a trust evaluation model for cloud computing using service level agreement

c© The British Computer Society 2014. All rights reserved.For Permissions, please email: [email protected]

doi:10.1093/comjnl/bxu129

A Trust Evaluation Model for CloudComputing Using Service Level

Agreement

D. Marudhadevi, V. Neelaya Dhatchayani and V.S. Shankar Sriram∗

School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India∗Corresponding author: [email protected]

To access cloud services the user needs to negotiate a service level agreement (SLA) with the serviceprovider. There will be inadequate assurances to customers on whether the services are trustworthyto pick. Trust management plays a major role in guiding the users to access trustworthy services.Hence a trust mining model (TMM) is proposed to identify trusted cloud services while negotiatingan SLA. The knowledge is discovered from a previously monitored dataset and a trust value isgenerated. The proposed trust model helps both the service provider and cloud user, where the usercan make a decision on whether to continue or discontinue the service with the service provider. ARough set and Bayesian inference are used together to generate the overall results. Using rough setspreviously monitored data are mined and the indiscernibility in them is analyzed. Bayesian inferenceis applied to infer the overall trust degree. The accuracy of the results is compared with the previousmodels and the result shows that the TMM gives better accuracy. The model is simulated using

CloudSim.

Keywords: trust model; service level agreement; cloud computing; trust management; rough set;Bayesian inference

Received 3 February 2014; revised 3 October 2014Handling editor: Dr David Rosado

1. INTRODUCTION

Cloud computing is the most prevailing technology, whichprovides on demand services. The user can access all typesof services such as infrastructure, platform and software atlow cost from anywhere through the Internet. While accessingthese services, the user should have the confidence whetherhis data stored in the cloud are secured and the service istrustworthy. Trust management [1] is one possible solutionto give the user that confidence. The cloud providers needto build trust models that provide privacy protection throughreliable security mechanisms. This enables consumers to trustthe service, thereby establishing a trusted relationship betweencloud providers and cloud users.

Service level agreement (SLA) plays an important role tomake the service trustworthy. It is a negotiation between cloudproviders and cloud users [2]. The contract must be clearlydefined such that it meets the requirements of service users.Achieving the terms and conditions in a SLA can make theservices more trustworthy. The user has to monitor the service

and check whether it meets the conditions mentioned in theagreement. This may comfort the user and make him continuethe service further with the same cloud provider. To do so,the user needs some additional information like prior data orknowledge about the service which can help him to understandbetter about the quality of service. Trust management of a webservice and cloud service are different. Though most of the webservices are hosted in a cloud environment, the trust modelsdeveloped for generic web applications cannot be applied fora web application hosted in a cloud environment because ofthe differences in the SLA of users who access the same webservice hosted in a cloud. The resource allocation for one userdiffers from another based on the SLA, which demands thedesign of a dedicated trust model for web applications hostedin a cloud.

In this research contribution a trust model is proposedwhich can be effective for a cloud environment. This modelcalculates the trust degree on the prior data obtained aboutthe service at the time of the SLA. The data are divided

Section D: Security in Computer Systems and NetworksThe Computer Journal, 2014

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2 D. Marudhadevi et al.

into multiple attributes. The attributes include the numberof service denials, average response time, task success ratioand number of complaints registered by the users. Attributesused to formulate any trust model can be either objective orsubjective. The work proposed here uses both types of value.Along with objective attributes mentioned above, the modelalso uses subjective opinions of users about the service. Thishelps the system to enhance its trust formulation. In this way,both service provider and service consumers can be benefitedfrom the proposed system. The user can feel secure workingwith the cloud provider. On the other hand, the service providercan monitor the performance and enhance its service to build atrust relationship with the users.

The existing system [3], which is referred to as TraditionalTrust Model (TTM) throughout this paper, uses conditional(objective) attributes to derive a decision (trust degree)attribute using a rough set-based fuzzy classifier. Theindiscernibility in the data is not considered here. But inthe proposed work, both conditional and decision attributesare obtained from the cloud provider’s evidence base. Theambiguity in the data is analyzed using rough sets and anintermediary trust value is calculated for each user data inthe evidence base. Bayesian inference theorem is then appliediteratively to the intermediary trust values and an overall trustdegree (OTD) is obtained.

Rough sets [4] is the technique that helps to mine data.It effectively mines the decision from inconsistent andambiguous data which the existing system fails to do so withthe rough sets. The proposed system calculates trust at twolevels. At level 1, the previously monitored data stored in thecloud provider’s evidence base are mined and a trust degree iscalculated. This is done during the SLA and it is called Level 1Trust Degree (L1TD).

After SLA, the user is allowed to access the service; finally,when signing off from the service, the user gives the feedbackabout the service and a Level 2 Trust Degree (L2TD) iscalculated with the L1TD using Bayesian inference theorem.The L2TD is stored in the evidence base along with otherattributes monitored during this time of usage of the user.Based on L1TD and L2TD, the user may decide on whetherto continue with the service or not.

2. OUR CONTRIBUTIONS

The user must be assured that the service provided istrustworthy and that it meets the requirements. Here thetrustworthiness refers to the reliability and security of thecloud services. The main advantage of this work is that itovercomes the difficulties of traditional approaches in thefollowing aspects:

1. Objective and subjective ratings. The system considersboth objective and subjective ratings of the cloudservices. The objective ratings are monitored by the

cloud provider each time a user uses its services. Thesubjective ratings are the feedback given by the userat the time of his/her usage of service. These ratingsare stored in the evidence base so the next user can usethem to arrive at a conclusion about the trustworthinessof the provider. The proposed model uses the objectiveratings from a previously monitored data as conditionalattributes and the trust degree obtained previously as adecision attribute. Using both these attribute types, thetrust degree is generated. The existing TTM uses onlyconditional attributes to calculate the trust degree.

2. Intermediate trust degree (ITD). The user needs towork with dataset to find the trustworthiness of theservice. Rough set theory helps to find the obvioussolution even when the dataset contains incomplete andinconsistent data. In the proposed trust mining model(TMM) the weight for each attribute is calculated basedon the conditional probability of all combinations ofattributes to take a decision on the dataset. Roughset helps to weigh the attributes and estimate theuncertainty in the dataset. The boundary value in roughsets helps to predict the unreliable data and evaluate theITD. ITD is calculated based on the concept of roughset.

3. Level 1 and Level 2 Trust Degree. Bayesian inferencehelps to calculate L1TD from intermediate trust valuescalculated from the dataset of previous users. Thevalues can be evaluated iteratively to find the L1TD.In the TTM an IOWA operator is used to calculate thetrust degree. The advantage of the proposed systemover the TTM is that it calculates both Level 1 andLevel 2 Trust Degrees based on prior probabilityusing Bayesian inference rather than assigning weightsto arrive at the conclusion. These trust degrees areexplained in section 4.2

3. RELATED WORKS

Many trust models have been proposed, but still the key issueslike indiscernibility in the dataset have to be tackled whilemanipulating the data. Chen et al. [5] proposed a trust modelwhich establishes efficiency in time series to find the OTD. Theauthor has considered only objective data which is not efficientwithout direct experience (subjective data). To make the modelsimple, a learning rate is implied in training the data. A backpropagation neural network is used to avoid the fragileness innonlinear data. This is not effective when the data are too large,which leads to an increase in time and space complexity.

Li et al. [3] proposed a trust model which helps toevaluate the cloud service based on multiple attributes.The methodology preferred by the authors uses aggregationof the trust degree based on time series after which the weightsare subjectively assigned.


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A Trust Evaluation Model for Cloud Computing 3

Song et al. [6] suggested a methodology using subjectivemappings to manipulate the trust factor. According to theuser preferences, trust evidences are collected from differentsources. The well-equipped trust model used in the systemreduces malicious attacks. But this model is quite difficult toimplement in a real-time environment.

Wang et al. [7] suggested an approach for extractingcertainty from the document and ranking them according tothe probability of trustworthiness. They built a trust modelbased on content with the help of factoid learning. This helpsin evaluating the solution of trustworthiness in the web.

Zhang et al. [8] recommended a semantic-based trust mecha-nism using direct evidences but limited human interaction withthe web pages. It provides an effective mining methodology fortrust relation in social networks. But this model cannot handledata they are huge and unstructured in online social media.

The trust model using fuzzy logic and rule-based interpre-tation was developed by Skopik et al. [9]. Based on the expe-riences and interactions between the services and human theyproposed the rule-based trust model. It can evaluate the trustdynamically. But it takes more time to collect the data.

Fong et al. [10] estimated the relative trust factor withthe help of feature selection algorithms with predictor class.Though this model evaluates the trust factors effectively inmany applications, the weights are assigned equally among allattributes. But in the proposed work, weights are distributed toeach attribute based on their importance.

Skopik et al. [11] determined the trust relationship betweenthe members of a particular community and apply the miningin communication data. While evaluating the trust relationship,they consider a local trust relationship. But this model fails todetermine malicious attacks.

In [12] the trust model is either manual or automated, whichremoves the influence of additional factors that affects thenode’s trust. The algorithm proves to avoid overloading onnodes and the success rate gets increased for a service request.The organizational reputation and its trust can be computedbased on the amount of user feedback. The decision can betaken from the feedback given by users dynamically [13]. Thecommon perception of different organizations can be preparedby using the ontology. In [14] a reputation model for trustcalculation in a distributed environment has been proposed. Ina distributed environment, it is a challenging task to managethe risks from malicious users.

Trust is computed based on the recommendation modelsin service-oriented cloud models in [15]. This architecturehelps to evaluate the trust value based on both direct andrecommendation trust. The combination of both the trustmodels leads to better accuracy results; moreover, therecommendation-based trust helps the user to select thebest service. The recommendations from like-minded people[16] will give better accuracy and compute the trust bymonitoring the behavior of the individual members in thesocial community. The behavior of individual members

leads to invitation of friendship from the members of thecommunity.

4. PROPOSED TRUST ARCHITECTURE

The architecture of the proposed trust model is shown inFig. 1. The objective to obtain trust both subjectively andobjectively makes the user identify the knowledge of theservice effectively.

4.1. Modules

The trustworthiness of cloud services can be evaluated in threemodules.

SLA manager module. The SLA manager negotiates serviceagreement between the cloud provider and the cloud user. Thismodule interacts with the trust manager module and gets thetrust value to be included in the agreement before the entireagreement process is completed. The negotiation is completedonly when the user agrees upon the providers’ trust rate. Theresources are then allocated by the cloud manager dependingon the user needs.

Trust manager. This module helps to evaluate the L1TD usingthe past data of previous users extracted from the evidencebase. The value calculated for L1TD is sent to the SLAmanager module to complete the SLA. This module alsocommunicates with the cloud performance monitor module toget feedback of the current user. Based on the feedback values,L2TD is calculated. The user then uses this L2TD and L1TD toarrive at a conclusion on whether to continue with the serviceor not. The L2TD value is stored in the evidence base forfurther use by other succeeding users.

Cloud User

SLA Management

SLA Manager Trust Manager

Cloud PerformanceMonitoring

Evidence Base

VM VM VM VM VM VM

Physical Machines

FIGURE 1. Trust architecture based on the SLA.


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Cloud performance monitor. This module helps to monitorthe service. Once the user negotiates the service, he/she startsmonitoring the service to calculate the trust degree. Monitoringthe service of the cloud provider includes the number of servicedenials, average response time, average task success ratio andthe bandwidth of the network. The user is also allowed to makeany complaints if he/she has any problem with the service. Allmonitored data are stored back in the evidence base with thename of the current user. The feedback is obtained from theuser and a trust value is calculated along with the monitoreddata. The service provider will take charge to monitor theamount of usage. The proposed architecture is shown in Fig. 1.

4.2. Algorithm

The user negotiates for the services and starts accessing themusing Algorithm 1. If the initial negotiation without trust issatisfied, then the trust manager module accesses the evidencebase and calculates the L1TD. Depending upon the trust degreevalue (L1TD), the user can make the decision to continue withthe SLA. That is, if LITD is above the threshold value α, theuser continues with the SLA else discontinues.

The trust value (L1TD) here is calculated using roughsets [4]. Let A = {a1, a2, . . . an} be the set of attributes inthe dataset X . The data set refers to the Evidence Base ofthe proposed system. Let R be the subset of A. The lowerapproximation set R∗(X) is defined as the set of all possiblevalues in set X , i.e. set of all values that completely abideby a decision algorithm of the given application. The upperapproximation set R∗(X) is defined as the set of values thatare more or less certain, i.e. partially abide by the decisionalgorithm. The decision algorithm is specific to the applicationbeing implemented.

Let the accuracy of these approximations be denoted asβR(X). This value helps to find out the uncertainty in thedataset X . The accuracy can be calculated using the followingequation:

βR(X) = |R∗(X)||R∗(X)| (1)

The accuracy value determines the roughness of the dataset[4]. In the proposed trust algorithm, this accuracy value isused to determine the amount of indiscernibility that existsbetween the conditional and decision attributes. A thresholdvalue α is set to measure the accuracy rate. If βR(X)is belowα, then the decision is made in favor of the dataset, i.e. thelatter is accepted to be accurate and complete. Then the L2TDor OTD is calculated using equation (5). If βR(X)is aboveα, then the dataset is assumed to have indiscernibility in thedata. This uncertainty in the given data is solved by analyzingthe degree of certainty in the data on whether the serviceprovider is trustworthy or not. In algorithm 2 this degree oftrust is denoted as σ and it is calculated using the followingequation:

σ = y

m(2)

In equation (2), the indiscernibility of the dataset is to bedetermined for all attributes and their combinations. For eachsuch combination we obtain separate values; y is the set ofall conditional attributes that differs with its correspondingdecision attribute; m is the total number of conditionalattributes in the dataset. The average value of each combinationis represented as σ .

This σ value obtained is then used to compute the ITD foreach user. It is considered as a weight for each attribute andtheir combinations as well. This weight is equally distributedamount all attributes and their combinations using discreteprobability distribution. The ITD is calculated using thefollowing equation:

ITD =n∑

i=1

ai zi (3)

In equation (3) ai is an attribute value or an average ofcombination of attribute values. We see that zi is σi distributedequally for each attribute. The probability distribution of zi is

Algorithm 1 Decision making of trusted servicesNegotiate an agreement with the SLA managerIf (negotiation is ok?)

Access the dataset X from evidence base and calculate the LEVEL 1 TRUST DEGREE (L1TD) using algorithm 2.If (L1TD > α)

Start accessing the cloud services and store the monitored data in the evidence baseCalculate the LEVEL 2 TRUST DEGREE (L2TD) using algorithm 2.If (L2TD > α)

Continue with the serviceElse

Withdraw with the serviceEnd

EndEnd


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Algorithm 2 Computes L1TD and L2TD using Bayesian inference1) Input: Set of attributes Ai in the data set X, where i=1. . . p and p is the number of conditional and decision attributes.2) Output: Overall Trust Degree (OTD) or Level 2 Trust Degree (L2TD)3) Get the dataset from the evidence base4) Evaluate the dataset according to the decision algorithm and calculate

Lower approximation set R∗(X) andUpper approximation set R∗(X)

5) Calculate the accuracy of approximations

βR(X) = |R∗(X)||R∗(X)|

6) if (βR(X) > α)

Compute σ value for all combinations of conditional attributes Cσ = y

m where m is the total number of conditional attributes in the datasety is number of indiscernible data.

Compute the weight zi for each attribute and combination of attributeszi = σ i

m∑j=1

σ j, where i = 1. . .n, n is the number of conditional attributes for a given column in the dataset

Evaluate the Intermediate Trust Degree (ITD)

I T D =n∑

i=1

aizi

End7) Repeat step 6 to calculate ITD value for each user and sort all ITD values to calculate Level 1 Trust Degree L1TD8) Using Bayes’ theorem calculates the L1TD in an iterative fashion.

P(H1|E) = p(E |H1)P(H1)

p(E |H1)p(H1) + p(E |H2)p(H2)

9) If L1TD > α

User starts accessing the serviceCalculate L2TD

P(H1|E) = p(E |H1)P(H1)

p(E |H1)p(H1) + p(E |H2)p(H2)

If L2TD > α

User decides on continuing the access to the serviceElse

User can decide to quit the Service ProviderEnd

ElseUser can decide to quit the Service Provider

End

given in the following equation:

zi = σi∑nj=1 σ j

(4)

After calculating ITD for each user in the dataset, the systemperforms a sorting algorithm. The ITD values are sorted intotwo sets, one set having those values greater than αand theother set having values less than α. The set with values greaterthan α gives positive trust degree to the service provider

and vice versa. Now the set having more number of valuesis considered for decision-making. Then the L1TD can beevaluated on this final set using the Bayes inference techniquegiven in equation (5) [17]. In Bayes’ inference the L1TD iscomputed in an iterative fashion

P(H1|E) = p(E |H1)P(H1)

p(E |H1)p(H1) + p(E |H2)p(H2)(5)


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where P(H1|E) is the L1TD; P(E |H1) is the probability thatthe services are good according to the current user; P(H1) isprobability that the services are good according to the previoususer; P(E |H2) is the probability that the services are badaccording to the current user; P(H2) is the probability that theservices are bad according to the previous user.

Here P(H1|E) is the L1TD value. If the L1TD value isgreater than α, then the service is considered as a trustedservice and the user decides on whether to start using theservice. After the user has started using the service, the cloudperformance monitoring module starts monitoring the serviceand stores the monitored data in the evidence base. Finally, theuser gives feedback on the services used based on which theL2TD is calculated similarly using equation (5). Based onthe L2TD value, the user can decide on whether to continueor discontinue with the service. Finally the L2TD is storedback in the evidence base. Similarly, the monitored data onthe services during the time of usage of the user are alsostored in the evidence base, so that these values can be usedby other users in the future for deriving trust upon the serviceprovider.

5. RESULTS AND DISCUSSIONS

The system is simulated using Cloudsim simulator. Both theproposed TMM and the existing TTM were simulated. Thesimulation results show that the proposed TMM algorithmusing rough set theory and Bayes theorem produce accurateresults compared with the traditional TTM model. For thepurpose of simplicity, an evidential base of 10 users ispresented in this discussion. Table 1 shows the raw datasetof 10 users; with conditional attributes (no. of denial services,average response time, average task success ratio, bandwidthof network, no. of complaints) and decision attribute (TrustDegree calculated by the previous user). The system convertsthese raw data into different ratings between 0 and 1 usingthreshold values. The converted data are represented in Table 2.Table 3 gives the L1TD value calculated using Bayesianinference technique. From Table 3, it can be concluded that theservice provider is a trusted provider assuming that α = 0.5.Observing the values of trust degree for the service providerat different times from T1 to T5 by different users, Table 4provides the L2TD obtained using the proposed TMM and the

TABLE 1. Raw dataset.

No. of denial Average Average task Bandwidth No. ofUser services response time success ratio of network complaints Decision

1 3 5670 0.6 30 5 0.482 2 3289 0.833 65 4 0.863 0 3432 0.8 55 2 0.34 3 6217 0.375 42 5 0.45 1 2359 0.833 47 8 0.336 1 1231 0.916 65 2 0.757 0 1023 1 60 1 0.838 4 9890 0.428 37 5 0.489 1 2239 0.9 40 3 0.74

10 0 7890 0.888 58 7 0.36

TABLE 2. Dataset transformed after fixing the threshold value.

No. of denial Average Average task Bandwidth No. ofUser services response time success ratio of network complaints Decision

1 0.9 0.6 0.6 0.3 0.6 Bad2 0.9 0.9 0.9 0.9 0.6 Good3 0.9 0.9 0.9 0.6 0.9 Bad4 0.6 0.3 0.3 0.6 0.6 Bad5 0.9 0.9 0.3 0.9 0.6 Bad6 0.9 0.9 0.9 0.9 0.9 Good7 0.9 0.9 0.9 0.9 0.9 Good8 0.6 0.3 0.6 0.6 0.6 Bad9 0.9 0.9 0.9 0.6 0.9 Good

10 0.9 0.3 0.9 0.6 0.3 Bad


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traditional TTM models. From Table 4, it can be observed thatthe proposed Trust Management Model gives better accuracythan the existing model. To measure the rate of accuracybetween the proposed and the existing systems, the table alsoincludes a column called actual trust degree of the serviceprovider. This value is an assumption so as to prove the resultsof the two algorithms implemented.

TABLE 3. After the calculationL1TD.

User ITD

1 0.55842 0.80473 0.86214 0.75725 0.64926 0.66937 0.91518 0.51759 0.4008

10 0.5815L1TD 0.857

TABLE 4. Final trust degree of service provider from TimeT1 to T5.

Existing Trust Proposed Trust Actual trustdegree of TTM degree of TMM degree of the

Final trust Final trust providerTime degree (L2TD) degree (L2TD) (Assumed).

T1 0.6 0.8 GoodT2 0.6 0.3 BadT3 0.3 0.5 GoodT4 0.4 0.7 GoodT5 0.7 0.5 Bad

FIGURE 2. Graph showing trust rate of proposed and existingsystems.

The graph in Fig. 2 shows that the trust degree of theproposed model is nearer to the actual trust value of the trustprovider than that of the existing trust model.

6. CONCLUSION

This paper provides a trusted cloud model using techniquessuch as Rough set theory and Bayesian inference technique.The architecture helps the user to find a trustworthy cloudservice and the cloud service provider to monitor and improvethe services provided. Using the proposed system, the user cancompute results from the past record of evidence generatedfrom a previous user’s experience. The system helps inachieving a Quality of Service constraint for the ServiceProvider by enabling him/her to provide better services basedon the results of the system. The results prove that the overallaccuracy of the proposed system is better when comparedwith the existing system. The proposed work confines toprovide trust management for a single application at a time.Our future work aims to incorporate advanced features totrust management. This is currently implemented only for oneapplication in cloud, and we aim to extend it to multipleapplications. Moreover, future work also includes addressinguncertainty in the trust data.

FUNDING

This work was funded by Fund for Improvement ofS&T Infrastructure in Universities and Higher EducationalInstitutions (FIST), Department of Science and Technology,Government of India (SR/FST/ETI-349/2013).

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a trust evaluation model for cloud computing using service level agreement

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