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Hierarchical Trust Model to Rate Cloud Service Providers based on

Infrastructure as a Service

Supriya M1, Sangeeta K1, G K Patra2

1Department of CSE, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India.

2CSIR Fourth Paradigm Institute (Formerly CSIR C-MMACS), Bengaluru, India

{m_supriya,k_sangeeta}@blr.amrita.edu, [email protected]

Abstract

In large scale distributed systems like cloud

computing, customers need to interact with unknown service providers to carry out tasks or transactions.

The ability to reason about and assess the possible

risks in carrying out such transactions is necessary

for providing a safe and trustworthy environment.

Cooperative characteristics of distributed computing

systems enforce a proper and secure trust

management to be in place to minimize the risks

posed by different malicious agents. Trust is the

estimation of competency of a resource provider in

completing a task based on dependability, security,

ability and availability in the context of distributed

environment. It enables users to select the best

resources in the heterogeneous cloud infrastructure.

In this paper, a hierarchical trust model has been

proposed to manage the trust and to rate the service

providers and their various plans based on IaaS in

cloud computing environment.

1. Introduction Cloud model is the latest advancement in the large

distributed system category. Cloud computing is a

pervasive paradigm, where large pools of systems are

connected in private or public networks, to provide

dynamically scalable infrastructure for application, data and file storage [1]. It refers to the underlying

infrastructure that provides services to customers via

defined interfaces. Services are provided “on

demand” basis to cloud users over high-speed internet

within the “X as a service (XaaS)” computing

framework where X represents “Infrastructure”,

“Platform”, “Software”, “Database” etc. Among the

various service models available in cloud,

Infrastructure as a Service (IaaS) plays a vital role.

IaaS is the delivery of computing resources as a

service through APIs, which includes virtual

machines, operating systems and other abstracted

hardware [2]. The customer rents these resources

which are dynamically scalable as per usage, rather

than buying and installing them. Examples for IaaS

include Amazon EC2 and S3 service providers.

Due to the large scale and openness of these systems,

a customer is often required to interact with service

providers with whom he has few or no shared past

interactions. To assess the risk of such interactions and to determine whether an unknown service

provider is trustworthy, an efficient trust mechanism

is necessary. Trust is an important ingredient

facilitating reliable interactions among autonomous

participants in diverse large-scale systems including

e-commerce, distributed and peer-to-peer systems,

multi-agent systems and dynamic collaborative

systems [3].

Firdhous et. al. [4] have provided a comprehensive

survey on the trust management systems implemented

on distributed systems with a special emphasis on

cloud computing. The critical security challenges like

data service outsourcing security and computation

outsourcing security are outlined in [5], with

emphasis on the need to address access control and

multitenancy issues for a trustworthy public cloud

environment. A formal trust management model for Software as a Service (SaaS) based on the basics of

the trust characteristics is presented in [6]. This model

is capable to handle various cloud services access

scenarios where an entity may or may not have a past

experience with the service. Xin [7] proposes the use

of stereotypes to assess trustworthiness of the target

agent whose past behaviour information is not locally

available to a trustor, which is very common in large

scale, open distributed systems. The problem of trust

evaluation has not been done practically. In recent

years, fuzzy logic has been used in several decision

support systems, to represent uncertainties, especially

when they need to be handled quantitatively. It offers

the ability to handle uncertainty and imprecision

effectively, and is therefore ideally suited to reasoning

about trust. The fuzzy operations and rules can be

used in the formal decision-making process to handle

uncertainty in trust management.

Supriya M et al, Int.J.Computer Technology & Applications,Vol 5 (3),1102-1111

IJCTA | May-June 2014 Available [email protected]

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A model to estimate the trust value of the cloud

service providers using fuzzy logic is described in [8].

This model is used in [9] to compare the cloud service

providers and their various plans based on the direct

and recommended trust considering Agility, Finance

and Performance as parameters. In this model,

Security was not considered as a parameter which is

an important requirement for the users of the cloud

since when a company outsources its confidential data to another company or a cloud; it needs assurance that

the service provider has used “reasonable security” to

protect those data. In this paper, we propose a

hierarchical model which extends the model described

in [9] and rates the cloud service providers and their

plans based on 5 parameters: Agility, Finance,

Performance, Security and Usability. In this model

the user has the option to give priority to

Security/Finance as compared to other parameters and

rate the service providers based on their level of

security and cost.

The rest of the paper is organized as follows:

Section 2 describes the SMI Framework and the

proposed hierarchical model is described in Section 3.

Cloud Service Providers (CSPs), their plans and

infrastructure details are described in Section 4.

Section 5 explains the simulation and results of Cloud Analyst. The rating of the service providers using the

hierarchical model is arrived at in Section 6. The

paper is concluded in Section 7.

2. SMI Framework The Service Measurement Index (SMI) is a set of

business-relevant Key Performance Indicators (KPI's)

that provide a standardized method for measuring and

comparing a business service [10]. This method is

used by the organizations to measure cloud-based

business services based on their specific business and

technology requirements. The Service Measurement

Index is currently being developed by the Cloud

Services Measurement Initiative Consortium

(CSMIC). The SMI framework describes

Accountability, Agility, Assurance, Financial,

Security/Privacy, Performance and Usability as the

seven KPI’s. These KPI’s have various attributes that

help to measure and compare the business services.

3. Hierarchical Model Description The trust evaluation model described in [8] uses

Agility, Financial and Performance KPIs described by

CSMIC and rates the CSPs using fuzzy logic toolbox of Matlab [11]. This model implementation comprises

of 2 stages. The first stage is the implementation with

the help of Mamdani Fuzzy Inference System which

evaluates Performance, Financial and Agility

parameters. The Performance parameter is evaluated

by considering the number of processors and the

RAM capacity available with the CSP. Financial

parameter is evaluated using Virtual Machine (V.M)

Cost, Storage Cost, and Data Transfer Cost. Agility

parameter takes number of Data Centers (DCs),

Storage space and number of V.Ms as its inputs. The

second stage implementation takes the output of the

first stage FIS and helps to obtain the trust rating for

each plan of the CSP. The trust values obtained from

the above is considered as the Direct Trust (i.e) only

by the observation of the infrastructure facilities

available with the CSP. These infrastructure facilities

are simulated using Cloud Analyst [12] which provides the DC processing time and Total cost as the

output. These outputs are fed to the Performance

parameter and Finance parameter respectively (in

addition to the above mentioned inputs) and the FIS is

re-run to get the Recommended Trust value between 0

and 1. The model block diagram (Direct Trust) is

shown in Figure 1.

Figure 1 Model Block Diagram

No. of Processors

Processor Speed

(RAM)

V.M Cost

Storage Cost

Data Transfer

Cost

No. of DCs

Storage Space

No. of V.Ms

Performance Fuzzy

Inference System

Financial

Fuzzy Inference

System

Agility

Fuzzy Inference System

Performance

Financial

Agility

Trust

Fuzzy Inference

System

Degree of

Trust



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The model discussed above rates the CSPs and

their plans with no emphasis given to security. But the

data security raises a number of concerns, including

the risk of loss, unauthorized collection and usage, if

the CSP does not provide adequate data protection

[2]. In other words, security is a major concern for all

consumers. So the model of Figure 1 has now been

extended to include two more parameters - Security

and Usability. The Security parameter is described in terms of the Physical Security, Internal Security and

Network Security levels available with the cloud

provider, while the Usability parameter of the model

is calculated based on the contributions from the

Understandability, Easability and Flexibility

attributes. Contribution of various attributes towards

the model parameters is listed Table 1.

Once again a two stage Fuzzy Inference System

(FIS) is used for estimation of trust value

corresponding to different plans of the CSP for the

model shown in Figure 2. But in this model, the

second stage of the FIS alone takes 55 = 3125 rules,

i.e the number of inputs to the FIS to the power

number of membership values (very low, low,

medium, high and very high). If we desire to extend

this model further with one additional parameter it

would take 56 = 15625 rules. Hence the complexity of

the system increases exponentially, if we desire to

compute the trust values based on all parameters of

the SMI as mentioned in section 2. This necessitates

the development of hierarchical model where the

input parameters can be chosen by the consumer as

per his requirements and priority to bring down the number of rules for rating the service providers.

TABLE 1

Model Parameters

KPI Parameter Contributing Attributes

Agility No. of Physical Units (DCs),

No. of V.Ms, Memory Size

Finance V.M Cost, Storage Cost, Data

Transfer Cost

Performance No. of Processors, Processor

Speed (RAM)

Security Physical Security, Internal

Security, Network Security

Usability Understandability, Easability,

Flexibility

Figure 2 Proposed Model Block Diagram

In this work a hierarchical model to rate the CSPs

has been designed giving priority to Finance/Security

as input parameters to the trust model as shown in

Figures 3 and 4 respectively. These models provide

the trust value for the CSP based on Direct Trust

rating. The DC Processing time and Total cost

obtained from the Cloud Analyst simulation are then

added to the Performance and Financial FIS

respectively to obtain the Recommended Trust rating.

The user or customer of the cloud may need to

transact with the service provider and the task he

needs to complete may be a highly confidential one.

He may not bother about the Financial aspect. His

concern would be only on the Security aspect. For,

such scenario the user may prefer the model shown in

Figure 4 whereas if his concern is mainly on the

Finance rather than Security he may choose the model

shown in Figure 3.

No. of Processors

Processor Speed

(RAM)

V.M Cost

Storage Cost

Data Transfer Cost

No. of DCs

Storage Space

No. of V.Ms

Performance

Fuzzy Inference

System

Financial

Fuzzy Inference

System

Agility

Fuzzy Inference

System

Performance

Financial

Agility

Trust

Fuzzy

Inference

System

Degree of

Trust

Physical Security

Internal Security

Network Security

Understandability

Easability

Flexibility

Security

Fuzzy Inference

System

Usability

Fuzzy Inference

System

Security

Usability



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Figure 3 Hierarchical Model based on Finance

Figure 4 Hierarchical Model based on Security

No. of DCs

Storage Space

No. of V.Ms

Physical Security

Internal Security

Network Security

No. of Processors

Processor Speed

(RAM)

Agility

Fuzzy Inference

System

Security

Fuzzy Inference

System

Performance Fuzzy

Inference System

Agility

Security

Performance

Fuzzy

Inference

System

V.M Cost

Storage Cost

Data Transfer Cost

Understandability

Easability

Flexibility

Financial

Fuzzy Inference

System

Usability

Fuzzy Inference

System

Financial

Usability

Fuzzy

Inference

System

Trust Fuzzy

Inference

System

Degree

of Trust

No. of DCs

Storage Space

No. of V.Ms

V.M Cost

Storage Cost

Data Transfer Cost

No. of Processors

Processor Speed

(RAM)

Agility

Fuzzy Inference

System

Financial

Fuzzy Inference

System

Performance Fuzzy

Inference System

Agility

Financial

Performance

Fuzzy

Inference

System

Physical Security

Internal Security

Network Security

Understandability

Easability

Flexibility

Security

Fuzzy Inference System

Usability

Fuzzy Inference

System

Security

Usability

Fuzzy

Inference

System

Trust Fuzzy

Inference

System

Degree

of Trust



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The hierarchical models shown in Figures 3 and 4

have three stages and works as follows: If Finance

parameter is more important then as shown in Figure

3 the Finance and Agility FIS gets evaluated

separately and the Performance, Security and

Usability FIS gets evaluated separately and finally the

trust value of the CSP plan is obtained. Likewise, if

Security has a higher priority then as shown in Figure

4 Security and Agility FIS gets evaluated as one set and Performance, Financial and Usability FIS gets

evaluated as another set to obtain the trust value

corresponding to a CSP plan. When compared with

the model shown in Figure 2 which requires 3125

rules in the second stage, the hierarchical model

requires only 175 rules including both second and

third stages.

4. CSPs and their Plans The various plans provided by the following

service providers: GoGrid, Rackspace, Amazon EC2

and Cloudflare have been rated using the hierarchical

trust model. Table 2 and Table 3 show the different plans, Data Center (DC) location across the globe and

the input parameters corresponding to each service

provider drawn from the published information [13,

14, 15, 16]. However, Security and Usability are

customer dependent and not exactly quantifiable. So,

to test the model a value between 0 and 1 is assigned

based on the available survey data. The information

of Table 2 and 3 has been used to set up the DCs

available with each CSP during the simulation.

TABLE 2

CSPs and their location

CSP Name of the Plans DCs and their Location

GoGrid 4 plans: standard,

advanced, ultra, elite

3 DCs : two in U.S.A,

one in Europe

Rackspace

4 plans: Enhanced

one, Enhanced two, Performance one,

Performance Two

8 DCs: Five in U.S.A,

two in Europe, one in

Asia

Amazon

EC2

3 Plans: Amazon EC2

Small, Amazon EC2

Medium, Amazon EC2 Large

6 DCs: three in U.S.A, one in Europe, two in

Asia

Cloudflare

3 Plans: Cloudflare Pro, Cloudflare

Business, Cloudflare

Enterprise

4 DCs: one in U.S.A,

one in South America, two in Asia

TABLE 3

Various Plans of CSPs with their Parameter Details

CS

P a

nd

Ser

ver

typ

e

Agility Financial Performance Security Usability

No

of

V.M

No

of

DC

Sto

rag

e

Sp

ace

in

T.B

V.M

Co

st/h

r($

)

Sto

rag

e

Co

st /

GB

($)

T

ran

sfer

Co

st /

GB

($)

No

. o

f

Pro

cess

ors

RA

M

In G

B

Ph

ysi

cal

Sec

uri

ty

Inte

rnal

Sec

uri

ty

Net

wo

rk

Sec

uri

ty

Un

der

stan

d

abil

ity

Eas

abil

ity

Fle

xib

ilit

y

Gogrid

Standard Dedicated

Server

4 3 0.642 0.4166 0.15 0.29 4 8 0.9 0.87 0.82 0.88 0.9 0.8

Gogrid

Advanced

Dedicated Server

8 3 1 0.5553 0.15 0.29 8 12 0.84 0.89 0.86 0.9 0.8 0.85

Gogrid Ultra

Dedicated

Server

8 3 0.735 0.8333 0.15 0.29 8 24 0.82 0.78 0.9 0.87 0.85 0.87

Gogrid

Elite Dedicated

Server

12 3 0.934 1.666 0.15 0.29 12 48 0.75 0.8 0.9 0.95 0.9 0.9

Rackspace

Enhanced

One

2 8 0.219 1.068 0.1 0.18 2 8 0.85 0.9 0.82 0.9 0.85 0.9

Rackspace

Enhanced Two

4 8 0.292 1.525 0.1 0.18 4 12 0.87 0.9 0.85 0.85 0.9 0.87



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Rackspace

Performanc

e One

6 8 0.6 1.694 0.1 0.18 6 32 0.79 0.85 0.9 0.84 0.87 0.9

Rackspace

Performanc

e Two

12 8 0.292 2.083 0.1 0.18 12 32 0.8 0.85 0.87 0.87 0.85 0.9

Amazon EC2 Small

2 6 0.16 0.06 0.15 0.20 2 1.7 0.87 0.79 0.9 0.85 0.9 0.84

Amazon EC2

Medium

3 6 0.41 0.12 0.15 0.20 3 3.75 0.9 0.85 0.84 0.89 0.87 0.88

Amazon

EC2 Large 4 6 0.85 0.16 0.15 0.20 4 7.5 0.87 0.85 0.85 0.9 0.85 0.87

Cloud flare

Pro 2 4 0.5 1.76 0.17 0.25 2 8 0.8 0.75 0.85 0.7 0.78 0.8

Cloud flare

Business 6 4 0.65 1.84 0.17 0.25 6 15 0.85 0.8 0.87 0.8 0.85 0.87

Cloud flare Enterprise

8 4 0.8 1.92 0.17 0.25 8 20 0.8 0.85 0.84 0.85 0.87 0.88

5. Cloud Analyst Simulation and Results Cloud Analyst simulation of CSPs mentioned in

Section 4 needs the User Bases (UB) to be defined

randomly across the regions in the globe as described

in [8] and [9]. The regions considered are the six

continents labelled R0 through R5 as listed in Table 4.

This UB description is kept constant throughout the

simulation to analyze the performance of different

CSPs under the same load.

TABLE 4

UserBase Description

Name

Region

Requests

per

User

per Hr

Data

Size per

Request

(bytes)

Peak

Hrs.

(GMT)

Peak

Hrs

End

(GMT)

Avg Peak

Users

Avg

Off-

Peak

Users

UB1 0 60 100 3 9 1000 100

UB2 2 60 100 3 9 1000 100

UB3 5 1000 100 9 21 1000 100

UB4 4 150 100 0 9 1000 100

UB5 3 610 100 3 18 1000 100

UB6 1 600 100 3 18 1000 100

UB7 2 300 100 6 14 1000 100

A sample Cloud Analyst simulation setup and the

results after simulation of Amazon EC2 Medium Plan

are shown in Figures 5 and 6 respectively.

Figure 5 Amazon EC2 Medium Simulation setup

Figure 5 shows the Data Centers represented as

DC numbered 1 through 18 (6 DCs each offering 3

plans, three in USA, one in Europe, two in Asia as

mentioned in Table 2) and the User Bases numbered 1 through 7 located across the globe (as mentioned in

Table 4). The infrastructure details of Table 3 and the

UB requests of Table 4 are loaded in the

configuration window of Cloud Analyst. The

simulation run corresponding to each CSP plan

provides the average response time, DC processing

time and total cost involved in the transaction. A

snapshot from Cloud Analyst simulation showing the

maximum and minimum response times against each

of the User Bases for the Amazon EC2 Medium plan

is shown in Figure 6. Since this response time is

random for every simulation, it has not been

considered in the evaluation of Recommended Trust.

Table 5 lists the DC processing time and total cost

obtained from the Cloud Analyst simulation for each

CSP plan which has been used to obtain the

Recommended Trust.



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Figure 6 Amazon EC2 Medium after Simulation

TABLE 5

Cloud Analyst Simulation Results

CSP and Server

type

DC Processing

Time (ms)

Total Cost

($)

Gogrid Standard

Dedicated Server 0.86 32.54

Gogrid Advanced Dedicated Server

0.96 65.84

Gogrid Ultra


Gogrid Elite


Rackspace Enhanced

One 3.25 76.14

Rackspace Enhanced

Two 0.86 203

Rackspace Performance One

0.91 333.07

Rackspace

Performance Two 1.06 807.77

Amazon EC2 Small 0.99 10.8

Amazon EC2

Medium 0.86 15.12

Amazon EC2 Large 0.86 20.16

Cloud flare Pro 16.01 53.04

Cloud flare Business 0.92 143.30

Cloud flare

Enterprise 0.97 195.15

6. Results and Discussion

6.1 Direct and Recommended Trust with 3

and 5 model parameters The non hierarchical models shown in Figures 1

and 2 have been modelled in simulink which in turn

calls the FIS created for each parameter. Execution of

the simulink model gives a set of Direct and

Recommended Trust values for each plan of CSP as

listed in Table 6. It is observed that the

Recommended Trust values are higher than the Direct Trust values as these include the recommendations or

references collected from other parties in the initial

trust.

Addition of Security and Usability parameters to

the model of Figure 1 reduces the variations in the

trust values for all CSPs which are as expected. Also,

the plans that provide high security are clearly

differentiated from the other CSPs. For example, with

three parameters Gogrid Elite Dedicated Server and

Amazon EC2 Large plans are rated highest in

Recommended Trust (with trust value of 0.794), but

their trust values go down with five parameters,

which is because of the level of security provided by

these CSP plans.

6.2 Direct and Recommended Trust based on

Finance/Security Table 7 shows the trust values of the CSPs

corresponding to the models in Figures 3 and 4. Here

too the Recommended Trust values are higher than



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the Direct Trust values except for Rackspace

Performance Two (highlighted in the Tables 6 and 7)

due to higher total processing cost (in $) for the user

requests (as shown in Table 5, this plan takes the

highest $807.77). This is also reflected as a

considerable reduction in Recommended Trust value

for the Finance based model.

Another important observation is that the priority

based model is better in distinguishing between various plans. This can be seen from Figure 7, which

shows comparison of the recommended trust values

corresponding to various plans from different models.

In the non-hierarchical model, where all parameters

have equal weights, trust values of all the plans (see

column 2 of Table 6) fall in the range between 0.499

and 0.52 (excluding 0.38) which makes it difficult to

rank the CSPs. But in a priority based model with

Finance / Security (Table 7), we can see that the range

varies from 0.471 to 0.755. Thus we can rank the

various service provider plans.

Thus it is seen that with Finance as the main

requirement, Gogrid Elite Dedicated Server,

Rackspace Performance One, Amazon EC2 (Small,

Medium, Large) and Cloud flare Enterprise plans are favourable whereas with Security as the main

requirement Rackspace Performance One and

Amazon EC2 Large would be preferable. Such a

conclusion cannot be arrived at from Table 6. It may

be noted that all the above results are subject to

variation depending on the network load conditions.

TABLE 6

Trust Values for 3 and 5 Model Parameters

CSP and Server type

Three Parameters (Agility, Financial and Performance)

Five Parameters

(Agility, Financial, Performance,

Security and Usability)

Direct Trust Recommended Trust Direct Trust Recommended Trust

Gogrid Standard Dedicated Server 0.496 0.793 0.465 0.5

Gogrid Advanced Dedicated Server 0.629 0.695 0.5 0.5

Gogrid Ultra Dedicated Server 0.57 0.681 0.499 0.499

Gogrid Elite Dedicated Server 0.794 0.794 0.489 0.5

Rackspace Enhanced One 0.567 0.614 0.497 0.5

Rackspace Enhanced Two 0.567 0.627 0.5 0.5

Rackspace Performance One 0.763 0.793 0.501 0.52

Rackspace Performance Two 0.793 0.765 0.5 0.5

Amazon EC2 Small 0.422 0.568 0.374 0.5

Amazon EC2 Medium 0.501 0.694 0.374 0.5

Amazon EC2 Large 0.568 0.794 0.499 0.499

Cloud flare Pro 0.418 0.567 0.315 0.38

Cloud flare Business 0.567 0.682 0.429 0.5

Cloud flare Enterprise 0.625 0.794 0.5 0.5

Although the results have been described prioritizing

Finance/Security we would like to emphasize that the

model utilizes most of the parameters listed by

CSMIC to arrive at a trust value, and hence can be

customized to get a trust value for a service provider by selecting parameters as per user requirement. For

eg: a consumer who has security as a priority may not

have focus on the Agility. The model represented in

Figures 3 and 4 can be modified by removing the

respective parameter and can then be evaluated using

the FIS to get the trust value of the service provider.

For eg: the trust value obtained for Rackspace

Enhanced One considering Security based

Recommended Trust as shown in Figure 4 is 0.605

(highlighted entry in Table 7), which increases to 0.678 when we remove the Finance parameter while

evaluating the trust value. Thus, the model parameters

can be relaxed too as required by the user and the

trust value of the CSPs can be estimated.



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TABLE 7

Trust values for Hierarchical Models (Finance and Security based)

CSP and Server type Finance based Security based

Direct Trust Recommended Trust Direct Trust Recommended Trust

Gogrid Standard Dedicated Server 0.585 0.673 0.589 0.606

Gogrid Advanced Dedicated Server 0.585 0.665 0.585 0.607

Gogrid Ultra Dedicated Server 0.591 0.64 0.585 0.661

Gogrid Elite Dedicated Server 0.697 0.75 0.679 0.679

Rackspace Enhanced One 0.622 0.624 0.578 0.605

Rackspace Enhanced Two 0.585 0.624 0.585 0.585

Rackspace Performance One 0.75 0.755 0.67 0.755

Rackspace Performance Two 0.755 0.578 0.755 0.67

Amazon EC2 Small 0.5 0.725 0.471 0.578

Amazon EC2 Medium 0.586 0.736 0.586 0.619

Amazon EC2 Large 0.65 0.736 0.65 0.755

Cloud flare Pro 0.498 0.557 0.498 0.578

Cloud flare Business 0.623 0.649 0.64 0.649

Cloud flare Enterprise 0.623 0.75 0.67 0.67

Figure 7 Comparison of Recommended Trust Values



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7. Conclusion This paper proposes a hierarchical model to rate

the various plans of CSPs considering Agility,

Financial, Performance, Security and Usability

parameters listed by CSMIC which provide a

standardized method for measuring and comparing

business services. Considering Finance as priority

requirement results are obtained to compare the

various plans of CSPs available in the market.

Likewise by providing suitable priority to Security

users can ensure that cloud applications are

sufficiently secure. The paper also suggests the

addition/dropping of parameters from the model as

per the requirements of the consumer.

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servers.php GoGrid Cloud Hosting: Dedicated Servers,

Physical Servers.

[14]http://www.rackspace.com/managed_hosting/configurations RackSpace: Dedicated Server, Managed Hosting and

Web Hosting Configurations.

[15] http://www.Amazon.com/services/configurations

different entities. [16]http://www.Cloudflare.com/business types/pricing/

Cloudflare services.



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ISSN:2229-6093

http://www.nist.gov/itl/cloud/upload/cloud-def-v15.pdf

http://www.cloudcommons.com/documents/10508/186d5f13-f40e-47ad-b9a64f246cf7e34f



http://www.mathworks.com/help/pdf_doc/fuzzy.pdf

http://www.gogrid.com/cloud-hosting/dedicetad-servers.php

http://www.gogrid.com/cloud-hosting/dedicetad-servers.php

http://www.rackspace.com/managed_hosting/configurations



http://www.amazon.com/services/configurations

http://www.cloudflare.com/business%20types/pricing/

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