optimizing and enhancing parallel multi … and enhancing parallel multi storage backup compression...

OPTIMIZING AND ENHANCING PARALLEL MULTI STORAGE

BACKUP COMPRESSION FOR REAL-TIME DATABASE SYSTEMS

1. M. Muthukumar 2. Dr.T.Ravichandran

Research Scholar, Principal,

Karpagam University, Hindusthan Institute of Technology,

Coimbatore, Coimbatore,

Tamilnadu, India. Tamilnadu, India.

[email protected]

ABSTRACT

One of the big challenges in the world was the

amount of data being stored, especially in

Data Warehouses. Data stored in databases

keep growing as a result of businesses

requirements for more information. A big

portion of the cost of keeping large amounts of

data is in the cost of disk systems, and the

resources utilized in managing the data.

Backup Compression field in the database

systems has tremendously revolutionized in

the past few decades.

Most existing work presented attribute-level

compression methods but the main

contribution of such work had been on the

numerical attributes compression alone, in

which it employed to reduce the length of

integers, dates and floating point numbers.

Nevertheless, the existing work has not

evolved the process of compressing string-

valued attributes. To improve the existing

work issues, in this work, we study how to

construct compressed database systems that

provides better in performance than

traditional techniques and to store the

database backups to multiple storages.

The compression of database systems for real

time environment is developed with our

proposed Hierarchical Iterative Row-

Attribute Compression (HIRAC) algorithm,

provides good compression, while allowing

access even at attribute level. HIRAC

algorithm repeatedly increases the

compression ratio at each scan of the

database systems. The quantity of

compression can be computed based on the

number of iterations on the rows. After

compressing the real time database systems, it

allows database backups to be stored at

multiple devices in parallel. Extensive

experiments were conducted to estimate the

performance of the proposed parallel multi-

storage backup compression (PMBC) using

HIRAC algorithm with respect to previously

known tehniques, such as attribute-level

compression methods.

Keywords: Backup Compression, Parallel

Multistorage, Large Database,

Compression Techniques

1. INTRODUCTION

Database compression has been a very

familiar topic in the research, and there has

been a requirement of amount of work on this

subject. Data available in a database are

highly valuable. Commercially available real

time database systems have not heavily

utilized compression techniques on data stored

in relational tables. One reason is that the

trade-off between time and space for

compression is not always attractive for real

time databases. A typical compression

technique may offer space savings, but only at

a cost of much increased query time against

the data. Furthermore, many of the standard

M Muthukumar et al ,Int.J.Computer Technology & Applications,Vol 3 (4), 1406-1417

IJCTA | July-August 2012 Available [email protected]

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mailto:[email protected]

techniques do not even guarantee that data

size does not increase after compression.

Global domains like banking, insurance,

finance, railway, and many other government

sectors will have a million and billions of

transaction on day-by-day. All these

transactions should be recorded on the

database for future reference. Such a domain

database will be grown GBs and TBs of data

during daily activities. Global domain

provider used to take the backup of their

database multiple times in a day also once in

few hours. This daily activities will be

consuming considerable time on every day.

This research will provides a solution to

compress the very large scale databases

(VLSDB‟s) more effectively, reduce the

storage requirements, costs and increase the

speed of backup. VLSDB‟s has a

distinguished role because it would otherwise

be very difficult to compress or store these

databases on the available disk. The cost of

storage, retrieval, and transmission of data

within a VLSDB‟s database system is greatly

reduced by database compression.

Over the last decades, enhancements in CPU

speed have outperfomed improvements in disk

access rates by providing an orders of

magnitude. For instance, the CPU speed has

been enhanced by about one thousand fold,

whereas disk bandwidth and latency have

been enhanced by nearly forty fold

correspondingly. The hardware posses‟ drastic

challenges to database system performance, as

database applications are frequently disk-

bound or memory-bound. This research

examines the uses of compression and backup

techniques to identify the reduced disk I/O and

memory access against additional CPU

overhead. In a compressed database system,

data are accumulated in compressed type on

disk and are decompressed with query

processing.

Information technology has met with several

advances for the production of massive high-

dimensional database systems applicable for

new applications such as corporate data

warehouses, bio-informatics and

networktraffic monitoring. Normally, the sizes

in the datbase systems are in the range of

terabytes, hence it is a challenging purpose to

protect them efficiently. Several techniques

are available to diminish the distinct sizes of

such tables, but the process of using

conventional data compression method is

stationary or dictionary-based. Such strategies

are „syntactic‟ in nature because they

overview the table as a large byte string and

worked at the byte level. More precisly,

compression techniques, which obtain

semantics of the table in the database systems

are taken into consideration at the time of

compression, have obtained necessiate

attention. In particular, these algorithms first

starts to present a descriptive model M, of the

database systems by taking the semantics of

the attributes present in the tables and then

divide them into the three groups with respect

to M:

a. Data values that can be obtained

from model.

b. Data values necessary for obtaining

the data values using M.

c. Data values that do not fit M.

By accumulating only the M combined with

the groups of data values, compression is

mainatined because typically takes up less

storage space contrast to the original database.

The advantages over compression are analysis

of data is complex, fast retrieval of data,

enhancement of query.

To compress data based on the attrbiutes

presence, the complex issue is to select a

appropriate set of representative rows. For this

reason, in this work, we present an algorithm

called HIRAC algorithm which repeatedly

enhances the set of selected representative



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rows. From one step to the next, new

representative rows may be chosen, and old

ones are retarded. Although the representative

rows are changing continuosly, each steps

monotonically enhances the universal quality.

For a smaller number of iterations taken over

with the database systems, it is necessary to

present proficient compression performance.

In addition to this, each steps involved in the

algorithm needs only a single scan over the

database systems, leading to a fast

compression strategy.

2. LITERATURE REVIEW

An authentic datasets are regularly huge

enough to demand data compression.

Conventional data compression strategies care

for the table as a huge byte string and run at

the byte level. A semantic compression

algorithm [1] called ItCompress ITerative

Compression, which attains fine density while

authorizing contact even at attribute stage

without need of the decompression of a

superior unit.

Database compression has been a much

admired subject in the examine literature and

there is a huge quantity of work on this

subject. The most evident motivation to

believe compression in a database background

is to diminish the space essential in the disk.

The paper [2] proposed the firmness of data in

Relational Database Management Systems

(RDBMS) using offered content compression

algorithms. Information theory habitually

compacts with “usual data,” be it textual data,

image, or video data. Nevertheless, databases

of different sorts have appear into survival in

current years for accumulating “alternative

data” counting social data, biological data,

topographical maps, web data, and medical

data. In compressing such data, one must

reflect on two types of information [3] with

lossy compression techniques [4]. A system of

lossy compression of distinct memory less

sources for constricting the sources supported

on bounded distortion measure [5].

Planning at

enhancing compression presentation,

an iterative algorithm [6] is planned to

discover the best separation of a frame with

computation of compression ratio [7]. The

sequential processing utilized by

mainly compression algorithms, signify that

these long-range replication are not noticed

[8]. In order to assemble the confronts of

important storage and application

development, as well as condensed

backup [10] windows and restricted IT

resources, more and more association hold

Hierarchical Storage Management (HSM) [9]

supported on network environment [11].

Run-length encoding (RLE), where repeats of

the same element are expressed as pairs, is an

attractive approach for compressing sorted

data in a column store [12]. The task of string

matching to find the most effective phrase

[13] for replacement in the input stream is not

a simple one. Real-time database compression

algorithm must provide high compression

radio to realize large numbers of data storage

in real-time database [14].

In this work, HIRAC compression algorithm

is used for compressing the database and

allowed the compressed database at multiple

storages in parallel based on row-attribute

iterative compression.

3. PARALLEL MULTI STORAGE

BACKUP COMPRESSION FOR REAL-

TIME DATABASE SYSTEMS The proposed work is efficeintly desgined and

developed for a backup compression process

for real-time database systems using HIRAC

algorithm and can allow the compressed

backups to store it in multiple storages in

parallel. The proposed HIRAC with parallel

multi-storage backup for real time database



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systems comprises of three operations. The

first operation is to identify and analyze the

entire database, the next step is to compress

the database systems to take up as backups

and the last step is to store the compressed

backups in multiple storages in parallel. The

architecture diagram of the proposed HIRAC

based Parallel Multi-Storage Backup model

for real time database systems are shown in

fig 3.1.

The first phase is to analyze the database

based on the environment in which it creates.

At forst, the attributes/tuples present in the

database sytems are analyzed and identify the

goal of the database creation and maintain a

set of attributes and tables maintained in the

database systems.

The second phase describes the process of

taking the backup of database system by

compressing the database using Hierarchical

Iterative Row-Attribute Compression

(HIRAC). The HIRAC algorithm is used to

provide a good compression techniques by

allowing access to the database level and

enhances the compression ratio for a ease

backup of database systems.

The third phase describes the process

of storing the compressed backups at different

levels of storages in paralell. Since it has been

stored at multi-storage devices, the copy of

compressed backups are always available at

any system, there is less chance of database

systems to be lost and can easily be recovered.

Fig 3.1 Architecture diagram of proposed PMBC for real time database systems

From the above figure (Fig 3.1), the process

of parallel multi-storage backup

compression comprises of three steps.

a. Analyzing the entire database

b. Implementing the HIRAC algorithm

c. Copying the data from the database

files into backup devices at parallel.

The compressing techniques have been used

in this work efficiently to compress the



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database systems in an iterative procedures

and process of the compression algorithm is

describes under subsections.

3.1 Analyzing the entire database

Database analysis is apprehensive

with the environment and use of data. It

engages the classification of the data

elements which are desired to sustain the

data dealing out system of the organization,

the introduction of these elements into

rational groups and the description of the

relations among the resulting groups. The

prologue of Database Management Systems

(DBMS) has optimized a superior stage of

analysis, where the data elements are

definite by a rational model or `schema'

(conceptual schema). When conversing the

schema in the context of a DBMS, the things

of substitute designs on the effectiveness or

ease of completion is measured, i.e. the

examination is still somewhat achievement

reliant. The figure below describes the

database analysis life cycle.

Fig 3.2 Database Analysis lifecycle

The progress of strategies of data

analysis has assisted to recognize the

structure and meaning of data in

organizations. Data analysis strategies can

be utilized as the first step of extrapolating

the difficulties of the genuine world into a

form that can be detained on a computer and

be contacted by many users. The data can be

collected by conservative methods.

3.2 Implementing HIRAC algorithm

To compress database, an algorithm

is presented here called HIRAC algorithm

which iteratively enhances the collection of

selected representative rows. From one step

to the next, new representative rows may be

chosen, and old ones discarded. The

algorithm below (fig 3.3) describes the

process of implementing the HIRAC

algorithm.



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Fig 3.3 Algorithm for implementing Database Compression

The algorithm begins by identifying the arbitrary set of rows from the table. It then repeatedly

enhances with the aim of enhancing the total coverage over the table to be compressed. There are

two phases in each iteration:

Phase1 : In this phase, each row R in D is assigned to a representative row Pmax (R) and the

G(Pi) indicates the set of rows that are implied to a representative row [step 3]

Phase2 : In this phase, it identifies the number of occurrences of the categorical attribute

obtained in the table. [step 4 to 6]

The table below describes the parameters used in the algorithm:

Parameter Description

D Database

T Set of tables

R Set of rows

P Set of representative rows

Pi[xj] Value of attribute X for representative row Pi

Pmax (R) Representative row that best matches with R

G(Pi) set of rows which have Pi as the best match

Table 1 Constraints Description

Input: Database D, Set of tables T, rows R

Output: Compressed database with a set of representative rows P = {P1, P2, …, Pk}

Identify an arbitrary set of rows

If total count of (D, T) increases, do

For each T in D

Assign id of each row

Find Pmax (R)

Recompute each Pi in P as folloes

{

For each attribute

Pi[xj] = fv(xj, G(Pi))

End For

End For

End If



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HIRAC algorithm which is discussed above

on the other hand assumes an effortless

viewpoint of “direct optimization”. Since the

endeavor of a compression algorithm is to

decrease the storage constraint for a

database, Compression openly utilize this as

an optimization principle and guarantee that

only patterns which progress the

compression are established in each step of

its iterations. Though the optimization

problem is hard in an existing compression

algorithms case as well, the heuristic used in

this simple. HIRAC algorithm approach

provides much lower time complexity

contrast to an existing technique.

3.3 Parallel storage

Creating and storing backups compressed

database is the same as creating and storing

backups with a single device. The only

difference is to specify al, the backup

devices which are ready to store the backup

data are involved in the operation. The

diagram (fig 3.4) below describes the

process of parallel storage mechanisms.

Fig 3.4 Process of Parallel Multi Storage

Using several backup devices used for backup of the compressed database utilized in parallel in

terms of enhancing the speed of backup and restore operations. Each backup device was

processed from the similar time like other backup devices. The main concern over parallel multi-

storage backup devices is data security. Since the compressed database systems are backup in

several storage devices in parallel, no need to worry about the database system to get lost. Each

storage devices which holds compressed database systems are involved in operations.

4. EXPERIMENTAL EVALUATION

The proposed backup compression process for real-time database systems using HIRAC

algorithm with parallel multi-storage process is implemented in oracle java and used housing

data set derived from UCI repository. The housing dataset consists of 506 instances with 14

attributes, but here we have taken 10 set of attributes.

In this work, we have how the compressed housing database systems using HIRAC algorithm by

identifying the set of representative rows and columns for real time environments. The

implementation of the proposed backup compression process



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for real-time database systems using HIRAC algorithm with parallel multi-storage process in

Java, and it allowed out a series of performance experiments in order to examine the efficiency

of the proposed backup compression process for real-time database systems using HIRAC

algorithm with parallel multi-storage process. The experiments were run on an Intel P-IV

machine with 4 GB memory and 2GHz dual processor CPU. The proposed HIRAC based

Parallel backup model for real time environment is efficiently designed for compression and

taking backup compressed data with the database systems. The performance of the proposed

backup compression process for real-time database systems using HIRAC algorithm with

parallel multi-storage process is measured in terms of

i) Compression ratio

ii) back up time

iii) Throughput

Compression ratio is the ratio of size of the compressed database system with the original size

of the uncompressed database systems.

edsizeUncompresssizeCompressednratioCompressio :

……………. (eqn 1)

Backup time is the time taken to take the backup of compressed database systems.

Throughput defines the system rate of storing the compressed backups in multiple storages at

parallel.

5. RESULTS AND DISCUSSION

In this work, we have seen how the database is efficiently compressed with the proposed backup

compression process for real-time database systems using HIRAC algorithm with parallel multi-

storage process and compared the results with an existing attribute-level compression methods

written in mainstream languages such as Java. We used a real time database for an

experimentation to examine the efficiency of the proposed backup compression process for real-

time database systems using HIRAC algorithm with parallel multi-storage process. The below

table and graph described the performance evaluation of the proposed backup compression

process for real-time database systems using HIRAC algorithm with parallel multi-storage

process.

No. of Data in Database Compression Ratio (gb)

HIRAC Algorithm Existing Attribute

Level Methods

25 1.3 2.9

50 1.9 3.6

75 2.3 4.5

100 3 6.9

125 3.9 5.9

Table 5.1 No. of Data in Database vs Compression Ratio



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The above table (table 5.1) described the compression ratio based on number of data present in

the database. The efficiency of compression using the proposed backup compression process for

real-time database systems using HIRAC algorithm with parallel multi-storage process is

compared with an existing attribute-level compression methods.

25 50 75 100 125

0

1

2

3

4

5

6

7

Compression

ratio (mb)

No. of data in database

Proposed PMBC Existing aattribute level methods

Fig 5.1 No. of data in database vs Compression ratio

Fig 5.1 describes the process of compression ratio based on number of data present in the

database systems. When the number of data in the database increases, the compression ratio of

the database is decreased in the proposed backup compression process for real-time database

systems using HIRAC algorithm with parallel multi-storage process. Since the proposed HIRAC

algorithm is used, the compression of data is done based on the iterative process. So, the

compression ratio become less in the proposed backup compression process for real-time

database systems using HIRAC algorithm with parallel multi-storage process. The compression

ratio is measured in terms of megabyte (mb). Compared to an existing attribute-level

compression methods, the proposed HIRAC achieves good compression rate of 83%.

No. of Data in Database Compression Time (sec)

HIRAC Algorithm Existing Attribute

Level Methods

25 2.9 5.8

50 4.5 9.2

75 7.2 12.1

100 9.3 14.2

125 11.6 17

Table 5.2 No. of Data in Database vs Compression Time

The above table (table 5.2) described the time taken for backup based on number of data present

in the database. The efficiency of backup time using the proposed backup compression process

for real-time database systems using HIRAC algorithm with parallel multi-storage process is

compared with an existing attribute-level compression methods.



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25 50 75 100 125

0

5

10

15

20

Backup time

(sec)

No. of data in database


Fig 5.2 No. of data in database vs. Compression time

Fig 5.2 describes the process of time taken for backup based on number of data present in the

database systems. When the number of data in the database increases, the time taken for backup

for the database is decreased in the proposed backup compression process for real-time database

systems using HIRAC algorithm with parallel multi-storage process. Since the proposed HIRAC

algorithm is used, the compression of data is done based on the iterative process. So, the backup

become less in the proposed backup compression process for real-time database systems using

HIRAC algorithm with parallel multi-storage process. The backup time is measured in terms of

seconds (secs). Compared to an existing attribute-level compression methods, the proposed

HIRAC based parallel backup achieves less backup time and the variance would be 70% better.

No. of Location used

for PMBC

Throughput (%)

Proposed HIRAC

based Parallel Backup

Existing Attribute

Level Methods

1 12 5

2 20 10

3 25 15

4 32 19

5 40 22

Table 5.3 No. of Storage Location vs Throughput

The above table (table 5.3) described the throughput based on number of devices used for

parallel storage mechanism. The throughput obtained using the proposed backup compression

process for real-time database systems using HIRAC algorithm with parallel multi-storage

process is compared with an existing attribute-level compression method.



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1 2 3 4 5

0

10

20

30

40

Throughput

No. of devices used


Fig 5.3

No. of Location vs Throughput

Fig 5.3 describes the process of parallel multi storage mechanisms based on number of devices

involved in the operation. When the number of devices used for multi-storage increases, the

throughput rate for performing the storage of backup at multiple locations in parallel is increases

in the proposed backup compression process for real-time database systems using HIRAC

algorithm with parallel multi-storage process. Since the throughput rate increases, the security

level of the compression of data is also being increased. Compared to an existing attribute-level

compression methods, the proposed HIRAC achieves better throughput and the variance would

be 75% better in the proposed HIRAC with parallel backup.

From the experimental results, it is examined that the proposed backup compression process for

real-time database systems using HIRAC algorithm with parallel multi-storage process achieves

the better compression process and the backups compressed data are also being efficiently stored

under different locations in parallel.

6. CONCLUSION

In this work, we efficiently done the

compression of database system present in

the real time environment. The compression

is done using HIRAC algorithm which

followed iterative steps to achieve the better

compression rate of the database and stored

the backups at different locations in parallel.

The advantages of using the proposed

backup compression process for real-time

database systems using HIRAC algorithm

with parallel multi-storage process are

It increases the speed of backup by

using HIRAC algorithm

It efficiently allowed the database to

be stored at multiple devices at

parallel.

Greatly reduced the time taken for

backup and restore operations.

Experimental results have shown that the

proposed backup compression process for

real-time database systems using HIRAC

algorithm with parallel multi-storage process

are efficient in terms of throughput, backup

processing compared to an existing attribute

level compression methods.



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7. AUTHORS PROFILE

Mr.M.Muthukumar is pursing Ph.D in

Database Compression. He received the

B.Sc degree from Madras University,

Tamilnadu, India in 1997, M.C.A degree

from the Bharathidasan University,

Tamilnadu, India in 2000 and M.Phil from

Manonmaniam Sundaranar University,

Tamilnadu, India in 2003. He is currently

working as Technical Lead in Department of

Application Delivery, MphasisS Limited,

Bangalore, Karnadaka, India. Before

Mphasis Limited he was working as IT

Analyst, in Department of Application and

Software Development, IBM India Private

Limited, Bangalore, Karnadaka, India.

Professor Dr.T.Ravichandran received the

B.E degrees from Bharathiar University,

Tamilnadu, India and M.E degrees from

Madurai Kamaraj University, Tamilnadu,

India in 1994 and 1997, respectively, and

PhD degree from the Periyar University,

Salem, India, in 2007. He is currently the

Principal of Hindustan Institute of

Technology, Coimbatore Tamilnadu, India.

Before joining Hindustan Institute of

Technology, Professor Ravichandran has

been a Professor and Vice Principal in

Vellalar College of Engineering &

Technology, Erode, Tamilnadu, India. His

research interests include theory and

practical issues of building distributed

systems, Internet computing and security,

mobile computing, performance evaluation,

and fault tolerant computing. Professor

Ravichandran is a member of the IEEE, CSI

and ISTE. Professor Ravichandran has

published more than 80 papers in refereed

international journals and refereed

international conferences proceedings.

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