Research Article Game Theory Based Trust Model...

Research ArticleGame Theory Based Trust Model for Cloud Environment

K. Gokulnath and Rhymend Uthariaraj

Ramanujan Computing Centre, Anna University, Sardar Patel Road, Chennai, Tamil Nadu 602001, India

Correspondence should be addressed to K. Gokulnath; [email protected]

Received 10 March 2015; Revised 4 July 2015; Accepted 21 July 2015

Academic Editor: Sung Won Kim

Copyright © 2015 K. Gokulnath and R. Uthariaraj. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

The aim of this work is to propose amethod to establish trust at bootload level in cloud computing environment.This work proposesa game theoretic based approach for achieving trust at bootload level of both resources and users perception. Nash equilibrium (NE)enhances the trust evaluation of the first-time users and providers. It also restricts the service providers and the users to violateservice level agreement (SLA). Significantly, the problem of cold start and whitewashing issues are addressed by the proposedmethod. In addition appropriate mapping of cloud user’s application to cloud service provider for segregating trust level is achievedas a part of mapping.Thus, time complexity and space complexity are handled efficiently. Experiments were carried out to compareand contrast the performance of the conventionalmethods and the proposedmethod. Several metrics like execution time, accuracy,error identification, and undecidability of the resources were considered.

1. Introduction

Cloud computing caters versatile service needs to diversecomputing requirements. Services are catered through estab-lished TCP/IP network. Service providers and service usersare distinct of each other; this raises the notion of trust. Trustis always unease for the distributed computing environment.Trust is the expected continuous behaviour of an entity. Usersand resources vary over time in cloud environment; thusdynamic nature of the cloud environment makes trust evencomplex. Thus, trust is a major concern in cloud computing.Cloud computing faces the challenge of trust in its ownway. Resources are abstracted from applications becauseof usage of third-party owned resources (datacenter) andquestion of belief arises. Cloud faces the challenge of trustin following ways. First, virtual machine (VM) IDs createdduring VM boot up by host machines are temporary. Hence,if anything goes wrong during execution, host machinescan trace the actual VM using VMID. But, it is difficultto trace the actual VM which carried out the task afterthe shutdown. Thus reassigning becomes difficult. Second,Probability of a high trust rated resource failure is possible.Thus, ensuring trustworthiness for each interaction resultsin safer transactions and finally, due to growing popularity,

new vendors getting into market to provide services. If theyare utilized properly, service utilization can be increased andwaiting time shall be reduced to greater extent.

In distributed computing paradigm’s trust evaluation,initial trust or trust bootstrapping begins with uncertaintyover resource trustworthiness. Bootstrapping of trust incloud environment is quite uneasy; at the same momentit is vital for both users and service providers. Generally,trust in distributed system is classified into two categories,namely, indirect trust and direct trust. Indirect trust is furtherclassified into reputation based trust and recommendationbased trust. The purpose of the reputation based trust [1] isto measure trustworthiness of the resources by the credibilityearned globally. Services are well explored by the global users;hence it is not mandatory for individual users to experienceand test the trustworthiness. Services are obtained fromaccepted service providers. Levels of trust are consideredhigh for the resources. However, new resources for renderingservices may not be evaluated by this method. Reputationearned for services may vary based on the context; thus,context specificity is also to be considered. Estimated trustvalues are obtained from external entities and owing to thisreason reliability is a concern. Furthermore, this type ofevaluation may increase time complexity of trust calculation

Hindawi Publishing Corporatione Scientific World JournalVolume 2015, Article ID 709827, 10 pageshttp://dx.doi.org/10.1155/2015/709827

2 The Scientific World Journal

because of the complex analysis involved in analyzing thefeedback [2] from global users. Recommendation based trust[3] aggregates the trust values obtained from trusted friends.Users with transaction history with a resource of interestpass the trust value calculated to other users for exploringthe service. This method helps to evaluate trust withoutglobal reputation or direct past interaction. Computationalcomplexities of identifying the friends, communication costinvolved in transit, and estimating the trust acquired are theoverheads to be considered. Moreover, like the reputationbased category, reliability is a concern in this category,because of the contribution of external entities. Direct trust[4] measures the trustworthiness of the resource providerthrough the past history of transactions from self-encountersand its subsequent trust values assessed. Since the externalentities involvement is reduced to a greater extent, com-putational complexity involved is comparatively minimalcompared to the previous methods and also the communi-cation cost is avoided. Further the history of transaction ismandatory for evaluation.The improvement of the reliabilitydoes not rely upon external entities. New requests face troubleof no past statistics; thus assessment of trust faces challenge.New requests have no past statistics and hence assessment oftrust is a challenge. Since statistics-maintenance is essentialfor this method, it results in space complexity.

Most of the trust evaluation methods presented in lit-erature are based on past transactional history. However,bootload level of trust evaluation is rarely identified. Thiswork emphasizes direct trust for establishing trust rela-tionship with cloud users and/or providers. Reliability fordirect trust is comparatively higher than other discussedtrust evaluations. Moreover, it should be considered howinitial trust obtains the trust value for interaction. In caseof uncertainty it assigns equally good level and bad level oftrust. Computational complexity is considerably higher inassigning the values for uncertain sources. Response time canbe considerably improved, if the computation is reduced.

In addition, as per Gartner’s prediction [5], by 2016,the enterprises will make independent testing of securityas mandatory proof to use any service provided by cloud.Hence, direct trust may have a major contribution towardsassessing cloud services in future. This is to be handledprecisely. Eventually, the proposed method addresses theissue. Furthermore, Cloud Security Alliance (CSA) report [6]says the number of cloud threat issues has grown to 12. Trustand uncertain risk profile are the serious challenges to becombated. Thus from the analysis of the current trends it canbe inferred that trust is an issue of serious concern.The aboveproblem resembles one-time game. Hence, the proposedwork uses game theoretic approaches for attaining trust ininitial level. One-time games are also suitable for uncertainenvironment and are applicable for first-time interaction. Asa contribution to establish trust between users to resourceprovider and vice versa at bootload level, this work uses2-player Nash equilibrium. Computational complexity ishandled effectively by 2-player Nash equilibrium, which fallsunder PPAD-complete [7]. To the best of our knowledge thisbootload level of trust evaluation in cloud environment is thefirst of its kind.

The rest of the paper is organized as follows. Section 2presents the related works to this work in the literature.Section 3 describes the game theoretic methods used in thispaper; Section 4 elaborates how trust concepts assorted withgame theory. In Section 5 the results achieved in this workare discussed. Finally Section 6 highlights the achievementsof this work and quotes the future enhancements to be carriedout. The list of references to build the work is furnished.

2. Related Works

Trust is a dynamic notion, which transforms with respect totime and context. Particularly, in cloud computing, frequentchanges in users and service providers are highly probableand thus results in cold start problem towards the evaluationof trust. Problem of evaluating trust at initial transaction hasbeen addressed by several works in the literature. Like thepeer-to-peer network, trusting the unexplored resource mayend up with the problem of whitewashing. Most commonmethods of bootstrapping like [8] are to allot default values(low to high) by discrete scale to the new resources.

Hard trust by contract was proposed by [9]. It overcomesthe soft trust solutions. While soft trust evaluates trustby judgments, hard trust invokes the service before theinteraction. This method suffers the demerits of maintainingthe registry for providers list and to maintain the trust levels,for all the available service that dampens memory efficiency.A proactive reputation for rating new resources and to attendthe cold start issue was proposed by [10]. P2P reputationmodel was carried out, in which, to generate the reputationratings, proactive initiation for interaction among peers wasproposed. Endorsement based web service selection wasproposed by [11]. This method is used to endorse the newservices by trusted participant in the system. Difficulty inassessing the newcomer is not cited. Trust bootstrappingbased on ontology has been proposed in [12]. It addressesthe problem of initial trust by means of peer review process.Though the method evaluates the trust during bootload ofweb services, it involves coordination amongst the availablepeers. Coordinating is a task to be handled against some cost.Predefined communities evaluate the trust. In another case,trust can be assigned with some initial assumption [12]. Bothcases do not attend the problem of trust under uncertainty.

The problem of initial trust by trusted recommender(community based) was attended by [13]. Updating therecommenders trust level seize considerable amount ofcomputation time, thus directly reflecting the computationalcomplexity at initial level. Also, reliability of trust fromexternal entity is an issue. A novel method by introducingthe concept of new metrics for trust was proposed in [14].Their method suffers from the problem of segregating theresource metrics, based on context. Computation time isconsiderable and also the method has the drawback ofassigning some default initial value. The methods discussedabove, addresses the problem of initial trust or trust beforeinteraction. Several demerits can be quoted from the abovelist, for instance, assigning default value for unrated (new)resources; if genuine resources are assigned a low trustvalue, probability of provisioning of resources may get stifled.

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At the other end, if a nongenuine resource gets assigneda high trust value, then the outcome may lead to disas-trous outage. Gathering reputation or recommendation forunexplored resources is uneasy as the resources behaviouralattributes are uncertain. Problem of exploring potential cloudresources under uncertain circumstances is to be consideredas an earnest issue. Most cited works do the task of evaluatingthe trust in certainty. Under uncertain circumstances, ran-dom assignment of trust values, as adopted by the previousworks, may not be fair with respect to genuine resources.Uncertain resource trust levels and uncertain user behaviourmakes beginning uneasy. Cloud computing includes newresources (without pretrust evaluation) and it is obviousthat new users are inevitable. Hence evaluating trust undercomplete uncertainty in a fair manner improves the numberof potential resources at the initial stage. Furthermore, theproblem of cold start and whitewashing are supposed to beaddressed. Thereby, the total number of resources availablefor service can be increased. In general, as said in [15]“establishing trust in cloud systems requires two mutuallydependent elements.”Whether it is boot level or higher level,both entities must cooperate for trust.

3. System Model and Methodology

Objective of the proposedmethod is to present a novel modelto evaluate trust for naive entities in cloud environment withan unbiased manner. Also, the proposed method neutrallyevaluates trust for both user and resource provider. Outline ofthe cloud model adopted in this work is depicted pictoriallyin Figure 1. Layered approach paves easy deployment of theversatile services within the cloud. Highlighted boxes are thecore in-house layers to the cloud. User and resource are theend layers. Interface collects requirements from the user anddisplays potential resource providers list to the user. Servicelayer carries the task of scheduling, load balancing, SLAcreation, monitoring, and trust managing. Service layer iselaborately discussed in later sections. Based on the selectedtype of service (IaaS, Paas, and SaaS), appropriate servicelayer proceeds the task further. Virtualization is the closelyassociated layer with physical resources and the actual servicedeployment done by this layer.

Detailed service layer illustrated pictorially in Figure 2.SLA (service level agreement) ensures two parties (user andprovider) and furnishes actual statements that are availablebefore the service begins and recorded for future corre-spondence. The trust manager updates the level of trust foran entity against another by applying Nash equilibrium byforming a game structure between user and provider. Themonitor continuously monitors the SLA for any violationand predefined accord. To play a game and to attain Nashequilibrium, certain parameters are mandatory with respectto trust relationship. These parameters, namely, risk, belief,disbelief, and uncertainty, are highlighted in the next section.

3.1. Parameter Description

Risk. Whenever the notion of trust is discussed, risk is theassociated parameter of interest. Risk is an integral part of

Application

Interface

Service

IaaS PaaS SaaS

Virtualization

Resource

Clou

d la

yers

Cloud, a layered architecture

Figure 1: Proposed layered model.

Service layer

Service level agreement (SLA)

Monitor Trust manager

Scheduler

Load balancer Cost estimator

Figure 2: Service layer.

trust. The relationship between trust and risk is highlighted[16]. It states that trust and risk are in complementaryrelation; that is, the higher the trust, the lower the riskand vice-versa. Risk of the provider’s resources is underlime lights, when accessing the services. Assessing the riskinvolved helps in evaluating trust of an entity. Based on risk,user can be segregated into three broader categories:

(1) risk seeking;(2) risk neutral;(3) risk aversion.

This work considers risk as the key parameter for evaluatingtrust. An application is segregated along with risk as riskseeking application, risk neutral application, and risk averseapplication. Once the application is segregated, the nextmeasure is to estimate the risk associated with it. In fact,risk estimation becomes easier after segregating the type ofapplication.

Belief.Belief is the degree of confidence expressed as objectiveand subjective, with respect to entity and time. Belief isa dynamic entity, which changes with respect to time and


context in any computational paradigm. Cloud is a dynamicenvironment, wherein the belief of an entity over otherschanges frequently. The proposed method uses belief as asubjective logic over cloud user’s perception to cloud resourceprovider. The cloud resource provider also expresses it as thesame for cloud users. Josang and Ismail present models andmethods to assign weights for belief [2].

Disbelief. Disbelief is the degree of doubt over an entity.In belief, degree of disbelief changes with respect to timeand context. Disbelief refers to the negative consequencesexpected.

Uncertainty. When there is no previous interaction with anentity, uncertainty emerges over its trustworthiness. Usually,resources performing the very first transaction of any kindof services face this challenge. Resources with uncertainspecifications are normally not considered to avoid any futureloss by the users as well as the service providers (in case ofhigher transaction values).

Belief, disbelief, and uncertainty together form a beliefadditivity function represented in [2]. Uncertainty can beaddressed by means of substituting some probability valueknown as uncertain probability. User’s opinion is representedas subjective belief. Furthermore Josang and Ismail state thatthe sum of belief, disbelief, and uncertainty equals one [2].Thisis mathematically represented as

𝑏 (𝑥) + 𝑑 (𝑥) + 𝑢 (𝑥) = 1, (1)

where

𝑏(𝑥) is the belief over entity 𝑥;

𝑑(𝑥) is the disbelief over entity 𝑥;

𝑢(𝑥) is the uncertainty over entity 𝑥;

thus, any two entities are sufficient for expressing trust.

3.2. Methodology. This work focuses on game theory as amajor framing structure, as it forms a game between userand service provider through a bimatrix. Bimatrix gameformation helps us to achieve a service level agreementbetween the user and the provider throughNash equilibrium.Since 2-player Nash equilibrium falls under PPAD-complete[7], time complexity is handled more efficiently than othermethods. Nash defines game theory as a study of strategicdecision making. Specifically, it is the study of mathematicalmodels of conflict and cooperation between intelligent rationaldecision makers [17]. Game theory provides a mathematicalway of solving situation conflicts. Also, it helps to analyzethe efficiency of decision making where interdependencebetween entities exists. At least in zero-sum games, it framesan experimental computing means to reach an optimalstrategy. General elements of a game include player, action,strategy, result, information, equilibrium, and payoff. Asingle player will have his/her own strategy derived from

chosen action and the payoff for the action from the giveninformation.

Player. A player is rational decision maker and intends tomaximize his payoff.

Payoff. It is the expected outcome for a player.

Strategy. A strategy (A) means the action selected by a playerin each iteration. 𝑆 = {𝑠

1, 𝑠2, . . . , 𝑠

𝑛}.

Strategy Profile. It is a set of strategies selected by playersinvolved in each iteration.

Equilibrium. It is a strategy profile equally poised for allplayers.

Nash Equilibrium (NE). NE is a solution concept of a gameinvolving two or more players, in which each player isassumed to know the equilibrium strategies of the otherplayers, and no player has anything to gain by changing onlyhis or her own strategy unilaterally [18].

To simplify further, Nash equilibrium is a strategy agreedbetween two players where none of the players has the chanceto deviate from the agreement to benefit unilaterally.

Let us consider (𝑆, 𝐹) as a game with 𝑛 players; here 𝑠𝑗is

considered as a strategy set for player 𝑗. As discussed earlier𝑆 = {𝑠

1, 𝑠2, . . . , 𝑠

𝑛} is the set of strategy profiles with 𝑆

𝑢as set of

strategy profile for user and 𝑆𝑝as strategy profile for provider

and 𝐹 is the payoff function for 𝑥 ∈ 𝑆 with 𝐹𝑢as payoff

function for user and 𝐹𝑝as payoff function for provider. In

this scenario, 𝑆𝑢, 𝑆𝑝∈ 𝑆 and 𝐹

𝑢, 𝐹𝑝∈ 𝐹 form the set

(𝑆, 𝐹). Let 𝑥𝑗be a strategy profile for player 𝑗 and let 𝛽

−𝑖be

a strategy profile of all players except for player 𝑖. When eachplayer 𝑖 ∈ {1, 2, . . . , 𝑛} chooses strategy 𝑥

𝑖resulting in strategy

profile 𝑥 = {𝑥1, 𝑥2, . . . , 𝑥

𝑛} then player 𝑗 obtains payoff 𝑓

𝑗(𝑥).

A strategy profile 𝑥∗ ∈ 𝑆 is Nash equilibrium (NE) if nounilateral deviation by single player is selfishly profitable byparticular strategy; that is,

∀𝑖, 𝑥𝑖∈ 𝑆𝑖: 𝑓𝑖(𝑥𝑖

∗, 𝑥−𝑖

∗) . (2)

When the inequality above holds strictly (with > insteadof ≥) for all players and all feasible alternative strategies, thenthe equilibrium is said to be a strict Nash equilibrium. If,for some players, there is exact equality between 𝑥

𝑖

∗ andsome other strategy in the set 𝑆, then the equilibrium issaid to be a weak Nash equilibrium. Thus, a game betweena user and a provider makes SLA achieve Nash equilibriumthrough a bimatrix game resulting in trust by adopting theabove discussed parameters. Nash equilibrium is applicablefor one-time game only and considers players rationality.This characteristic of NE helps to coordinate two players foroptimal payoff.

4. Game Model

The proposed work extends (1) from a single linear equationto two-dimensionalmatrix representations (a bimatrix game)


Table 1: Table form of (1).

User Resource providerBelief Disbelief Uncertainty

Belief (𝑏, 𝑏) (𝑏, 𝑑) (𝑏, 𝑢)

Disbelief (𝑑, 𝑏) (𝑑, 𝑑) (𝑑, 𝑢)

Uncertainty (𝑢, 𝑏) (𝑢, 𝑑) (𝑢, 𝑢)

as a primary objective. Equation (1) is used to express theparameters like belief, disbelief, and uncertainty by a singleuser only. This gives the chance for single user to expressthe weights of desired attributes, whereas in a game modelminimum of two users gets involved. Hence, a minimalmatrix representation is required here. While a cloud user’sperception forms one dimension, resource provider formsanother dimension and the two are equally poised to infusefairness towards estimation of trust. Since, the bimatrix gameis formed with mixed profile strategies, equal chance of bothcloud user and resource provider is entitled through propertrust management activity. Moreover, one-time games areapplicable for naıve entities alone.Thus (1) extended as a gamearena between a user and a service provider is represented inTable 1.

Table 1 enclosed in SLA of the proposed architectureobtains perception values from the cloud user and the serviceprovider with respect to (1).

It is mandated by the SLA for both cloud users and theservice providers to assess the type of application/resourcelevel risk involved prior to assigning values for the belief,disbelief, and uncertainty. In fact, as per the design of theproposed work, only after considering the appropriate risklevel, a cloud user or service provider can start assigning valueto belief, disbelief, and uncertainty. Thus, the type of risk ismapped with the type of application/resource as follows. Asample of risk type and associated clouduser’s application andresource provider’s resources are highlighted in the following.

Risk Aversion (Type of Risk), for Example, Real Time Appli-cation. For Example, there are several mission critical appli-cations, which require cloud environment, where a videostreaming kind of application could tolerate the risk ofunavailability for a few seconds. At the other end, somemission critical resources like bank transaction like servicescannot tolerate the risk of unavailability.

Risk Neutral (Type of Risk), for Example, Free Mobile Apps.Several applications do exist with the requirement of evenmore time flexible services under cloud environment. Forexample, static pages of portals can tolerate the risk of delayfor a certain time period. At the other end service providershosting offline applications under cloud shall not tolerate therisk slackness in catering the services.

Risk Seeking (Type of Risk), for Example, Time InvariantApplication. Applications like time insensitive streaming ofmultimedia applications and certain applications like offlinedownloading can be performed even with risk of loss during

Application

Risk aversion

Risk neutral

Risk seeking

Cloud user

Resource

Risk aversion

Risk neutral

Risk seeking

Cloud service provider

1

2

3

45

6

7 8

9

Figure 3: Mapping of cloud users risk to resource providers risk.

the transaction. Service providers hosting offline time insen-sitive and noncasual applications under cloud can tolerate therisk of delay in times.

A prospective cloud user to resource provider mapping isshown in Figure 3. It shows mapping numbers from 1–9 forthree types of risk from cloud users to resource providers.Here mappings 1, 5, and 9 are straight mapping. Here riskaverse kind of applications are mapped to risk averse kind ofresources, risk neutral applications aremappedwith risk neu-tral resources, and risk seeking applications are mapped withrisk seeking resources.Thus, level of belief can be set at a highlevel for these direct mapping theoretically, say, more than66%. For practical reasons, level of percentage is restricted tokind of mapping and a cap of 85% is considered. Since morethan 85% [15] of value may end up with dominated strategyin calculating the equilibrium, such percentage forces othervalues to marginally lower the chances of Nash equilibrium.Hence, threshold of 85% is considered. Mappings 2, 4, 6, and8 are considered as midlevel belief, since the nature of neutralrisk is involved in either of the entities. Thus, it can ends upin the range of 33% to 66%, and remaining mappings 3 and 7are considered as low level of belief.

In this mapping, 33% of value is considered as the gradeseparator for one kind of belief value to another, because fora risk neutral application, 33% of belief value can be appliedfor all the three types of risk. Here, high level belief valuesare belief (𝑏), midlevel beliefs are uncertain (𝑢), and low levelbeliefs are disbelief (𝑑).

Apart from these types of segregation considered byplayers, Jensen’s inequality [19] is used by players to aversethe risk. It states that

∫ V (𝑥) 𝑑𝑓 (𝑥) ≤ V (∫𝑑𝑓 (𝑥)) , (3)

where

V(𝑥) is gain utility for 𝑥;𝑓(𝑥) is nondegenerate value for 𝑥;∫𝑥𝑑𝐹(𝑥) is expected value.

𝑓(⋅) is a cumulative distribution function; thus the continu-ous utility value is integrated with respect to nondegeneratevalue to estimate the outcome. On the other hand expectedutility of𝑥 is calculated and compared by considering the type


Table 2: Table 1 with values after risk calculation.

User Resource providerBelief Disbelief Uncertainty

Belief (.4, .3) (.4, .5) (.4, .2)

Disbelief (.25, .3) (.25, .5) (.25, .2)

Uncertainty (.35, .3) (.35, .5) (.35, .2)

of risk, for risk aversion (3) is applied to averse the risk. Sincerisk seeking is inverse of the risk averse scenario, (3) can beconverted to risk seeking as

∫ V (𝑥) 𝑑𝑓 (𝑥) ≥ V (∫𝑑𝑓 (𝑥)) (4)

and, for risk neutral, as

∫ V (𝑥) 𝑑𝑓 (𝑥) = V (∫𝑑𝑓 (𝑥)) . (5)

After assigning the values from either end of the cloud,SLA forms the game between users and providers. A modelSLA as a game is given in Table 2.

4.1. Solving for Nash Equilibrium (NE). Thepresent work usesthe algorithm of [18] for attaining Nash equilibrium, overLemke-Howson algorithm.Thus, we use four steps to achieveit which are as follows:

(1) tableau formation;(2) identifying entering and leaving variable;(3) result.

Step 1. By identifying the strictly dominated strategies fromthe mixed profile strategies, fairness can be preserved. Fair-ness helps to proceed the game equally poised for both cloudusers and service provider. This is achieved by applying rowdominance rule and column dominance rule, respectively.

Step 2. Tableau formation of the available mixed profilestrategies after applying dominance helps to identify Nashequilibrium from Step 3.

Step 3. Pivoting elements are identified arbitrarily from cloudusers or cloud provider’s tableau formed in the previous stepandmin-ratio rule is applied for appropriately identifying theentering and leaving variables. The step is repeated until aunique variable is obtained as the winner.

Step 4. From the appropriate variable’s row,Nash equilibriumis achieved from the previous step.

One advantage from the above cited method to this workis that need for normalization shall be optional. Normal-ization is applied only when the resulting Nash equilibriumdeviates from the listed strategies. That is to identify thenearby strategy from the tableau and not for normalizing tothe value of (1) as in [18]. Also, the method fixes both entitiesto be in a single strategy throughout the transaction.

Table 3: Test bed.

S. number Entity Quantity Purpose1 VM 10–100 Virtual machines2 Cloudlet 10–10000 Job to cloud3 User 10–1000 Service requester4 Provider 10–10000 Service provider5 Risk 3 types Parameter

4.2. Algorithm. See Algorithm 1.

5. Results and Discussion

The core objective of this work is to frame a model forcloud environment at bootstrapping level. This work wasexperimentally evaluated by simulation using cloudsim andstudied the performance. Test bed consists of the attributesshown in Table 3.

As defined in [20] and depicted in Figure 4, datacenteris at the higher level of abstraction in the service/resourceprovider’s end. This forms the highest external interfacefrom provider’s part. Host comes under the datacenter andaggregates the virtual machines (VM’s). A datacenter maycontain “𝑛” numbers of host bounded by its capacity; virtualmachines are the actual boxes that replicate the actualphysical boxes. A host may have “𝑛” number of VM’s, whichare created according to the requirements. Final level ofhierarchy forms the processing elements (pe’s). Pe’s are similarto actual processors, which performs computing inside theVM’s. Multiple pe’s of a single machine can be made ashomogeneous and/or heterogeneous clusters. The proposedwork is compared with the existing approaches like hardtrust and metric based trust. Table 4 summarizes the threemethods in detail.

5.1. Availability. Identification of trusted resources at thebootloading level is the prime focus of this work. 10 to 100numbers of resource providers were considered for simula-tion setup in cloudsim environment. Results reveal that Nashequilibrium method (NEM) out-performs hard trust andmetric based trust bootstrapping mechanisms for identifyingmore number of resources at the boot level. Hard trust basedbootstrapping evaluates the trust for a given resource throughsemantics identified by the evaluator (user/provider). Sincesemantics need to be stored in the form of a database,considerable amount of time is required for retrieving thedatabase. Furthermore the semantics for fresh dimensionsare not easy for newer context resources. Metrics based trustevaluation indirectly cites risk involved in entities. Attributesmay not be defined for the entire resource context; sometimesit may be redundant as well. Availability of fresh and genuineresources is sometimes not evaluated as shown in Figure 5,due to these reasons. Trustworthy resources identification byNEM involves direct risk basedmetric as provided by the userand provider. In spite of being so close to hard trust basedmethod in few instances, marginally NEMoutperforms thoseinstances. Overall NEM identifies more genuine resourcesthan other cited methods. From the results it is clear that


Table 4: Comparison of existing and proposed methods.

S. No Method Parameters Achievements Observations

1 Hard trust Security features, interfaces, andservice semantics.

Semantics defined for all the keyattributes. Considering contextlevel semantics.

Defining semantics for initialprovider is a little complex.

2 Metric basedtrust

Subjective service trust metric,objective service trust metric,and trust rate.

“Services selected and trustedaccording to their securityfeatures, and they are discoveredaccording to their interfaces orsemantic descriptions.”

Honesty of service providers atbootstrapping level is evaluatedin a lighter sense.

3 Proposed(NEM)

Risk type, application type,belief, and uncertainty.

Considering risk involved withrespect to both user and serviceprovider.

Method facilitates mutualagreement at the boot loadinglevel.

Inputs Belief (𝑏(𝑥)), Disbelief (𝑑(𝑥)), Uncertainty (𝑢(𝑥)), Type of Application.Output Evaluated trust value.NE-Trust

𝑏(𝑥) ← belief over 𝑥𝑑(𝑥) ← disbelief over 𝑥𝑢(𝑥) ← uncertainty over 𝑥

Let,𝑏(𝑥) + 𝑑(𝑥) + 𝑢(𝑥) = 1

Create 2D array for the above equation𝑆𝑢= {𝑠𝑢1, 𝑠𝑢2, . . . , 𝑠

𝑢𝑛}

𝑆𝑝= {𝑠𝑝1, 𝑠𝑝2, . . . , 𝑠

𝑝𝑛}

𝑆𝑢, 𝑆𝑝∈ 𝑆

𝐹𝑢= {𝑓𝑢1, 𝑓𝑢2, . . . , 𝑓

𝑢𝑛}

𝐹𝑝= {𝑓𝑝1, 𝑓𝑝2, . . . , 𝑓

𝑝𝑛}

𝐹𝑢, 𝐹𝑝∈ 𝐹

(𝑆, 𝐹) in 2D arraycall LemkeHowson()

identify appropriate NE cell from 2D arrayreturn: best strategy 𝑆

LemkeHawson()let 𝑎0denotes label over 𝑥

while (𝑎 0)𝑆𝑢remove 𝑎

0

if (𝑎0== 1)quit loop

in 𝑆𝑝remove 𝑎

0

if (𝑎0== 1)quit loop

return NE

Algorithm 1: Pseudocode of the Nash equilibrium (NE) based trust model.

NEM performs 12% to 40% better than metric based trustand achieves an improvement of 3% to 30% over hard trust.Average betterment ofNEMovermetric based trust is slightlyhigher than 24% and 15% over hard trust. The more thenumber of available resources at the bootload level, the morethe number of services and, hence, the better the service ratioand the higher the throughput.

5.2. Undecidability. Methods cited in the literature over mul-tiple instances fail to evaluate the resources to be trusted oruntrusted for certain resources. As depicted from the experi-ments conducted against hard trust, metric based, and NEM

methods, there are several instances where trust evaluationalgorithms were terminated with undecidability. Metricsbased evaluation will no longer hold, if the requirement doesnot match the resources with specified metrics. There areinstances where the metrics carry null value and thus pavesthe way for undecidability. Hard trust method is paralyzedwhen nomatching semantics weremapped. NEM also suffersfrom undecidability, when the risk parameter is completelyuncertain from user and/or provider. Figure 6 shows at least3% to 17% lesser undecidability with respect to metric basedevaluation, 16% lesser undecidability with respect to hardtrust evaluations, and an average of 13% and 7% lesser


Datacenter

Host 1 Host 2 Host n

Vm 1 Vm 1 Vm 1

Pe 1 Pe 2 Pe n· · ·

· · ·

· · ·

Figure 4: Cloudsim hierarchy.

90

80

70

60

50

40

30

20

10

0

10 20 30 40 50 60 70 80 90 100

Number of resources

Metric basedHard trustNEM

Availability analysis

Num

ber o

f res

ourc

es id

entifi

ed

Figure 5: Trusted resources identified.

undecidability, respectively. It can be observed that the lesserthe number of undecidable resources, the more the numberof evaluations.

5.3. Erroneous Evaluation. The experiment also shows(Figure 7) the erroneous evaluation of resources, whereuntrustworthy resources are reported as trustworthy. Severaluntrusted resources were generated for simulation purposeand tested with all the three methods. In each and every setof resources, from 10 to 100, 0% to 100% of untrustworthyresources were incorporated and the results shown aboveare the aggregation over the instances ranging from 10 to100 set of resources. While all the above algorithms dofalsely evaluate the resources as trustworthy, attenuationof errors in number is achieved through proposed NEMmethod. While the highest number of erroneous evaluationis reported in hard trust method with 40%, only 15% wasreported in NEM and 30% in metric based. Maximum of97% reliability is achieved through NEM and minimum is85%, while 86% and 70% in metric based evaluation and91% and 60% in hard trust evaluation approach. Thus, lessererroneous evaluation improves reliability of the evaluationmethods.


10 20 30 40 50 60 70 80 90 100

Number of resources

0.60

0.50

0.40

0.30

0.20

0.10

0.00

Undecidability

Ude

cida

ble r

esou

rces

pr

obab

ility

Figure 6: Unevaluated resources.


45.00

40.00

35.00

30.00

25.00

20.00

15.00

10.00

5.00

0.00

Erro

r (%

)

10 20 30 40 50 60 70 80 90 100

Number of resources

Erroneous evaluation analysis

Figure 7: Time taken for trust evaluation.

5.4. Accuracy. Figure 8 depicts that a higher level of accuracyis achieved by the NEM method with more than 80%accuracy in 50%of the instances simulated. Simulation resultsin varying number of resources from 10 to 100. Metric basedmethod achieves more than 70%, while metric based methodend up with just above 60%.

5.5. Overhead. To carry out the evaluation of trust over agiven set of new resources, certain tolerable overheads can beincorporated. One such overhead is time taken for decidingthe resource to be trustworthy or untrustworthy. Experimentsconducted exemplifies (Figure 9) that overhead is lesser inNEM than metric based and hard trust based methods.Evaluation of more number of parameters and its associatedcomplexities will result in high time consumption for metricbased method. Depending on database like structure formaintaining and retrieving semantic aggravates the complex-ity in hard trust. Due to the mapping techniques discussedin previous section, lesser amount of time for evaluation isachieved in simulating the specified number of resources.



100.00

90.00

80.00

70.00

60.00

50.00

40.00

30.00

20.00

10.00

0.00

Accu

racy

iden

tified

(%)

Accuracy in resources identification

10 20 30 40 50 60 70 80 90 100

Number of resources

Figure 8: Trusted resources accuracy in percentage (%).


1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

1 2 3 4 5 6 7 8 9 10

Instances

Overhead

Tim

e (m

s)

Figure 9: Trusted resources error in percentage (%).

6. Conclusion

This work proposed a model for evaluating trust for first-time user/provider in cloud environment. It can be furtherextended to any uncertain environment. Apart from mod-elling the problem, simulation was also carried out to validatethe model. The proposed method shows improvement inevaluation with respect to time, reliability, and erroneousevaluation. Cold start problem were addressed to a largerextent by covering maximum number of resources. Risk isstudied as a parameter of direct involvement by user andservice provider. This improves the efficiency by increas-ing throughput, availability, and reliability. At the sametime risk of erroneous evaluation is attenuated to greaterextent by proper mapping of appropriate resource to user.Whitewashing was largely attenuated by making appropriatemapping. Nash equilibrium ensures that neither the usernor the service/resource provider overcomes the service levelagreement (SLA), as the transaction on-goes.

Future direction does exist to improve the proposedmethod for iterated removal of dominated strategies astransaction proceeds further. Thus, a better equilibrium canbe achieved by playing the game with similar set of inputsfrom the second time. This work can further be extendedto solve the uncertain nature of applications and resources,which actually pulls down the performance.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Acknowledgment

The authors would like to thank Anna Centenary ResearchFellowship (ACRF), a funding body for supporting theproject through financial assistance in the form of stipendsand contingencies.

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