ijcss, vol.4, no.1, 2012 issn: 1803-8328 © usar ...€¦ · data warehouse, advanced data...

IJCSS, Vol.4, No.1, 2012

ISSN: 1803-8328

© USAR Publications

1

S. No.

Paper Title Page No.

1 Data Warehouse in Telecommunication Industry: Survey and

classification

Hoda A. Abdelhafez

2-15

2 Comparison artificial neural network (ANN) and explicit equations

for estimation of the friction factor in pipes

Farzin Salmasi

16-24

3 Impact of Cloud Computing in Developing the Education Process

Ibrahiem M. M. El Emary

25-33


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Data Warehouse in Telecommunication Industry: Survey and classification

Hoda A. Abdelhafez

Information Systems & Decision Support Dept.,

Faculty of Computers & Informatics, Suez Canal University

Al-Shekh Zayid Street, Old campus, Ismailia,

Egypt

[email protected]

Abstract

Data warehouse for decision support system is advanced tool in telecommunications

companies for dealing with huge amounts of data. It helps telecommunications industry to

cope with competitive pressures and achieve higher profits. The theme of this paper is to

focus on data warehousing in telecommunications companies and why they need new

technology and data warehouse platforms. The results demonstrate that the advanced data

warehouse platforms are capable to handle large scale of CDRs and provide daily reports for

decision makers.

a warehouse; data warehouse telecommunications industry; data warehouse; Teradat Keywords:

appliance, SOA; Cloud computing; Exadata.

1. Introduction

Data warehouse is defined as a subject oriented,

integrated, time variant, non volatile collection of

data in support of management's decision making

process (Hotchkiss, 2009). Data warehouse

includes historical data for many years. This stored

data is extracted, transformed, and loaded from

different data sources such as mainframe

applications, OLTP applications, or external

sources.

Early data warehouse was based on database

management systems which were oriented toward

transaction processing, but now most vendors who

are specialized in database management systems

(DBMS) make the transition to data warehousing.

These vendors are IBM’s DB2, Oracle, NT SQL

Server, and Teradata. IBM’s DB2 was offered both

an SMP and MPP architecture; Oracle was applied

SMP architecture; but NT SQL Server started small

and inexpensively. Meanwhile, Teradata became

specialized in handling huge amounts of data using

a shared MPP solution (Inmon, 1995; Inmon,

2007).

The purpose of this paper is to focus on new data

warehouse platforms through survey in

http://us.f522.mail.yahoo.com/ym/[email protected]


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telecommunication companies and illustrate how

these companies overcome the increasing pressure

in analyzing massive volumes of Call Detail

Records (CDRs). The results shows that the new

DW platforms in telecommunication industry can

afford to do detailed analytics on large volumes of

CDRs and can scale to hundreds of terabytes of

data with excellent query performance.

This paper includes the following sections: tradition

data warehouse, advanced data warehouse

platforms, new data warehouse platforms in

telecommunications companies, Oracle Exadata,

and the benefits of advanced DW platforms

compared with traditional data warehouse.

2. Related Works

Within Telecommunication Companies, there are

three infrastructure groups: business management,

service management and network management.

These groups might build and utilize together a data

warehouse for a decision support system. These

companies are installing call detail record (CDR)

based decision support system which contain a gold

mine information about customer, products,

networks and competitors in order to maximize

access and use of these corporate information

(Conine, 1998).

In telecommunication industry, the first generation

of data warehouse applications was based on push

approach which is loaded all call detail records

(CDRs). The result of this approach was huge of

warehouses that were rich in data but poor in

business intelligence. The second generation of

data warehouse applications was based on pull.

This approach reduces the storage requirements and

ensures that decision makers can view the contents

of the data warehouse in a meaningful and useful

format (Frost, 2009).

The vision in this paper is to compare tradition data

warehouse with new DW platforms in

telecommunication companies and describe how

these companies apply the new data warehouse

platforms. The main finding is that using new DW

platforms can help telecom companies to take

competitive advantage in the market.

3. Traditional Data Warehouse

Telecommunications become one of the most

competitive business arenas of today's market

because of three forces: technology development,

user demand and deregulation. Early, long distance

service was the heart of the telecommunication

companies. Nowadays there are new services such

as cellular phones and wireless Internet; these

services are demanded by users with increased

quality (Krivda, 2008).

Telecommunication companies deals with large

amount of customer inputs; these inputs come from

numerous channels, including call centers and

increasing number of online transactions and data

warehouse is the best way to integrate and manage


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these transactions. Moreover, each company needs

to have a full history of the customer transactions in

its database in addition to the new transactions.

Although those issues are similar to the ones faced

by many companies applying data warehouses, the

telecommunications industry does have some

unique requirements. The sheer volume of data is

one of these requirements.

The huge amounts of information could be stored in

traditional data warehouse architectures, but in the

new millennium, the massive growing of data

volumes demonstrated inability to provide the

detailed analysis that businesses needed in a timely

manner. On these traditional data warehouse, the

process of SQL queries took days or even weeks to

provide the required information which is out of

date especially in this type of business (Lamont,

2000).

Since there is a difference between a data

warehouse and the platform that manages it, you

can remodel the DW significantly to add value

without replacing the platform. There are some new

generations involve tools that are tangential to the

platform, such as solutions for data integration,

quality, master data, reporting, and others.

Incremental additions to hardware are common (to

add more CPUs, memory, or storage), and these

satisfy next generation requirements (fast queries,

in-memory databases, and scalability) by doing

more with the current platform (Russom, 2009).

4. New Data Warehouse Platforms

The telecommunications industry has been a major

user of traditional data warehousing technologies

for many years. However, the cost and difficulty of

scaling these platforms has limited their ability to

support large scale analysis of CDRs. As a result,

data warehousing has been generally limited to

supporting the billing cycle. This situation is

getting worse as new sources of traffic such as

VoIP are increasing volumes to beyond one billion

records per day. Legislation and competitive

pressures are forcing carriers to retain CDRs for up

to 25 months. In addition, Fraud detection and

network traffic analysis require near real-time

access to data (Frost, 2007).

Advanced data warehouse platforms such as data

warehouse appliances and software appliances

provide many more options and new interest today

as well as columnar databases. Moreover, open

source Linux is also common in data warehousing.

Recently, the new platform includes real-time

integration between the data warehouse platform

and operational applications, several types of

advanced analytics, and reusable interfaces

(Russom, 2009).

According to the survey conducted by the data

warehouse institute, real-time data warehousing has

the greatest projected growth rate of the DW

options surveyed. Vendors with database


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management systems (DBMSs) have added new

features to their products and helped develop best

practices for RTDW and similar DW options such

as Teradata’s “active data warehousing” and IBM’s

“dynamic data warehousing. Also, the prospects for

data warehouse appliances (DWA) are very

positive, based on the survey’s indications of good

growth. This balance of growth and commitment

shows that the DWA has definitely “arrived” as a

common DW platform.

Moreover, Service-oriented architecture (SOA) is

the most anticipated hot data warehouse options;

SOA is adopting services to data warehousing

which will lead to more real-time interfaces. In

addition to cloud computing, one of the newest

platforms which enables customers to leverage

platforms and software that are more scalable and

cost effective It is fully utilize the server resources

with less administrative work as compared to

traditional data center approaches. (Russom, 2009).

5. Applied New Data Warehouse

Platforms in Telecommunication

Industry

This section discuses the real applications of the

new DW platforms in the large telecommunication

companies. These platforms include Data

Warehouse Appliances, Active Enterprise Data

Warehouse (Active EDW), Service Oriented

Architecture (SOA), and Cloud Computing.

Data Warehouse Appliances

A. Orange UK

Orange UK covers 99% of the UK population as a

mobile phone service provides high quality. Orange

UK had been using Business Intelligence (BI)

systems to manage and quickly leverage vast

amounts of corporate data. But the growing volume

of data caused a lot of problems such as low

performance, the significance of data latency and

strained infrastructure (Sawkins, 2009).

In 2003, Orange applied data warehouse appliance

technology from Netezza to analyze billions of Call

Detail Records (CDRs). It became the first

organization in Europe using data warehouse

systems. DW appliance provides Orange with

several significant benefits in data quality, the areas

of performance and data center space. In previous

BI system, the number of queries that could be

performed was very low because of the poor

performance. These queries would take 12-24 hours

on the company’s previous system. But now in the

new system, Orange is able to perform an average

of 1,800 complex queries per week. Using the

Netezza DW appliance system, the average query

run is up to 90 seconds and almost all queries queue

for less than 3 seconds. Orange became to realize

that improving query performance has increased the

quality of output and business decision making.


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The decisions were made based on data that was

eight weeks old but now they are made based on

daily report.

B. Reliance Communications

Reliance Communications is the second-largest

telecommunications company with a digital

network covering over 14,000 towns and 400,000

villages in India. It is also the telecommunications

arm of Reliance Group which is the largest private

sector company (Greenplum, 2008).

Traditional database systems in Reliance

Communications could not able to operate the

business because the increase in demand of its

services was producing explosive growth in the

systems and infrastructure. The ability to provide

accurate, timely analytics to all parts of the business

was becoming more acute. Reliance’s rapid growth

caused inability of traditional database systems to

scale and perform; a request for records took

multiple days to deliver. The Reliance Company

applied the S1004 model of the Sun Data

Warehouse Appliance. This model (S1004)

integrates Greenplum Database with 4 Sunfire

X4500 systems and a Sunfire X4200 system. As a

result of implementing the S1004, a request for

detailed call records would take few hours instead

of multiple days. Compared to Reliance’s previous

database system, the Greenplum system reduced the

average time to load a day’s worth of data by over

90 percent, from 2 hours to less than 10 minutes.

Active Enterprise Data Warehouse (Active EDW)

A. KDDI

KDDI is the second largest telecommunications

company in Japan, has implemented a Teradata

Active Enterprise Data Warehouse (EDW).

Teradata’s Active Enterprise Data Warehouse

platform, designed for fast processing speed and

scalability for complex data analysis by many

concurrent users. The Active EDW will serve as

the data retrieval and analysis platform that KDDI

uses to perform multi-faceted analyses of customer

and other related data from its mission-critical sales

support and planning system. Testing of the

Teradata Active EDW platform demonstrated a

seven-time increase in the average performance

than the current data warehouse system, without

requiring tuning (Hotchkiss, 2009).

The Teradata enables KDDI to integrate data from

customers and other critical information from

KDDI service operations, and to analyze that

information to further improve customer service.

Using the Teradata Active EDW platform for

extensive analysis of KDDI business operations

will help it find ways to improve its customer

service. Active EDW platform compared with the

previous system according to these improvements,

and the lower cost of operating a Teradata, the

KDDI will begin to realize a return on this

investment.


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B. Bouygues Telecom

Bouygues Telecom is a wireless

telecommunications company, the third largest one

in France. Bouygues telecom has applied Teradata

for a real-time enterprise data warehouse (EDW).

This system will support sales, finance, marketing,

fraud and insurance revenue processes through

transforming customer information into enterprise

intelligence (Baudet and O'Sullivan, 2008).

Data warehouse in Bouygues Telecom will provide

first, the required real-time business information to

support daily operations and second, the high-level

business information to support strategic decision-

making. Implementation of Teradata active data

warehouse began with the consolidation of huge

amounts of customer data from numerous data

marts into a central data repository. The centralized

EDW provides services to various departments with

greater speed and consistency. It also provides

users with faster access to information and an

integrated view of customer relationship

intelligence in order to increase their insight for

better decisions.

C. Vodafone New Zealand

In 2004 Vodafone became a leading

telecommunications company in New Zealand. It is

a subsidiary of the United Kingdom global

telecommunications company. Competitive and

tougher market let Vodafone executives recognized

that faster decision making required real-time

knowledge of current conditions (Krivda, 2008).

The legacy system in Vodafone was Red Brick data

warehouse. This legacy system was unable to

support the company needs in modeling processes,

transactional analysis, or research in order to meet

its goals. Vodafone has selected an enterprise data

warehouse (EDW) from Teradata in 2004 as well as

the Teradata Communications Logical Data Model.

Vodafone was able to use the advanced features of

the EDW to create advanced analytics, research and

competitive intelligence. The EDW provides a lot

of benefits as multi-variant analysis, pre-designed

models, online analytical processing, query and

model performance, hot staging of reports, an

integrated database that supports reports, fixed

downstream feeds, comprehensive ad hoc analysis

and customer segmentation. Vodafone was able to

use the power of the EDW to create advanced

analytics, research and competitive intelligence.

The EDW is also used to enhance Vodafone

customer life cycle program to build a scientific

base for optimum communication with customers.

It can get campaigns to the customer more

efficiently and effectively, through determine who

the company campaign to, and what is relevant to

the customer. Therefore, decision makers are

receiving more accurate information and faster than

ever before.

D. Xinjiang Telecom


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Xinjiang Telecom, part of the China Telecom

group, is a leader in the Xinjiang

telecommunication market. The number of

telephone users in Xinjiang is the largest among all

12 of China's western provinces. In order to

increase the efficiency and quality of its decision-

making process and to improve its targeted

marketing capability, Xinjiang Telecom decided to

build a data warehouse system in 2004. Xinjing

Telecom selected Teradata's data warehouse and

data mining technology to build a single data

platform. The centralized data platform provides

powerful support to Xinjiang Telecom's decision-

making and marketing re-engineering initiative

(Gale, 2006).

Service Oriented Architecture (SOA)

A. ChungHwa Telecom Company

ChungHwa Telecom, the largest telecommunication

company in Taiwan is the 14th largest in the world.

The company sought to upgrade its existent billing

system to one that was both NGOSS-compliant

SOA-based. It consists of an operations support

system using NGOSS (Next Generation Operations

System and Software) and it implements a service

oriented architecture (SOA) that relies on an

enhanced enterprise service bus (ESB). This

enhanced ESB makes it possible to carry out

changes to business rules at runtime, thus avoiding

costly shutdowns to the billing application.

Implementing this system provides complete

support to its billing application. As a result, the

billing process cycle time has been reduced from

10–16 days to 3–4 days (Chen, Ni and Lin, 2008).

B. Alestra in Mexico

Alestra is the third-largest telecommunications

provider in Mexico. It offers broadband, long

distance, and high-touch integrated

communications services to corporate and

residential customers. Alestra is using TIBCO’s

SOA and BPM (business process management)

platform to facilitate the process of upgrading and

integrating applications for CRM, billing, inventory

management, activation and other applications.

The integration was for 14 platforms and almost

100 services. Alestra’s successful approach

leverages TIBCO’s SOA platform to simplify

connectivity with both existing and new systems

(Ancira, 2009).

TIBCO’s BPM and SOA software helps the

company’s sales force, customer care

representatives and operations team to manage and

achieve customer requests throughout the entire

product lifecycle and reduce the time required to

complete service requests.

C. British Telecom

British telecom Group is one of the largest

telecommunications services companies in the

world and has operations in more than 170

countries. BT wants to allow customers to manage

their own subscribed services online through a


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Web-based interface. It also needs to reduce the

time and cost of integration and become a lot more

agile. BT decided to go with Microsoft’s CSF

(Connected Services Framework), a SOA-based

service-delivery platform that functions as an

extension of Microsoft BizTalk Server, SQL

Server, and Windows Server 2003. SOA enables

BT to cobble together new offerings with those of

third parties and integrate them quickly with their

internal, billing system, provisioning, and other

support systems (Erlanger, 2005).

Cloud Computing

A. AT&T

AT&T is the largest provider of mobile

telephony service in the United States, with over

95.5 million wireless customers and more than 210

million total customers (AT & T report). AT&T's

began to use cloud computing in 2006, its cloud

service, dubbed AT&T Synaptic Computer

Services, delivers on-demand computing-as-a-

service (CaaS).The AT&T cloud is built on the Sun

Open Cloud Platform and utilizes Sun Cloud API's

in conjunction with a VMware virtual environment.

The AT&T cloud is much more similar to the

Amazon which offers a self-service portal that

enables customers to add computing power or

storage space on the fly. AT&T is closely

following the Amazon blueprints to duplicate that

success (Bradley, 2009).

B. China Mobile

China Mobile is one of the big three integrated

telecom operators in the country. The company

wants to capitalize on an upcoming cloud

computing boom in the telecom industry. It was

developing a cloud computing platform BigCloud

to come up with the diversifying demand in the 3G

era. In its new platform, China Mobile adopted

some applications and high-efficiency cloud

computing management software like parallel

algorithms for data mining, cloud storage, large

capacity database, as well as search engine. The

China Mobile goal is to provide featured mobile

Internet information service for individual users

and corporate users. The company, via its

BigCloud platform, provides various mobile

Internet services like instant information, content

storage, mobile map, mobile mailbox, information

search, integrated communication, music

downloading, as well as mobile directions (C114,

2009).

6. Oracle Exadata

Oracle transformed the data warehousing industry,

with every customer experiencing performance

improvements of at

least 10x or more. With Exadata V2 Oracle

provides faster performance and the flexibility to

consolidate data warehousing as well as OLTP

workloads on the same machine (Russom, 2009).

The Sun Oracle Database Machine combines

industry-standard hardware from Sun, Oracle

http://en.wikipedia.org/wiki/Mobile_telephony

http://en.wikipedia.org/wiki/Mobile_telephony


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Database 11g Release 2, and Oracle Exadata

Storage Server Software to create a faster, more

versatile database machine. It represents a

completely scalable and fault-tolerant package for

data warehousing and transaction processing

applications. The Sun Oracle Database Machine

also includes Sun’s new FlashFire technology to

cache “hot” data for dramatically improved

transaction response times and throughput.

A. China Mobile Group (Liaoning Mobile)

China Mobile Group provider in Liaoning Province

(Liaoning Mobile) is the largest mobile

communications serving 27 million customers and

generating annual revenue of US$2.3 billion. The

company has 14 branch offices and 56 county

offices in the province, and it is responsible for

communications network construction,

maintenance, and service. Liaoning Mobile is a

subsidiary of China Mobile Group, China’s leading

mobile communications provider.

Due to rapid growth in its mobile communications

business, Liaoning Mobile’s business operations

systems were struggling to process data in real

time, and to support an increasing number of users.

The company had to find a more efficient way to

manage and use the database resources needed to

run these systems. Therefore, Liaoning Mobile

implemented Exadata Database Machine X2-2 to

build a database cloud architecture that improved

performance and system resource utilization and

cut deployment time for new applications.

Implementing Oracle Exadata Database Machine in

Liaoning Mobile achieved a more than six-fold

improvement in the performance of the business

operations systems. Staff satisfaction and

productivity increased, as the improved system

response speeds reduced wait times for processing

queries and transactions. The Liaoning Mobile has

also eliminated the need to make continuous

adjustments to databases, servers, and storage

systems as data volumes increase which made

database expansion easier (Oracle Customer

Success Stories, 2011).

B. Turkcell

Turkcell İletişim Hizmetleri A.Ş. is a leading global

system provider for mobile communications (GSM)

in Turkey. It has more than 34 million subscribers

and ranks third in Europe and 16th in the world by

number of subscribers. Turkcell covers

approximately 83% of the Turkish population

through its 3G and 99% through its 2G technology

supported network. The company manages 250

terabytes of data in an enterprise data warehouse

including more than 500 Oracle databases and more

than 150 new databases under development.

Running reports required analysis of up to 1.5

billion call data records generated by the

company’s customers, daily. To overcome these

challenges the company applied Oracle Exadata

Database Machine to reduce the size of the

company’s data warehouse to 25 terabytes with

hybrid columnar compression and simplified the

system architecture from ten storage cabinets to one

full rack. Turkcell replaced 10 data-storage

cabinets and a Sun M9000 server with a single full-


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rack high performance Exadata Database Machine.

Oracle Exadata reduced the mean time for

producing a report tenfold—from 27 minutes to just

3 minutes—and doubled reporting speed from 45%

to 90% of all reports (Oracle Customer Snapshot,

2010; Oracle Customer Success Stories, 2011).

C. SK Telecom

SK Telecom is a mobile communications service

provider, the largest one in Korea. The company

specializes in data-driven applications and

advanced multimedia services delivered to cell

phones, personal digital assistants, and MP3

players. It provides music and streaming video

services such as movies, video clips, animation,

games, and sports and television programs, as well

as real-time financial information (stock trades,

investments, and insurance policies) (Oracle

customer case study, 2010) . SK Telecom wanted

to ensure it could handle the growth in data

volumes, as well as improve billing verification and

analysis to prevent errors, SK Telecom decided to

implement Oracle Database Machine with Exadata

as its new database storage platform.

SK Telecom’s current billing

system manages the use of data

and information linked to more

than 210 wired and wireless

internet service systems. These

systems process an average of

500 to 600 million billing

transactions daily.

Oracle Database Machine is

linked to the billing system to

enable the reliable collection,

storage, and analysis of complex

billing data in a timely manner.

It analyzes the billing data and

highlights any inconsistencies so

that the errors can be fixed

before bills are sent to

customers. Since moving to

Oracle Database Machine, data

warehouse querying

performance has improved ten-

fold. The platform can analyze

24TB usable data in a 50-day

window, equivalent to about 1.2

billion transactions.

D. SoftBank

SoftBank Mobile Corporation established in 1986

as a leading mobile telecommunications service

provider based in Tokyo, Japan. It offers a range of

mobile services that run on Wideband Code

Division Multiple Access (W-CDMA) and

Universal Mobile Telecommunications System

(UMTS) 3G networks. SoftBank Mobile has

achieved the highest growth in Japan’s mobile

phone market over the past two years. The increase

in subscribers from a previous average of 50,000

per month into more than 200,000 subscribers per

month strained the company’s data warehouse. In


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2009 softbank decided to replace 36 Teradata racks

with just three Oracle Exadata racks after testing

oracle exadata database machine and during this

test, the company’s data warehouse performance

improved by up to eight times. The new data

warehouse, running on Oracle Exadata, is

connected to the customer care and billing system,

which runs on Oracle database. It can store up to

150TB of data, an increase in capacity of 150% on

the previous Teradata solution as well as reducing

database running costs by 50% and operational

costs by more than half (Oracle customer case

study, 2011).

7. Advantages of the New DW

Platforms Compared with

Traditional Data Warehouse

Many critical telecommunications functions rely on

fast, complex analysis of CDR data including

billing, revenue assurance customer relationship

management (CRM) and network performance.

Applying a single complex BI query against

billions of records using traditional DW systems

takes hours or days which results in incomplete

information for decision-making (Business

Intelligence Guide, 2006). Moreover, the terabytes

of dynamic customer data will continue to expand

as carriers add new services and as IP-based traffic

increases. This expanding volume of data is

straining the performance capabilities of relational

databases, servers and storage systems that provide

the foundation for BI (Business Intelligence Guide,

2006).

Advanced data warehouse platforms can face these

challenges in telecommunication companies by

handling real time, analysis of scalable database at

the CDR level. These DW platforms such as real

time data warehouse and data warehouse appliances

could provide decision makers with daily reports

and the queries would take only few hours.

Therefore, advanced data warehouse can effectively

apply CRM, revenue assurance, fraud prevention

and network traffic analysis (NETEZZA, 2004).

Moreover, Oracle exadata transformed the data

warehousing industry, Telecommunications

Company saved 33 hours of batch processing time

and is able to analyze more data faster.

8. Conclusion

The challenges of

Telecommunications industry

including legislation,

competitive pressures, , difficult

to scale large data, analysis

network traffic, fraud detection

and others demonstrate the

limitations of traditional data

warehouse. Therefore, the new

categories of data warehouse

platforms and new technologies


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such as Exadata are able to

overcome these limitations.

These platforms and technology

can help the telecommunications

industry to handle complex

queries against multi terabyte

data sets. Also, decision makers

are more certain about their

decision because they receive

daily reports instead of reports

based on old data.

9. References

1. Ancira R. (2009) “TIBCO Integrates

Alestra´s Telecom Infrastructure with

SOA/BPM Platform”, TIBCO Software

Inc. http://www.tibco.com/multimedia/ss-

alestra_tcm8-9650.pdf

2. Baudet C. and M. O'Sullivan (2008),

“Teradata Selected By French Telecom

Leader Bouygues for Real-Time”

Enterprise Data Warehouse,

http://www.teradata.com/t/newsrelease.asp

x?id=5977.

3. Bradley T. (2009), “IBM and AT&T

Unveil Cloud Computing Services”,

PCWorld Business Center,

http://www.pcworld.com/businesscenter/ar

ticle/182238/ibm_and_atandt_unveil_clou

d_computing_services.html.

4. C114 Online Media (2009) “China Mobile

Brews Cloud Computing Platform”,

http://www.cn-

c114.net/576/a416006.html.

5. Chen I., Ni G. and Lin C (2008), “A

runtime-adaptable service bus design for

telecom operations support systems”, IBM

Systems Journal, Vol. 47 No. 3, PP. 445-

456.

6. Conine R. (1998), “The data warehouse in

the telecommunications industry”, IEEE,

vol.1, PP. 205 - 209.

7. Erlanger L. (2005) “British Telecom dials

into SOA”, InfoWorld,

http://www.infoworld.com/d/developer-

world/british-telecom-dials-soa-898.

8. Frost S. (2007), “Saving

Telecommunications Data Warehousing

with DATAllegro”, White Paper, Version

(1), January,

http://www.datallegro.com/pdf/white_p

apers/wp_telcoms.pdf

9. Gale T. (2006), “Xinjiang Telecom

implements Teradata warehouse for its

business analysis system”, China Telecom

Magazine, Vol.13, No. 4, Page: 19(2).

10. Gomez J. (1998), “ Data Warehousing for

the Telecom Industry”, Information

http://www.tibco.com/multimedia/ss-alestra_tcm8-9650.pdf

http://www.tibco.com/multimedia/ss-alestra_tcm8-9650.pdf

http://www.teradata.com/t/newsrelease.aspx?id=5977

http://www.teradata.com/t/newsrelease.aspx?id=5977

http://www.pcworld.com/businesscenter/article/182238/ibm_and_atandt_unveil_cloud_computing_services.html



http://www.cn-c114.net/576/a416006.html

http://www.cn-c114.net/576/a416006.html

http://www.infoworld.com/d/developer-world/british-telecom-dials-soa-898

http://www.infoworld.com/d/developer-world/british-telecom-dials-soa-898

http://www.datallegro.com/pdf/white_p%20apers/wp_telcoms.pdf

http://www.datallegro.com/pdf/white_p%20apers/wp_telcoms.pdf


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Management Magazine, December,

http://www.information-

management.com/issues/19981201/260-

1.html

11. Greenplum (2008), Reliance

Communications Case Study, February,

http://www.greenplum.com/studies/?reg=1

12. Hotchkiss D. (2009), “ Japan’s Second

Largest Communications Carrier Chooses

Teradata for Enterprise Information

System”, Teradata news release,

http://www.teradata.com/t/newsrelease.

aspx?id=11507

13. Inmon W. (1995), “What is a Data

Warehouse?”, Prism Solutions, Vol. 1,

No. 1.

14. Inmon W. (2007), “A Brief History of

Data Warehousing: From the Vendors

Perspective”, EIMI Archives, Volume (1)

Issue 3 May,

http://www.eiminstitute.org/library/eimi-

archives/volume-1-issue-3-May-2007-

edition/a-brief-history-of-data-

warehousing-from-the-vendors-

perspective-part-i.

15. Krivda C. (2008), “ Dialing up growth in

a mature market: Vodafone New Zealand

Ltd. combines Teradata and powerful

analytics to optimize customer

communications and improve retention”,

Teradata Magazine-March,

http://www.teradata.com/tdmo/v08n01/

pdf/AR5549.pdf.

16. Lamont J. (2000), “Data warehouse in

telecommunication industry”, KMWorld

magazine.

http://www.kmworld.com/Articles/Editoria

l/Feature/Data-warehousing-in-the-

telecommunications-industry-9153.aspx

17. NETEZZA (2004), “Transforming

Telecommunications Business

intelligence: Real-Time, comprehensive

Analyses for Proactive Business

Decisions”, White Paper,1.866. Netezza,

www.netezza.com.

18. Oracle customer case study (2010), “SK

Telecom Builds Database Infrastructure

That Analyzes up to 1.2 Billion

Transactions Daily”, January,

http://www.oracle.com/customers/snapsho

ts/sk-telecom-rac-case-study.pdf.

19. Oracle Customer Snapshot (2010),

“Turkcell İletişim Hizmetleri A.Ş.

Reduces Mean Reporting Time Tenfold

for More Than 50,000 Reports”,

http://www.oracle.com/us/corporate/custo

mers/turkcell-exadata-snapshot-

189469.pdf.

20. Oracle Customer Success Stories (2011),

“Information for success: Customers

http://www.information-management.com/issues/19981201/260-1.html



http://www.greenplum.com/studies/?reg=1

http://www.teradata.com/t/newsrelease

http://www.eiminstitute.org/library/eimi-archives/volume-1-issue-3-May-2007-edition/a-brief-



http://www.teradata.com/tdmo/v08n01/

http://www.kmworld.com/Articles/Editorial/Feature/Data-warehousing-in-the-telecommunications-industry-9153.aspx



http://www.netezza.com/

http://www.oracle.com/customers/snapshots/sk-telecom-rac-case-study.pdf

http://www.oracle.com/customers/snapshots/sk-telecom-rac-case-study.pdf

http://www.oracle.com/us/corporate/customers/turkcell-exadata-snapshot-189469.pdf




ISSN: 1803-8328


15

Achieve Extreme Performance at Lowest

Cost with Oracle Exadata Database

Machine”,

http://www.oracle.com/us/products/databa

se/exadata-reference-booklet-400018.pdf.

21. Oracle Customer Snapshot (2011),

“Softbank Mobile improves database

query performance by up to eight times’,

http://www.oracle.com/us/corporate/custo

mers/softbank-mobile-corp-7-exadata-cs-

214491.pdf

22. Russom P. (2009) “Next Generation Data

Warehouse Platforms”, TDWI Best

Practice Report,

http://www.oracle.com/database/docs/t

dwi-nextgen-platforms.pdf.

23. Sawkins S. (2009), “Orange and Netezza:

Dealing with the Business End of BI”,

http://www.netezza.com/documents/ora

nge_case_study.pdf.

24. Sun Microsystems (2006), “Business

intelligence and data warehousing Sun

Microsystems”,

http://businessintelligence.ittoolbox.com/bro

wse.asp?c=BIWhite+Papers&r=http%3A%2

F%2Fwww%2Esun%2Ecom%2Fstorage%2

Fwhite%2Dpapers%2Fbidw%2Epdf

25. The Business Intelligence Guide (2006),

“Real-Time Analysis of Telco Data, The

Business Intelligence Guide”,

http://www.thebusinessintelligenceguide.c

om/industry_solutions/Telco/Telco_Data_

Manqgement/Real_Time/CDR_Analysis.p

hp.

http://www.oracle.com/us/products/database/exadata-reference-booklet-400018.pdf

http://www.oracle.com/us/products/database/exadata-reference-booklet-400018.pdf

http://www.oracle.com/us/corporate/customers/softbank-mobile-corp-7-exadata-cs-214491.pdf



http://www.oracle.com/database/docs/t%20dwi-nextgen-platforms.pdf

http://www.oracle.com/database/docs/t%20dwi-nextgen-platforms.pdf

http://www.netezza.com/documents/ora%20nge_case_study.pdf

http://www.netezza.com/documents/ora%20nge_case_study.pdf

http://businessintelligence.ittoolbox.com/browse.asp?c=BIWhite+Papers&r=http%3A%2F%2Fwww%2Esun%2Ecom%2Fstorage%2Fwhite%2Dpapers%2Fbidw%2Epdf




http://www.thebusinessintelligenceguide.com/industry_solutions/Telco/Telco_Data_Manqgement/Real_Time/CDR_Analysis.php





ISSN: 1803-8328


16

Comparison artificial neural network (ANN) and explicit equations for

estimation of the friction factor in pipes

Farzin Salmasi, Assistant Prof., Department of Water Engineering, Faculty of Agriculture, Tabriz

University, Tabriz-Iran. Email: [email protected] Phone: +98 4113392786

ABSTRACT

A non-iterative procedure was developed, using an artificial neural network (ANN), for

calculating the friction factor (f) in the Darcy-Weisbach equation when estimating head losses

due to friction in closed pipes. The successive substitution method was used as an implicit

solution procedure to estimate the f values for a range of Reynolds numbers, Re, and relative

roughness /D values. In developing the ANN model, two configurations were evaluated: (i)

the input parameters Re and /D was taken initially on a linear scale; (ii) input parameters Re

and /D was transformed to a logarithmic scale. Configuration (ii) yielded an optimal ANN

model with one hidden layer and 5 neurons in it. This configuration has R2 and RMS values

0.995 and 0.0218 respectively and was capable of predicting the values of f in the Darcy-

Weisbach equation and was in close agreement with those obtained using the numerical

technique. In addition comparison among some previous empirical equations for calculation

of the f and numerical method performed.

Keywords: Neural network modeling; Hydraulics of pipe flow; Darcy-Weisbach equation.

1. Introduction

The energy loss due to friction undergone

by a Newtonian liquid flowing in a pipe is

usually calculated through the Darcy–

Weisbach equation:

g

V

D

Lfh f

2

2

(1)

In this equation f is the so-called Moody or

Darcy friction factor which, from the

above equation, is calculated as follows:

22 2/12/1 V

P

L

D

V

gh

L

Df

f

(2)

The friction factor depends on the

Reynolds number (Re), and on the relative

roughness of the pipe, ε/D. For laminar

flow (Re<2100), the friction factor is

calculated from the Hagen–Poiseuille

equation (Romeo, et al., 2002):

VDRf

e

6464 (3)

mailto:[email protected]


ISSN: 1803-8328


17

For turbulent flow, the friction factor is

estimated through the equation developed

by Colebrook and White (Colebrook and

White, 1937):

)523.2

7.3(log2

1

fRDf

(4)

The Colebrook–White equation is valid for

Re ranging from 4000 to 108, and values of

relative roughness ranging from 0 to 0.05.

The formula is often used in pipe network

simulation software. It has an implicit form

in which the value of f appears on both

sides of the equation. Obtaining an

accurate solution for f can be very time

consuming, requiring many iterations. An

approximate equation for f that does not

require iteration can be used to improve

the speed of simulation software.

This equation covers the limit cases of

smooth pipes, ε = 0, and fully developed

turbulent flow. For smooth pipes, Equation

(4) turns into the Prandtl–von Karman

(Colebrook, 1939):

71.3

/log2)(log214.1

1 D

Df

(5)

If the flow is fully developed, it is verified

that 200/ fDRe . In this case, the

friction factor depends only on the relative

roughness and can be calculated through

the equation deduced by von Karman

(Colebrook, 1939):

52.2log28.0)(log2

1 fRfR

f

ee (6)

Unless the Karman number, Re√f , is

previously known, i.e. the pressure drop of

the fluid in the pipe is known, Equations

(4) and (6) are implicit with respect to the

value of f, and are solved using numerical

methods. Thus, if the auxiliary variable F

is defined as 1/√f , the Colebrook–White

equation (Equation (4)) can be re-written

to be solved by a method of successive

substitution:

)523.2

7.3(log21 nn F

RDF

(7)

Equation (7) converges very rapidly,

especially if there is a good initial

estimation of the friction factor. For this

the graph produced by Moody (1947) or

any of the explicit equations available in

the literature can be used.

An alternative solution to the iterative

methods is the direct use of an explicit

equation which is precise enough to

calculate the value of f directly. In the case

of smooth pipes, in which f depends only

on Re, Gulyani (1999) provides a revision

and discussion of the correlations more

commonly used to estimate the friction

factor. In the general case of rough tubes,

numerous equations have been proposed

since the 1940s. In this work, in addition of

applying ANN, a revision of those more

frequently used is presented.

More (2006) obtained an analytical

solution of the Colebrook and White

equation for the friction factor, using the

Lambert W function. Romeo, et al. (2002)

reviewed the most common correlations

for calculating the friction

factor in rough and smooth pipes. From

these correlations, a series of more general

equations has been developed making

possible a very accurate estimation of the


ISSN: 1803-8328


18

friction factor without carrying out

iterative calculus. In recent years, artificial

neural network (ANN) models have

attracted researchers in many disciplines of

science and engineering, since they are

capable of correlating large and complex

datasets without any prior knowledge of

the relationships among them. ANNs were

applied by Yuhong and Wenxin (2009) to

predict the friction factor of open channel

flow, by Zahiri and Dehghani (2009) to

determine flow discharge in straight

compound channels, by Ozgur Kisi (2004)

to predict mean monthly stream flow, by

Nakhaei (2005) for estimating the

saturated hydraulic conductivity of

granular material and by Landeras et al.

(2009) for forecasting weekly

evapotranspiration.

The overall objective of the present study

was to devise and evaluate a non-iterative

scheme for estimating the friction factor, f,

in the Darcy-Weisbach equation using an

ANN as a means to avoid the need for a

time-consuming, iterative solution of the

Colebrook equation. Also comparisons

between numerical solutions of Colebrook

with some empirical equation performed

.

1.1. Review of previous equations for

calculation of the friction factor

The most widely used equations postulated

since the end of the 1940s are stated below

in the order of publication.

(a) Moody (1947) proposed the following

empirical equation:

3

1

64 10

10*210055.0RD

f

(8)

According to the author, this equation is

valid for Re ranging from 4000 to 108 and

values of ε/D ranging from 0 to 0.01.

(b) Later, Wood (1966) proposed the

following correlation:

134.0

225.0

62.1,88.0

53.0094.0

.

DC

Db

DDa

Rbaf c

(9)

This equation is recommended for Re

between 4000 and 107 and values of ε/D

ranging from 0.00001 to 0.04.

(c) Churchill (1973), using the transport

model, deduced the following expression:

9.09.0

869.0/1 7

7.3

/log2

17

7.3

/

R

D

fR

De

f (10)

(d) Churchill (1977), again proposed the

following equation valid for the whole

range of Re (laminar, transition and

turbulent):


ISSN: 1803-8328


19

16

169.0

12

1

2

312

37530

7

7.3

/log2

88

RB

R

DA

BAR

f

(11)

(e) Chen (1979) proposed the following

equation:

8981.0

1098.1 8506.5

8257.2

)/(0452.5

7065.3

/2

1

R

DLog

R

DLog

f

(12)

This method involves carrying out two

iterations of the Colebrook–White

equation. The accuracy of the results

obtained from this equation is high due to

the fact that the initial estimate is good.

The equation proposed by Chen is valid for

Re ranging from 4000 to 4*108 and values

of ε/D between 0.0000005 and 0.05.

(f) Barr (1981), by a method analogous to

that used by Chen (1979), proposed the

following expression:

0.70.52

4.518 log 712log

3.7 1 . 29

RD

f R R D

(13)

(g

) Zigrang and Sylvester (1982) also

followed the same method as that used by

Chen (1979), but carried out three internal

iterations. They proposed the following

equation:

1 5.02 5.02 132log log log

3.7 3.7 3.7

D D D

R R Rf

(14)

2. Material and methods

2.1. Methodology

The development of any ANN model

involves three basic steps: the generation

of data required for training, the training of

the ANN model, and the evaluation of the

ANN configuration leading to the selection

of an optimal configuration. The ANN

software program employed was

Qnet2000. The procedure used for the

development of our ANN model is

outlined below:

(a) An iterative solution scheme was first

prepared to solve the Colebrook equation

at predefined values of Re and /D. The

parameters used for preparing the input

data file for the iterative solution scheme

included a combination of 74 Re and 28

D parameters resulting in a total of 2072

input data points.


ISSN: 1803-8328


20

(b) Several ANN models were then trained

and tested with the information about each

Re and /D as inputs and the generated

corresponding value of f as the output.

(c) The trained ANN models were then

used to predict the values of f based on

known Re and /D values.

(d) The optimum ANN model which

produces the best results based on some

preset measures was then selected and

validated using a larger dataset.

The values of f in Equation (4) must be

determined either by trial-and-error or after

implementing an implicit solution

procedure. In this study successive

substitution procedure used.

2.2. Training dataset

The data for training the ANN model were

generated using the numerical procedure

described above. A dataset consisting of a

total of 2072 points (74 values of Re

ranging from 2000 to 108 and 28 values of

/D ranging from 10-6

to 0.05) resulting

from the combination of Re and /D as

inputs and f as output was used for training

the ANN model. 30% of total input date

was selected as test data (622 point data).

The optimal ANN configuration was

selected from amongst various ANN

configurations based on their predictive

performance. The two error measures used

to compare the performance of the various

ANN configurations were: determination

coefficient (R2) and root mean square error

(RMS).

3. Results

The ANN configurations employed an

input layer having two neurons, with one

corresponding to each of the input

parameters (Re and /D) in some form, and

an output layer consisting of one neuron

representing the output parameter (f).

Various transfer function was used in all

cases. In order to find the optimal network,

several configurations were tried in which

the number of hidden layers varied from

one to two and the number of neurons

within each hidden layer varied from two

to 10 (Table 1).

Table 1. Prediction errors for the training

and testing dataset of the friction factor

associated with different ANN

configurations without transformations of

the input parameters

Training Test

Transfer function No. of hidden layers No. of neurons/layer RMS R2 RMS R

2

Sigmoid 1 2 0.0384 0.978 0.0422 0.968

Sigmoid 1 3 0.0375 0.978 0.0406 0.974

Sigmoid 1 4 0.0383 0.977 0.0386 0.977

Sigmoid 1 5 0.0379 0.977 0.0396 0.976

Sigmoid 1 6 0.0388 0.977 0.0382 0.976

Sigmoid 1 8 0.0369 0.977 0.038 0.977

Sigmoid 1 10 0.0399 0.976 0.038 0.974

Hyperbolic Tangent 1 5 0.0372 0.978 0.0388 0.976

Gaussian 1 5 0.0404 0.974 0.0374 0.979

Sigmoid 2 2,2 0.0385 0.977 0.0411 0.973

Sigmoid 2 2,3 0.0401 0.974 0.0374 0.979

Based on Table 1, ANN with one hidden

layer has enough accuracy and architecture

2,5,1 (input layer having two neurons, one

hidden layer with 5 neuron; one output

neuron) have minimum RMS and

maximum R2. So architecture 2,5,1 with

Sigmoid transfer function could be

selected in this case. Although the model

with optimum configuration predicted the

friction factor reasonably well for Re and


ISSN: 1803-8328


21

/D values at the upper end of the range of

input data, the overall performance of this

ANN configuration included errors that

could be deemed unacceptable for solving

many problems in closed pipe flow.

As it is clear from both Equation (4), the

parameter f is a logarithmic function of

both input parameters (i.e. Re and /D). For

this reason, a second attempt was made to

improve the performance of the ANN

model by transforming input data

parameters. Thus Re and /D parameters

were transformed using a logarithmic

function to the 10 base. Repetition of the

analysis outlined earlier produced an

optimum ANN configuration which

markedly improved the overall predictions

of the model. The error measures

associated with the different ANN

configurations for this case are presented

in Table 2.

Table 2. Prediction errors for the training

and testing dataset of the friction factor

associated with different ANN

configurations with transformations of the

input parameters

Training Test

Transfer function No. of hidden layers No. of neurons/layer RMS R2 RMS R

2

Sigmoid 1 2 0.0325 0.981 0.0409 0.97

Sigmoid 1 3 0.0262 0.988 0.0266 0.987

Sigmoid 1 4 0.0353 0.989 0.0258 0.988

Sigmoid 1 5 0.0218 0.995 0.0234 0.99

Sigmoid 1 6 0.022 0.992 0.023 0.991

These results demonstrate the importance

of choosing the right transformation of

input data parameters and the significant

impact this may have on the overall

performance of the ANN model. In

addition, Figure 2 shows a plot of f values

as predicted by some empirical equation

and numerical solution of Colebrook-

White equation. It is clear that predicted f

in equations by Moody (1947), Wood

(1966) and Churchill (1977), have low R2

respect the others.


ISSN: 1803-8328


22

Figure 2. Plot of f values as predicted by some empirical equation and numerical solution of

Colebrook-White equation.

Figure 3. shows plot of f values as

predicted by ANN and numerical solution

of Colebrook-White equation. Improved

ANN configuration with log(Re) and

log(/D) used in this figure with 2,5,1

setup. This ANN configuration has R2 and

RMS 0.995 and 0.0218 respectively (Table

2). This ANN model is capable of

predicting the values of f in the Darcy-

Weisbach equation and was in close

agreement with those obtained using the

numerical technique.


ISSN: 1803-8328


23

y = 0.9985x + 4E-05

R2 = 0.9949

0.00

0.02

0.04

0.06

0.08

0.10

0.00 0.02 0.04 0.06 0.08 0.10

Numerical

Sim

ula

ted

by

AN

N

Figure 3. Plot of f values as predicted by ANN and numerical solution of Colebrook-White

equation.

4. Conclusions

From the correlations shown in the

literature, a series of more general

equations has been shown making possible

a very accurate estimation of the friction

factor without trial and error. An optimum

ANN model was developed for calculating

the friction factor in the Darcy-Weisbach

equation as applied to the turbulent flow

regime in closed pipes. The model

involves a neural network with one hidden

layers and 5 neurons in that layer.

Following logarithmic transformations of

the input data parameters, the trained

network was able to predict the response

with R2 and RMS 0.995 and 0.0218

respectively (Table 2). This model allows

for an explicit solution of f without the

need to employ a time-consuming iterative

or trial-and-error solution scheme, an

approach that is usually associated with the

solution of the Colebrook equation in the

turbulent flow regime of closed pipes. For

these reasons, the model is useful for flow

problems that involve repetitive

calculations of the friction factor such as

those encountered in the solution of pipe

network problems as well as the hydraulic

analysis of pressurized irrigation systems.

1) References

2) Barr, D. I. H. (1981), Solutions

of the Colebrook-White

function for resistance to

uniform turbulent flow,

Proceeding Inst. civil

engineers, Part 2, 529-536.

3) Chen, N. H. (1979), An

explicit equation for friction

factor in pipe, Ind. engineer's

Chemical fundamental.

18(3):296.

4) Churchill, S. W., (1973),

Empirical expressions for the

shear stress in turbulent flow

in commercial pipe, AIChE

Journal, 19 (2):375.


ISSN: 1803-8328


24

5) Churchill, S. W., (1977),

Friction factor equations spans

all fluid–flow regimes,

Chemical engineers. 84(24):91.

6) Colebrook, C.F., White, C.M.,

(1937), Experiments with

fluid-friction roughened pipes,

Proc. R. Soc. Ser. A 161 367.

7) Colebrook, C.F., (1939),

Turbulent flow in pipes, with

particular reference to the

transition region between the

smooth and rough pipe laws, J.

Inst. Civil Engrs. (London) 11

133.

8) Gulyani, B.B., (1999), Simple

equations for pipe flow

analysis, Hydrocarbon Process.

(8) 67-78.

9) Landeras G, Ortiz-Barredo A.,

and Javier López, J., (2009)

Forecasting weekly

evapotranspiration with

ARIMA and Artificial Neural

Network models. Journal of

Irrigation and Drainage

Engineering, 135(3): 323-334.

10) Moody, M. L., (1947), An

approximate formula for pipe

friction factors, Trans., ASME,

69:1005.

11) More, Ajinkya A., (2006).

Analytical solutions for the

Colebrook and White equation

and for pressure drop in ideal

gas flow in pipes, Chemical

Engineering Science, 61 5515

– 5519.

12) Nakhaei, M. (2005) Estimating

the Saturated Hydraulic

Conductivity of Granular

Material, Using Artificial

Neural Network, Based on

Grain Size Distribution Curve.

Journal of Sciences, Islamic

Republic of Iran. University of

Tehran. 16(1): 55-62.

13) Ozgur K. (2004) River Flow

Modeling Using Artificial

Neural Networks, Journal of

Hydrologic Engineering,

9(1):60-63.

14) Romeo, E., Royo, C. and

Monzon, A., (2002). Improved

explicit equations for

estimation of the friction factor

in rough and smooth pipes.

Chemical Engineering Journal

86:369–374.

15) Wood, D. J., (1966), An

explicit friction factor

relationship, Civil engineers.

ASCE.

16) Yuhong, Z. and Wenxin H.,

(2009) Application of artificial

neural network to predict the

friction factor of open channel

flow. Commun. Nonlinear Sci.

Numer. Simulat. 14: 2373–

2378.

17) Zahiri A. and Dehghani A. A.,

(2009) Flow Discharge

Determination in Straight

Compound Channels Using

ANNs, World Academy of

Science, Engineering and

Technology 58, 12-15.

18) Zigrang, D. J. & Sylvester, N.

D., (1982), Explicit

approximations to the

Colebrooks friction factor,

AIChE J., 28 (3):514.


ISSN: 1803-8328


25

Impact of Cloud Computing in Developing the Education

Process

Ibrahiem M. M. El Emary, Ph.D

Information Technology Deanship, King Abdulaziz University

Jeddah, Saudi Arabia

E-mail: [email protected]

ABSTRACT At the moment, we see that implied big cloud plays as central to a wide range of

applications. While it may not be interactive in the physical sense, it has a strong

potential for social interaction. One of the main applications benefiting very

effectively from the cloud is e-learning systems that usually require many hardware

and software resources. There are many educational institutions that these investments

cannot, therefore, that cloud computing represents the best solution for them. The

implementation of cloud computing in the e-learning system with the characteristics

and specific approach. Therefore, the main objective of this paper is to address and

discuss how to tap the potential of cloud computing to promote much-needed practice

of cooperation between educators, as well as talk about the positive impact of using

cloud computing for e-learning development solutions architectures.

Key Words: HE, IT, Cloud, Web Browser, Microsoft, Google, Amazon, PDA

and PAU

1. Introduction The concept of computing in the cloud

can be defined as the delivery of IT

services that run in a web browser; the

type of services range from adaptations

of familiar tools such as email and

personal finance to new offerings

such as virtual worlds and social

networks. Storage of digital data is an

important service among these. Cloud

computing is a computing platform

that resides in a service provider’s

large data center and is able to

dynamically provide servers the ability

to address a wide range of needs of

clients. The cloud is a metaphor for the

internet. Some people call it the World

Wide Computer. Technically, it is a

computing paradigm in which tasks are

assigned to a combination of

connections, software and services

accessed over a network. This network

of servers and connections is

collectively known as the cloud.

Physically, the resource may sit on a

bunch of servers at different data

centers or even span across continents.

Actually, it is designed to work like a

whole computer in the cloud and

aimed at a wider audience, including

those who can’t afford their own

computer. Computing at the scale of

the cloud allows users to access

supercomputer-level power. Instead

of operating their own data centers,

firms might rent computing power

and storage capacity from a service

provider, paying only for what they

use, as they do with electricity or

water. This paradigm has also been

referred to as “utility computing,” in

which computing capacity is treated

like any other metered utility service—

one pays only for what one uses.

Users can reach into the cloud for

resources as they need from anywhere

mailto:[email protected]


ISSN: 1803-8328


26

at any time. For this reason, cloud

computing has also been described as

"on-demand computing."

There is a lot of existing technologies

supported by the cloud computing that

represents one of the most talked

subjects in the field of business world

today. For example, most mobile

applications are hosted in the cloud.

So, the cloud is one of the most

effective solutions for data back-up

and storage. While it was difficult to

find examples of cloud computing in

the learning organization just a few

years ago, it is clear that it is firmly in

place and will impact every level of

training in the coming years. Higher

education (HE) landscape around the

world is in a constant state of flux

and evolution, mainly as a result of

significant challenges arising from

efforts in adopting new and

emerging technologies and

pedagogies in their teaching and

learning environments. This is mainly

as a result of a new genre of students

with learning needs vastly different

from their predecessors, and it is

increasingly recognized that using

technology effectively in higher

education is essential to providing

high quality education and preparing

students for the challenges of the

21st century [1].

However, an unresolved challenge

to the effective use of technology

in education is the continued

dominance of traditional didactic

pedagogy despite the critical need for

a paradigm shift from the passive

teacher- centered approach

(transmission of information and

skills) to student-centered

constructivists approaches whereby

students construct knowledge

through interaction and collaboration

with peers as well as teachers. The

bulk of today’s eLearning systems still

consist of simple conversion of

classroom-based content to an

electronic format while still retaining

its traditional distinctive knowledge-

centric nature [1]. Although the new

technologies have the potential to play

an important role in the development

and emergence of new pedagogies,

where control can shift from the

teacher to an increasingly more

autonomous learner, and to rescue the

HE from this appalling situation, the

change is very slow or not forthcoming

at all for various reasons. This is

mainly because both teachers and

learners require a number of

specific skills for technology-

supported constructivist approaches

that is, online tutor skills, and online

learning skills; learners get limited

support to develop such skills from

their teachers who often lack these

same skills themselves. There’s no

doubt learning executives recognize

the advantages of cloud computing;

many have already integrated it into

their learning organizations [1]. The

major advantages of cloud computing

in the field of learning are:-

• Learning content is more

readily available and

accessible in the cloud

since it is an open

system and resides outside a

company’s firewall.

• Content can be managed and

sourced from anywhere within

the cloud and is easily scalable

to meet users’ needs.

• The cloud provides rapid

program implementation and

revision. Content can be

revised and published faster

and easier.

Adopting cloud computing technology

in HE face some challenges as well.

First is integration; how does the cloud

fit into a company’s existing learning

management system? There are


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concerns about privacy and security;

particularly how to protect proprietary

content within the cloud. Others are

concerned with the initial investment.

Will the cloud deliver strategic value

in addition to measurable cost-savings?

1. CLOUD COMPUTING STRUCTURE,

CHARACTERISTICS AND

FACILITIES IN EDUCATION

Frequently the “Cloud Computing”

term has been overloaded and flexed

by the hardware and software vendors

to fit their marketing strategy, resulting

in a general confusion of what truly

cloud computing is. However there are

some common traits that could loosely

define the characteristics of Cloud

Computing [7]: Virtualized which means that at any

given point the consumers are unaware

where the application software or virtual

machine lives. As a matter of fact the

appealing aspect of it is that the users

don't have to be concerned about that

aspect as long as the computing

resources are available to them.

Autonomic and Elastic which

simplifies and reduces the cost of

management. Unlike current systems,

the cloud computing infrastructure can

re-size itself based on the computing

demand. This is accomplished by

adding or removing dynamically

resources such as CPU, memory and

disk.

Multi-tenant or shared facility which

means that there has to be a mechanism

of sharing, separating and securing the

resources of many users who access and

use the system simultaneously.

Service Oriented which means that it's

only offered as a service. Nowadays

there are several distinct group of

services offered separately or in

combination:

− Infrastructure as a Service

(IaaS)

− Platform as a Service (PaaS)

− Software as a Service

(SaaS) Accessible from anywhere regardless of

user's geographical location. To

accomplish this HTTP has been

adopted as a common transport

protocol. To increase security it can be

combined with XML/SOAP. For the

IaaS; common Ethernet-based system

access protocol such as SSH, RDP,

VNC can be used.

Measurable and Billable: There must

an entitlement and control of consumed

resources along with ways to capture

the actual consumption.

Other desired but optional

characteristics are integration with

various private and public cloud

computing infrastructures, which

should result in service mobility and

utilization of combined resources from

many dispersed clouds. The

advantages that come with cloud

computing ( can help resolving some

of the common challenges one might

have while supporting an educational

institution [2, 3, 4]) are listed as

follows:- Cost; One can choose a subscription or

in some cases, pay-as-you- go plan –

whichever works best with that

organization business model.

Flexibility; Infrastructure can be

scaled to maximize investments. Cloud

computing allows dynamic scalability

as demands fluctuate.

Accessibility; This help makes data

and services publicly available without

make vulnerable sensitive information.

Some would resort to a cloud

computing vendor because of the lack

of resources while others have the

resources to build their cloud

computing applications, platforms and

hardware. But either way, components

have to be implemented with the

expectation of optimal performance

when mobile terminals are used [5]

are: - The Client – The End User;

everything ends with the client

(mobile) [see Fig.1].


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Fig. 1 Various components of Cloud

computing [2]

The hardware components, the

application and everything else

developed for cloud computing will be

used in the client. Without the client,

nothing will be possible. The client

could come in two forms: the hardware

component or the combination of

software and hardware components.

Although it’s a common conception

that cloud computing solely relies on

the cloud (internet), there are certain

systems that requires pre-installed

applications to ensure smooth

transition. In this work, all the pre-

installed applications can view by

mobile devices though clouds. The

hardware on the other hand will be the

platform where everything has to be

launched. Optimization is based on

two fronts: the local hardware capacity

and the software security. Through

optimized hardware with security, the

application will launch seamlessly with

mobile devices [5]. Cloud computing

always has a purpose. One of the main

reasons cloud computing become

popular is due to the adoption of

businesses as the easier way to

implement business processes. Cloud

computing is all about processes and

the services launched through mobile

cloud computing always has to deal

with processes with an expected

output.

Regarding the Services that exist in

Cloud Computing, it is divided into the

following:-

Infrastructure as a Service; One can

get on-demand computing and storage

to host, scale, and manage

applications and services. Using

Microsoft data centers means one can

scale with ease and speed to meet the

infrastructure needs of that entire

organization or individual

departments within it, globally or

locally [6].

Platform as a Service; The windows

azure cloud platform as a service

consists of an operating system, a

fully relational database, message-

based service bus, and a claims access

controller providing security-

enhanced connectivity and federated

access for on premise applications. As

a family of on- demand services, the

Windows Azure platforms offers

organization a familiar development

experience, on-demand scalability,

and reduce time to market the

applications.

Software as a Service; Microsoft

hosts online services that provide

faculty, staff, and students with a

consistent experience across multiple

devices. Microsoft Live at edu

provides students, staff, faculty, and

alumni long-term, primary e-mail

addresses and other applications that

they can use to collaborate and

communicate online— all at no cost

to the education institution. Exchange

Hosted Services offers online tools to

help organizations protect themselves

from spam and malware, satisfy

retention requirements for e-discovery

and compliance, encrypt data to

preserve confidentiality, and maintain

access to e-mail during and after

emergency situations. Microsoft

Dynamics CRM Online provides

management solutions deployed

through Microsoft Office Outlook or

an Internet browser to help customers

efficiently automate workflows and

centralize information. Office Web

Apps provide on-demand access to

the Web-based version of the

Microsoft Office suite of applications,

including Office Word, Office Excel,

and Office PowerPoint.’

With respect to the Cloud computing

usage; we say that the cloud plays the

main role in the business role and also

it is the only elastic data center which


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wrapped around various new

technologies into it. The technology is

most probably used in the business

oriented scenario than the service

motivated organization as per the

survey did by us. According to the

Survey made during the month of

October 2010 based on the

questionnaire prepared by us it was

found that a major part of the survey

group knew about cloud computing,

69% knew that cloud is used in

business, 12% knew it is used in

education, 88% agree to implement the

cloud for education sector, 94%

believes that the cloud technology can

reduce the cost of high quality

education system and most of them are

unaware that the cloud is also offered

at low cost.

The requirements for Cloud can be

stated as follows: In the previous

generation of the information

technology the data sharing which led

the path for the knowledge sharing was

not used by the users globally, in this

generation the various streams have the

knowledge of e-Learning and the

Mobile based learning. In this present

context the usage of the central data

center is an easy process for the

education system however the cost of

implementation and the maintenance

of the data storage space and also the

load capability also software licensing

depends on the real time usage of these

systems. Business streams can make

revenue out of those expenses whereas

for educational institutions which

really want to motivate the learners

and want to offer a quality education at

affordable cost can achieve this by

spending a large amount. This can be

overcome by the present cloud

computing technology that is "Pay as

Use" (PAU).

As shown above, we can summarize as

follows: Cloud-based services can be

categorized into three models: (i)

Software as a Service (SaaS), (ii)

Infrastructure as a Service (IaaS), and

(iii) Platform as a Service (PaaS). In a

SaaS infrastructure, service providers

make available applications for

personal and business use such as MS

Exchange and Quick books. IaaS on

the other hand, offers hardware

services which may include virtual and

physical servers. And lastly, PaaS

provides a framework and tools for

developers to build their own

applications. Online content

management systems and website

building services are examples of this

infrastructure. Cloud computing offers

several technical and economic

benefits. In terms of technical

advantage, it is possible to use the

processing power of the cloud to do

things that traditional productivity

applications cannot do. For instance,

users can instantly search over GBs of

e-mail online, which is practically

impossible to do on a desktop. One of

the greatest advantages is that the user

is no longer tied to a traditional

computer to use an application, or has

to buy a version specifically

configured for a phone, PDA or other

device. Any device that can access the

Internet will be able to run a cloud-

based application. Regardless of the

device being used, there may be fewer

maintenance issues. Users will not

have to worry about storage capacity,

compatibility or other matters. Cloud

computing infrastructure allows

enterprises to achieve more efficient

use of their IT hardware and software

investments: it increases profitability

by improving resource utilization.

Pooling resources into large clouds

cuts costs and increases utilization by

delivering resources only for as long as

those resources are needed.


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2. MAJOR CAPABILITIES AND

LIMITATIONS OF CLOUD

COMPUTING IN E- LEARNING

The cloud computing term was derived

from the way the Internet is often

represented in network diagrams. Due

to the fact it involves the existence of

data centers that are able to provide

services; the cloud can be seen as a

unique access point for all the requests

coming from the world wide spread

clients (see fig.2). Cloud computing

comprises of three layers [5]: Infrastructure as a service (IaaS)

Platform as a service (PaaS)

Software as a service (SaaS)

Depending on the requirements, the

customers can choose one or more

services provided. Hardware devices

(such as regular PCs, notebooks,

mobile phones, PDAs or any other

similar equipment’s) or software

applications (like web browsers, for

example Google Chrome) can

successfully play the role of a cloud

client (see figure 2). The customers are

renting or simply accessing the needed

processing capacity from the data

center using the above mentioned

client applications. The quality of the

service becomes a crucial factor of the

cloud computing success.

Fig. 2 Cloud computing

Cloud computing is by no means

different from grid computing. The

later tries to create a virtual

processor by joining together a cluster

of computers. The aim of a grid

computing architecture is to solve large

tasks by using the advantage of

concurrency and parallelism, while the

cloud is focused on collaboration.

Fig. 3 Cloud computing clients

Cloud computing becomes very

popular because it moves the

processing efforts from the local

devices to the data center facilities.

Therefore, any device, like an Internet

connected phone, could be able to

solve complex equations by simply

passing the specific arguments to a

service running at the data center level

that will be capable to give back the

results in a very short time. In these

conditions, the security of data and

applications becomes a very major

issue. Cloud computing is widely

accepted today due to its key

capabilities:-

• The cost is low or even free in

some cases. Also, there are no

costs (or very small ones) for

hardware upgrades;

• For some applications (like

spreadsheets) it can be used even

in the offline mode, so when the

client goes back online a

synchronization process is

refreshing the data;

• The strong connection that exists

today between the users and their


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personal computers can be

completely broken because a

customer can reach the same

result by using any Internet

connected device having

minimum software requirements;

• Devices with minimal hardware

requirements (mobile phones, for

example) could be successfully

used as cloud clients;

• In order to become part of the

cloud, there is no need to

download or install specific

software, only the Internet

connection is required;

• The cost of licensing different

software packages is moved to

the data center level, so there is

no need to upgrade the local

system when new service packs

or patches are released;

• Crash recovery is nearly

unneeded. If the client computer

crashes, there are almost no data

lost because everything is stored

into the cloud.

There are some of the cloud computing

limitations and weak points mentioned

as follows:- The Internet connection speed may

affect the overall performances;

On a long term basis, the data center

subscription fee may be more expensive

than using the hardware;

The service quality is crucial and the

need of the backups is critical when

speaking about data security.

The major players in the field of cloud

computing are Google, Microsoft,

Amazon, Yahoo and some legacy

hardware vendors like IBM and Intel.

Cloud Computing applications are

mainly intended to help companies and

individuals to stretch resources and

work smarter by moving everything to

the cloud. One of the biggest

promoters of the cloud computing is

Google that already owns a massive

computer infrastructure (the cloud)

where millions of people are

connecting to. Today, the Google

cloud can be accessed by Google Apps

[6] intended to be software as a service

suite dedicated to information sharing

and security. Google Apps covers the

following three main areas: messaging

(Gmail, Calendar and Google Talk),

collaboration (Google Docs, Video and

Sites) and security (email security,

encryption and archiving). Microsoft is

developing a new Windows platform,

called Windows Azure, which will be

able to run cloud based applications

[7]. In 2006, Amazon extended its

AWS (Amazon Web Services) suite

with a new component called Amazon

Elastic Compute Cloud (or EC2), that

allows to the users to rent from

Amazon processing power to be used

to run their own applications. The EC2

users rent out from Amazon virtual

machines that can be accessed

remotely. The cloud is an elastic one

just because the user can start, stop and

create the virtual machines through the

web service. There are three predefines

sizes for the virtual machines that can

be rented: small, medium and large,

depending on the physical hardware

performances.

3. E- LEARNING SOLUTIONS

THROUGH CLOUD COMPUTING

Many education institutions do not

have the resources and infrastructure

needed to run top e- learning solution.

This is why Blackboard and Moodle,

the biggest players in the field of e-

learning software, have now versions

of the base applications that are cloud

oriented. E-learning is widely used

today on different educational levels:

continuous education, company

trainings, academic courses, etc. There

are various e-learning solutions from

open source to commercial. There are

at least two entities involved in an e-

learning system: the students and the


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trainers. The students' actions within

an e-learning platform are:

• Taking online course

• Taking exams

• Sending feedback

• Sending homework, projects.

The trainers involved in e-learning

solutions are: -

• Dealing with content

management

• Preparing tests

• Assessing tests, homework,

projects taken by students

• Sending feedback

• Communicating with students

(forums).

Each of these actions requires a certain

degree of security, depending on the

importance and data sensitivity.

Fig. 4 E-learning system

Usually, e-learning systems are

developed as distributed applications,

but this is not necessary so. The

architecture of a distributed e-learning

system includes software components,

like the client application, an

application server and a database

server (see figure 4) and the necessary

hardware components (client

computer, communication

infrastructure and servers). The client

hardware could be a mobile device or a

desktop computer. The client

application can be a simple web

browser or a dedicated application.

Even with the current hardware and

software limitations, mobile devices

are supporting multimedia based

applications. Compared with desktop

applications, nowadays mobile

applications, especially multimedia-

based applications, have serious

limitations due the processing power

and memory constraints. Due the fact

that the data processing is on the server

side, the use of mobile devices for

learning is growing fast. Still, the

mobile applications need to be

optimized to be used for e- learning.

The e-learning server will use cloud

computing, so all the required

resources will be adjusted as needed.

E-learning systems can use benefit

from cloud computing using:

• Infrastructure: use an e-learning

solution on the provider's

infrastructure

• Platform: use and develop an e-

learning solution based on the

provider's development interface

• Services: use the e-learning

solution given by the provider.

A very big concern is related to the

data security because both the software

and the data are located on remote

servers that can crash or disappear

without any additional warnings. Even

if it seems not very reasonable, the

cloud computing provides some major

security benefits for individuals and

companies that are using/developing e-

learning solutions, like the following:

• Improved improbability; it is

almost impossible for any

interested person (thief) to

determine where is located the

machine that stores some wanted

data (tests, exam questions,

results) or to find out which is

the physical component he needs

to steal in order to get a digital

asset;

• Virtualization; makes possible

the rapid replacement of a

compromised cloud located

server without major costs or


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damages. It is very easy to create

a clone of a virtual machine so

the cloud downtime is expected

to be reduced substantially;

• Centralized data storage;

losing a cloud client is no longer

a major incident while the main

part of the applications and data

is stored into the cloud; so a new

client can be connected very

fast. Imagine what is happening

today if a laptop that stores the

examination questions is stolen;

• Monitoring of data access

becomes easier in view of the

fact that only one place should be

supervised, not thousands of

computers belonging to a

university, for example. Also, the

security changes can be easily

tested and implemented since the

cloud represents a unique entry

point for all the clients.

Another important benefit is related to

costs. If the e-learning services are

used for a relative short time (several

weeks, a quarter, a semester), the

savings are very important.

5. SUMMERY AND CONCLUSION

In the current decade, adopting cloud

computing for e-learning solutions

influences the way the e-learning

software projects are managed. There

are specific tasks that deal with finding

providers for cloud computing,

depending on the requirements

(infrastructure, platform or services).

Also, the cost and risk management

influences the way the e-learning

solutions based on cloud computing

are managed. So, cloud computing

play a significant scope to change the

whole education system. Cloud based

education will help the students, staff,

Trainers, Institutions and also the

learners to a very high extent and

mainly students from rural parts of the

world will get an opportunity to get the

knowledge shared by the professor on

other part of the world.

REFERENCES

[1] Teo, C. B., Chang, S. C. A., &

Leng, R. G. K (2006). Pedagogy

Considerations for E-learning.

Retrieved 10 Oct 2008

[2] N.Mallikharjuna Rao et al , “Cloud

Computing Through Mobile-

Learning”, International Journal of

Advanced Computer Science and

Applications, Vol.1, No. 6, December

2010

[3] Uhlig,R., Neiger, G. Rodgers, D.

S.M. Kagi, A.Leung, F.H. Smith :

Intel Corp., USA : Intel visualization

technology IEEE Computer Society :

May 2005.

[4] Perez, R., van Doom, L., Sailer, R.

IBM T.J. Watson Res. Center,

Yorktown Heights, NY: Visualization

and Hardware-Based Security -October

2008.

[5] Independent Cloud Computing

and Security

http://cloudsecurity.org/forum/stats/-

Augast 2010.

[6] GregorPetri: The Data Center is

dead; long live the Virtual Data

Center? Join the Efficient Data Center

Nov 2010.

[7] SMI Report Oskar Pienkos June

2011.


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