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Hierarchical Trust Model to Rate Cloud Service Providers based on

Infrastructure as a Service

Supriya M

1

, Sangeeta K

1

, G K Patra

2 1

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

2

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

{m_supriya,k_sangeeta}@blr.amrita.edu, gkpatra@csir4pi.in

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

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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

C S P a n d S er v er ty p e

Agility Financial Performance Security Usability

No o f V. M No o f DC S to ra g e S p ac e in T.B V.M C o st/ h r( $ ) S to ra g e C o st / GB ($ ) T ra n sf er C o st / GB ($ ) No . o f P ro ce ss o rs R AM In GB P h y sica l S ec u rit y In ter n al S ec u rit y Ne two rk S ec u rit y Un d er stan d ab il it y E as ab il it y F lex ib il it 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 Dedicated Server 0.96 92.50 Gogrid Elite Dedicated Server 1.06 252.47 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

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

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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.

8. References

[1] Mell P, Grance T, “A NIST definition of cloud

computing”. National Institute of Standards and

Technology. NIST SP 800-145.

http://www.nist.gov/itl/cloud/upload/cloud-def-v15.pdf, 2009.

[2] Siani Pearson, “Privacy, Security and Trust in Cloud

Computing”. HP Laboratories, Springer, June 2012.

[3] Xin Liu, Gilles Tredan and Anwitaman Datta, “A

generic trust framework for large-scale open systems using machine learning”. March 2011.

[4] Mohamed Firdhous, Osman Ghazali and Suhaidi Hassan, “Trust Management in Cloud Computing: A

Critical Review”. International Journal on Advances in ICT

for Emerging Regions, 2011, 04 (02): 24 – 36.

[5] Kui Ren, Cong Wang, and Qian Wang, “Security

Challenges for the Public Cloud”. Illinois Institute of

Technology, February 2012.

[6] Somesh Kumar Prajapati, Suvamoy Changder and Anirban Sarkar, “Trust Management Model For Cloud

Computing Environment”. Proceedings of the International

Conference on Computing. Communication and Advanced Network - ICCCAN 2013.

[7]Liu Xin, “Trust beyond reputation: Novel trust

mechanisms for distributed environments”. A thesis report,

2011.

[8] Supriya M, Venkataramana L.J, K Sangeeta and G K Patra, “Estimating Trust Value for Cloud Service Providers

using Fuzzy Logic”. International Journal of Computer

Applications, Volume 48– No.19, June 2012.

[9] Supriya M, K Sangeeta and G K Patra, “Comparison of

Cloud Service Providers Based on Direct and

Recommended Trust Rating”. IEEE CONECCT, January

2013.

[10]http://www.cloudcommons.com/documents/10508/186 d5f13-f40e-47ad-b9a64f246cf7e34f, Cloud Service Management Index Consortium (CSMIC). “Service Management Index Version 1.0” (PDF), September 2011. [11] http://www.mathworks.com/help/pdf_doc/fuzzy.pdf Fuzzy Logic Toolbox User’s Guide.

[12] Wickremasinghe, B, Calheiros R.N and Buyya, R.

“CloudAnalyst: A CloudSim-Based Visual Modeller for

Analyzing Cloud Computing Environments and

Applications” 24th

International Conference on Advanced Information Networking And Applications, Australia, April 2010.

[13] http://www.gogrid.com/cloud-hosting/dedicetad-servers.php GoGrid Cloud Hosting: Dedicated Servers, Physical Servers.

[14]http://www.rackspace.com/managed_hosting/configurat ions 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.

References

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