIKAL MALAYSIA MELAKA

2014

ABSTRACT

Data centers comprised of the building block of Internet Technology (IT) business

organizations holding the capabilities of centralized repository for storage, management,

networking and dissemination of data. The data centers consumed a lot of energy in order

to ensure all the processes in data centers running completely every second which this

situation can be lead to increase carbon footprint and give negative impact to the world

(global warming). One way to reduce the energy consumption is by optimizing space

storage in data centers. Thus, the proxy-based approach is used to optimize the space

storage in CMR database in order to establish green data centers. This technique requires

proxy candidate in order to drop some attribute for decrease the space storage. The proxies

can be discovered by using a developed algorithm of functional dependencies (FDs) called

as TANE algorithm. By using TANE algorithm, the proxy candidate and droppable

attribute for table in CMR database can be discovered before the space saving can be

calculate by given formula. The power saving can be calculated after the amount of space

saving is known. The correlation between amount of space saving and amount of power

saving was visualized in form of graph in the green data center correlation tool. The project

concludes the result from the experiment achieved all the objectives and answered all the

research questions.

i

ABSTRAK

Pusat data terdiri daripada organisasi perniagaan Teknologi Internet (IT) yang menjadi

pusat repositori untuk penyimpanan, pengurusan, rangkaian dan penyebaran data. Pusat

data memerlukan banyak tenaga elektrik untuk memastikan semua proses di pusat-pusat

data berjalan dengan lancar setiap saat yang mana keadaan ini boleh membawa kepada

peningkatan kesan karbon dan memberi kesan negatif kepada dunia (pemanasan global).

Salah satu cara untuk mengurangkan penggunaan tenaga adalah dengan mengoptimumkan

ruang penyimpanan data. Oleh itu, pendekatan berasaskan proksi digunakan untuk

mengoptimumkan ruang penyimpanan dalam pangkalan data CMR disamping

menubuhkan pusat data hijau. Teknik ini memerlukan calon proksi yang terdiri daripada

atribut yang mempunyai kebergantungan berfungsi kepada atribut yang akan digugurkan

sebagai cara untuk mengurangkan ruang dalam pangkalan data. Proksi boleh ditemui

dengan menggunakan algoritma berdasarkan kerbergantungan berfungsi (FD) yang

dikenali sebagai algoritma TANE. Dengan menggunakan algoritma TANE, calon proksi

dan attribut yang akan digugurkan daripada jadual dalam pangkalan data CMR boleh

ditemui sebelum pengiraan penjimatan ruang dibuat berdasarkan kepada formula yang

diberi. Hubungan antara jumlah penjimatan ruang dan jumlah penjimatan kuasa

ditunjukkan dalam bentuk graf di alat korelasi pusat data hijau. Kesimpulannya, projek ini

mencapai semua objektif dan menjawab semua soalan penyelidikan

ii

ACKNOWLEDGEMENT

By the Name of Allah, the Most Gracious and the Most Merciful

First, I would like to express my appreciation to Allah, the Most Merciful, and the Most

Compassionate who has granted ne the ability and willing to start and complete my project.

I do pray to His Greatness to inspire and enable me to continue the work.

My most profound thankfulness goes to my supervisor DR. NURUL AKMAR BINTI

EMRAN for her creativity encouraging guidance and directed me all the way through the

process of developing the project. She is very helpful person and she is my inspiration to

be more successful in the future.

I would like to thank my beloved parents and family who have always been my biggest

inspiration and always being supportive. Your advices are my greatest motivation to keep

on achieving my dreams.

Last but not least, I would like to thanks all my beloved friends and classmates whom

supports me and also helps me in this project. Your co-operation, sacrifice, knowledge

sharing and understanding is really appreciated.

iii

DECLARATION

I declare that this thesis entitle “Analysis and Modeling of Database Storage Optimization

Power Consumption Usage for Green Data Centers” is the result of my own research

except as cited in the references. The thesis has not been accepted for any degree and is not

concurrently submitted in candidate of any other degree.

Signature : ........................................................................

Name : ........................................................................

Date : ........................................................................

iv

APPROVAL

I hereby declare that I have read this dissertation/report and in my opinion this

dissertation/report is sufficient in terms of scope and quality as a partial fulfillment of

Master of Computer Science (Database Technology).

Signature :……………. .....................................................

Supervisor Name :……………………… ......................................

Date :…………………….. ........................................

v

LIST OF TABLES

TABLE TITLE PAGE

2.1 A Proxy Map in Pure Relational Table 21

2.2 A Proxy Map in Multi-Valued Table 21

2.3 Attribute and Schema of Microbial Data Sets 22

2.4 Summary of Three Techniques 30

3.1 Example of Amount Space Saving for each Proxy 45

3.2 Values of PW Based on Amount of Space Saving 46

4.1 Description of Bug Bug_Attribute, Taxon, Feat_Link and

ORF_Attribute

52

4.2 Proxy Candidate and Droppable Attribute for Taxon Table 60

4.3 Candidate and Droppable Attribute for Feat_Link Table 63

4.4 Proxy Candidate and Droppable Attribute for ORF_Attribute Table 66

4.5 Proxy 1 from CMR Taxon Table 70

4.6 Proxy 2 from CMR ORF_Attribute Table 70

4.7 Proxy3 from CMR ORF_Atrribute Table 71



4.10 Proxy 6 from CMR Bug_Attribute Table 74


4.12 Proxy 8 from CMR ORF_Attribute Table 76

4.13 Proxy 9 from CMR Feat_Link Table 76

4.14 Proxy 10 from CMR Feat_Link Table 77

4.15 Power Saving on Proxy 1 78

4.16 Power Consumption on Proxy 2 70



vi







4.25 A Set of Proxy with Details of Droppable Attribute, Proxy Candidate,

Space Saving and Power Saving

87

5.1 Details for G3 Error, Droppable Attribute and Proxy Candidate each

Table in CMR Database

96

5.2 Size of Table Involves in Space Saving Calculation 98

5.3 Relationship Percentage Amount of Space Saving and Percentage

Amount of Power Saving

99

vii

LIST OF FIGURES

FIGURE TITLE PAGE

2.1 Comparison of Data Center Power Consumption between Intel Xeon

Processor and Older Single-Core Processor

9

2.2 Comparison of Monthly Average PUE 11

2.3 A Conceptual Diagram Illustrating on How the Use of Power in Data

Centers Impact an Environment

17

2.4 Proxy Map Size of attribute for Evidence Table 23

2.5 Proxy Map Size of attribute for Taxon Table 23

2.6 Proxy Map Size of attribute for Bug_attribute Table 24

2.7 Energy Calculating Algorithm 26

2.8 Compression Graph 29

2.9 TANE Main Algorithm 33

2.10 Generating Level Algorithm 33

2.11 Computing Dependencies Algorithm 34

2.12 Pruning the Lattice 35

2.13 Computing Partitions Algorithm 36

2.14 Approximate Dependencies Algorithm 36

3.1 Flowchart of Project Implementation 39

3.2 Flowchart of Experimental Based Research Methodology 42

3.3 Command to Analyze Table 44

3.4 Command to Get Size of Table in Bytes 44

3.5 RAD Model 47

3.6 An Expected Graph of Correlation between

Space Saving and Power Saving

48

3.7 Quadrants of Plot Graph 49

4.1 Home Screenshot of Cygwin64 Terminal 54

4.2 Screenshot of Cygwin64 Terminal for command cd d:\TANE 54

viii

4.3 Screenshot of Cygwin64 Terminal for command ls 55

4.4 Screenshot of Cygwin64 Terminal for command cd descriptions 55

4.5 Screenshot of Cygwin64 Terminal for command cd src 55

4.6 Range 0.00 G3 Error for Bug_Attribute Table 57

4.7 Range 0.10 G3 Error for Taxon Table 60

4.8 Range 0.20Figure 4.15: Range 0.20 G3 Error for Feat_Link Table 64

4.9 Range 0.20 G3 Error for ORF_Attribute Table 67

4.10 Screenshot of selectpage.php 88

4.11 Screenshot of a Proxyset Table in phpMyAdmin Database 89

4.12 Screenshot of correlationtool2.php 90

4.13 Screenshot of graph.php 91

4.14 Screenshot of FusionCharts for Dreamweaver (DataGrid) 91

4.15 Screenshot of FusionCharts for Dreamweaver (Chart Selection) 92

4.16 Screenshot of FusionCharts for Dreamweaver (General Option) 93

5.1 Correlation between Power Saving with Space Saving Scattered

Graph

102

5.2 Quadrants of Plot Graph 103

ix

LIST OF ABBREVIATIONS

AHU - Air Handling Unit

CMR - Comprehensive Microbial Resources

CO2 - Carbon Dioxide

CRAC - Computer Room Air Conditioning

CPU - Central Processing Unit

CSV - Comma Separated Values

DBA - Database Administrator

DX - Direct Expansion

FDs - Functional Dependency

GDCE - Green Data Center Effectiveness

GUI - Graphical User Interface

kW - Kilowatts

IT - Internet Technology

PHP - Hypertext Preprocessor

PUE - Power Utilization Effectiveness

RAD - Rapid Application Development

UPS - Uninterruptible Power Supply

XML - Extensible Markup Language

x

TABLE OF CONTENT

PAGE

ABSTRACT i

ABSTRAK ii

ACKNOWLEDGEMENT iii

DECLARATION iv

APPROVAL v

LIST OF TABLES vi

LIST OF FIGURES viii

LIST OF ABBREVIATIONS x

1. INTRODUCTION

1.1 Introduction 1

1.2 Background 1

1.3 Problem Statement 4

1.4 Research Question 4

1.5 Objective 5

1.6 Scope 6

1.7 Significance of Study 6

1.8 Expected Output 6

2. LITERATURE REVIEW

2.1 Introduction 7

2.2 Power Consumption in Green Data Center 7

2.2.1 New Technology of Processor 8

2.2.2 New Cooling System 9

2.2.3 Server Consolidation Technique 12

2.2.4 Calculation of Power Consumption Used by One Server 15

2.2.5 The Impact of Power Consumption in Data Centers on

the Environment

16

2.3 Methods of Space Saving Optimization 18

2.3.1 Proxy-Based Approach 18

xi

2.3.2 Deduplication Technique 24

2.3.3 Data Compression Method 27

2.4 Comparison of Method 30

2.5 TANE Algorithm 32

2.5 Conclusion 37

3. METHODOLOGY

3.1 Introduction 38

3.2 Project Implementation Methodology 38

3.2.1 Experimental Based Research Methodology 41

3.2.2 Development Methodology 46

3.3 Conclusion 49

4. RESULT

4.1 Introduction 51

4.2 Pre-Processing Analysis 51

4.2.1 Installation of TANE Algorithm 53

4.2.2 Proxy Discovery 53

4.3 Space Saving Calculation 69

4.4 Power Consumption Estimation 78

4.5 Correlation Tool 88

4.6 Conclusion 93

5. RESULT ANALYSIS AND DISCUSSIONS

5.1 Introduction 95

5.2 Proxy Candidate Discovery 95

5.3 Space Saving Analysis 98

5.4 Power Consumption Analysis 99

5.5 Graph Analysis 101

5.6 Conclusion 103

6. CONCLUSION 104

REFERENCES 108

APPENDINCES 111

xii

1

CHAPTER 1

INTRODUCTION

1.1 Introduction

In this chapter, the background of green data centers will be presented. Data center

is one of the crucial parts for physical data storage which required efficiency in handling it

in order to avoid high power consumption. We motivate the need for green data centers

based on the problems identified on the traditional data centers before formulating the

objectives of the research.

1.2 Background

Data centers comprised of the building blocks of Internet Technology (IT) business

organizations holding the capabilities of centralized repository for storage, management,

networking and dissemination of data. Major part of a data center is data server where all

data centers plagued with a thousand servers (Uddin and Rahman 2010). These servers

consumed a lot of energy without performing useful work. The operators of data center

must fight to reduce utility cost as expensing of operating component to remain

competitive (Intel, 2011). Based on the finding of the EPA’s report on server and data

centers power consumption, one of the conclusion from this report is the server and data

centers consume about 2.5 percent of all purchased electricity (John, 2013). Thus, green

data centers needed to avoid wasteful power consumption and space storage. Green data

centers are managed and are operated on green computing principles which provide the

2

similar capabilities and characteristics of a typical data centers. However, they are four

distinctive characteristics such as reduce carbon footprint, optimization of space, cost

saving, and more efficiency as compared to a traditional data centers. Efficiency in data

centers can be measured by hardware assets, power, cooling, space and human resources.

First characteristic of green data centers as just mentioned earlier is reducing

carbon footprint. According to Pallamentary Office of Science and Technology (POST

2006), a carbon footprint can be defined as the total amount of Carbon Dioxide (CO2) and

other greenhouse gases which released over the full life cycle of a process that populate a

data center such as servers, UPS, building shell, cooling, etc (Bouley, 2012). The carbon

footprint is not expected to be 100 percent accurate because it is depended on financial and

environment terms. Thus, by establishing green data centers, we can reduce the carbon

footprint. Second characteristic is optimization of space which means the large amount of

data storage not only leads to inconsistent and impractically to maintain the data centers,

however also render to inefficient power usage. By optimizing the required storage to store

large data volumes, the physical data storage may be reduced ( Emran, Abdullah and Isa

2012). Space saving also gives benefits not only when space is highly constrained, but also

when concern is to reduce query response time. By reducing the space to store the data, the

time taken for input/output operation query also can be reduced.

Furthermore, third characteristic of green data centers is cost saving. Even though

an establishment a green data center can be expensive up front, but long-term cost saving

can be realized on operation and maintenance. When fewer data servers will be used, it is

means that fewer data centers space, lower power and cooling cost required. We also can

reduce cost with downsized, generator and battery backup infrastructure proportional to

reduction in peak power requirements. In addition, the fourth characteristic is gain more

efficiency. Efficiency of the data centers can be shown because providers can avoid

3

performing the task of: adding new staff, improving the server, adding new equipment of

data center and so on. Some of specific actions which the providers or industries can take

to support by increasing data center efficiency such as run more sophisticated power

management and define certain points for processor, server and data centers efficiency

based on the current technologies (Emerson, 2009). Efficiency of green data center will be

increase when limits the units of server by doing the optimization of space storage and

power consumption.

Based on four characteristics of green data centers as described earlier, all of them

are related to each other. When space is optimized, automatically cost of maintenance and

operation can be reduced. Then the efficiency of data centers will be increase where leads

to reduce carbon footprint. Consequently, this project proposed modelling of database

storage optimization power consumption usage for green data centers. Our main focus is to

study correlation between space saving and power consumption based on the selected

technique. The selected technique is proxy based approach because this technique requires

reduce physical storage space at schema level compared to dedulication technique and data

compression method that describes in the next chapter.

Comprehensive Microbial Resource (CMR) database has been chosen in order to

use the data sets. The data sets that have been used from four tables in CMR database

which are Bug_Attribute, Feat_Link, ORF_Attribute and Taxon. The data sets from these

four tables undergo the pre-processing steps for discovered a set of proxy by using TANE.

TANE is an algorithm to find out the functional dependency (FDs) which presented by

Huhtala et al (Huhtala et al., 1999). After a set of proxy already discovered, we can

calculate the space saving and power saving before presented the correlation between

power consumption and space saving in form of scattered graph in the green data center

correlation tool.

4

1.3 Problem Statement

The major problem of this project is on identifying the correlation between space

saving and power consumption. It is crucial for data centers administrator to plan or decide

on the amount of space to be saving based on the amount of targeted power saving. In

addition, sensitivity amount of power consumed to the increasing volume of space storage

also one of the problem where when the lower power is consumed, it will give benefits in

terms of cost saving and efficiency of data centers. Another problem is tool that use in

order to determine power consumption which related to amount of space saving. It is

because once the correlation of space saving and power consumption is identified, we

needs tool to see distribution of plotted points on scattered graph which is the graph will be

divide into four quadrants and analyze the best case scenario based on the plotted points.

1.4 Research Question

The following are the research questions that arise from the problem stated in the

problem statement section:

i. How space saving correlates to power consumption?

ii. How sensitive is the amount of power consumed to the amount of increase

in storage space?

iii. Is there a case for a selected storage space optimization method (proxy-

based approach) that contributes to lower power consumption?

5

1.5 Objective

The following are the objectives of this project:

i. To investigate the standard amount of power consumption in data

centers.

In order to understand the power consumption rate in data centers, we need

to explore the standard amount of power consumption rate based on read

scenarios. The result of this step will be used as the based-line for our

correlation process.

ii. To design the model of power consumption for green data centers using

space saving parameter.

The correlation tool will be design and develop based on the standard

amount of power consumption and space saving which has been identified

in order to do correlation.

iii. To simulate the correlation between data center’s power consumption

and space saving.

By using storage space saving rate and power consumption variables,

correlation between these two variables will be graphically presented in

form of a data center correlation tool.

6

1.6 Scope

This project covers the study of database storage optimization’s efficiency in

relation to the power consumption variable in order to establish a green data center for

microbial domain from the J. Craig Venter Institute. We use the data sets from CMR which

includes four tables in order to find out a set of proxy. The tables that will be used are

Bug_Attribute, Feat_Link, ORF_Attribute and Taxon. Even though there are 25 tables in

the CMR database, but not all tables can be used because some of the data sets cannot be

download from the internet due to the corrupted data sets. Space storage and power

consumption can be correlates after the space saving and power saving are calculated.

1.7 Significance of Study

The investigation on the correlation of power consumption and space storage

contributes to better understanding on the behaviour of proxy-based approach. The result

from this project can be applied by database administrators (DBAs) from J. Craig Venter

Institute in order to minimize the space storage and power consumption in the data center.

Technical issues during the development and implementation of the correlation tool

provide guidance for future improvement.

1.8 Expected Output

Expected output from this project is a software green data center correlation tool

that can be used to correlate the amount of power saving for a given space saving target for

DBAs.

7

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

This chapter over review of literature on power consumption and techniques of

space saving for green data centers. By understanding the requirements of green data

centers from power consumption and space saving point of view, the model of correlation

between spaces saving and power consumption can be built.

2.2 Power Consumption in Green Data Center

A data center is a facility that comprised of the building blocks of IT business

organizations holding the capabilities to supply source energy for IT equipments used. The

outlines of common data center energy consumption can be divided into three areas such as

server load and computing operation, cooling equipment and power conversion and

distributions (Energy, 2012). There is an increasing concern about the cost and availability

of energy (John, 2013) due to many researchers study how to optimize power consumption

in many domains such as IT (eg., data center), engineering (e.g., digital signal processor)

and so on because by reducing the power consumption in data center, the emission of CO2

can be reduce and operating cost can be saved (eg., cooling system for data centers).

Power consumption in green a data center can be defined as amount of energy

consumed by running the data centers in order to support process or system (e.g., cooling

8

system). Power consumption used for data centers is about half from the electricity for an

organization and percentages of power consumption usage is increase year by year as

mentioned by DataCenterDynamic (DCD Intelligence, 2014). The increasing of usage is

about 6.8% from 2012 to 2013. Hence, we need to overcome this problem before its

getting worst because rising of power consumption will let to high carbon footprint.

Consequently, there are a few approaches on how to optimize power consumption in data

centers. In this report, we will elaborate about three approaches for optimizing power

consumption which is by used the latest technology of processor, develop new cooling

system and by applying server consolidation technique. Later on, we also will be

explaining regarding to energy consumes by one server and the impact of power

consumption in data centers on the environment.

2.2.1 New Technology of Processor

Based on white paper from Intel (Intel, 2013), they have explained the

strategies developed and used by CERNopenlab. CERN is the largest physical

laboratory in worlds which function to provide the accelerators and infrastructure

for high-energy physic research. Comparison between power consumption of Intel

Xeon processors and older single-core processor has been made by CERN. The

latest Intel Xeon processor is based on the Intel Core micro architecture which can

support about five times more compute power per Watt than older single-core

processor which based on the earlier Intel NetBurst micro architecture. It is because

the latest Intel Xeon processor has four cores per processors compared to older

single-core processor that have only single core per processors.

9

Figure 2.1 Comparison of Data Center Power Consumption between Intel Xeon Processor

and Older Single-Core Processor (Intel, 2013)

A shown in Figure 2.1 is a graph of comparison of power consumption

between Intel Xeon processors and older single-core processor has been produced

by CERN. CERN was analyzing the data centers usage in MegaWatts power

consumption from 2008 until 2012 to see the power usage differences between

these years. CERN concluded that moving to the latest Intel Xeon processors is the

best solution because total power consumption can be reduced about five times

rather than the older single-core processors.

2.2.2 New Cooling System

By analyzing another research that was conducted by Green Data Center

Project from National Snow and Ice Data Center (NSIDC), power consumption can

be reduced by optimizing the cooling data center functions. The traditional data

center cooling infrastructure used standard air conditioning system that used direct

expansion (DX). DX system also can be referred as computer room air conditioner

10

(CRAC). DX is function to remove heat via a liquid refrigerant as running in a car

or refrigerator. Since DX used synthetic refrigerant which harmful the environment

and compressor in DX requires lot of energy. Even though this system is quite

economical but it was become antiquated. Thus, Green Data Center Project was

finding another solution to overcome this problem.

According to Weerts et al (Weerts et al., 2012), an approach that has been

implemented is by applying new cooling system design that consists of a unique

cooling system that uses both airside economization and a new air conditioner

(indirect evaporative cooling). Airside economizer control mode lets the air

handling unit (AHU) to cool the space with outdoor air when the outdoor air is

cooler than the air space. However, in several locations especially the Midwest and

East Coast of the United State, the airside economization may not work well cause

by hot and humid environment. On the other hand, indirect evaporative cooling

used the Maisotsenko Cycle which operates direct and indirect evaporative cooling

in order to produce a supply air state (16.7 °C to 22.2 °C).

In order to compare the differences between CRAC system and new cooling

system, the data center efficiency should be calculated. The Power Utilization

Effectiveness (PUE) for any data center can be calculated by using (1).

Power) Power)/(IT (Total =PUE (1)

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