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IEEE TRANSACTIONS ON CLOUD COMPUTING 1 Compatibility-aware Cloud Service Composition Under Fuzzy Preferences of Users Amir Vahid Dastjerdi, Member, IEEE and Rajkumar Buyya, Senior Member, IEEE Abstract—When a single Cloud service (i.e., a software image and a virtual machine), on its own, cannot satisfy all the user requirements, a composition of Cloud services is required. Cloud service composition, which includes several tasks such as discovery, compatibility checking, selection, and deployment, is a complex process and users find it difficult to select the best one among the hundreds, if not thousands, of possible compositions available. Service composition in Cloud raises even new challenges caused by diversity of users with different expertise requiring their applications to be deployed across difference geographical locations with distinct legal constraints. The main difficulty lies in selecting a combination of virtual appliances (software images) and infrastructure services that are compatible and satisfy a user with vague preferences. Therefore, we present a framework and algorithms which simplify Cloud service composition for unskilled users. We develop an ontology- based approach to analyze Cloud service compatibility by applying reasoning on the expert knowledge. In addition, to minimize effort of users in expressing their preferences, we apply combination of evolutionary algorithms and fuzzy logic for composition optimization. This lets users express their needs in linguistics terms which brings a great comfort to them compared to systems that force users to assign exact weights for all preferences. Index Terms—Cloud Computing; Cloud Service Composition; Ontology; Service Level Agreement; Quality of Service 1 I NTRODUCTION I N order to deliver their solutions, application ser- vice providers can either utilize the Platform- as-a-Service (PaaS) offerings such as Google App Engine and OpenShift or develop their own host- ing environments by leasing virtual machines from Infrastructure-as-a-Service (IaaS) providers like Ama- zon EC2 or GoGrid. However, most PaaS services have restrictions on the programming language, de- velopment platform, and databases that can be used to develop applications. Such restrictions encourage service providers to build their own platforms using IaaS service offerings. One of the key challenges in building a platform for deploying applications is to automatically compose, configure, and deploy the necessary application that consists of a number of different components. If we consider the deployment requirements of a web appli- cation service provider, it will include security devices (e.g. firewall), load balancers, web servers, application servers, database servers, and storage devices. Setting up such a complex combination of appliances is costly and error prone even in traditional hosting environ- ments [1], let alone in Clouds. Virtual appliances A.V Dastjerdi and R. Buyya are with the Department of Computing and Information Systems, The University of Melbourne, Australia. R. Buyya also serves as a Visiting Professor for the University of Hyderabad, India; King Abdulaziz University, Saudi Arabia; and Tsinghua University, China. provide an elegant solution for this problem. They are built and configured with a necessary operating system and software packages to meet software re- quirements of a user. In IBM smart Cloud, Amazon EC2, GoGrid, Rackspace, and other key players in the IaaS market, users first have to select their software solution (asset catalogs, images, etc.) and then select the proper virtual machine configuration (e.g. instance type) to host the software solution. Furthermore, a user may require more than one virtual appliance and machine, and a composition of them that can meet all the requirements of users is required. However, the se- lection of the best composition is a complex task and none of the providers provide any ranking system to choose the best instance type and software solution for the deployment. In addition, the best choices found for individual appliances cannot be simply put together as they may not be compatible with the hosting environment. For example, if an appliance format is OVF it cannot currently be deployed on Amazon EC2. Moreover, there exist legal constraints imposed by countries such as the USA on importing and exporting of appliances from a provider to another. Dealing with all these complexities is costly and aggravating for unskilled users and encourages them to seek professional help. In this paper, to simplify the process of selecting the best virtual appliance and unit (computing instance) composition, a novel framework is presented. The framework proposes:

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Page 1: IEEE TRANSACTIONS ON CLOUD COMPUTING 1 Compatibility … · 2) Translator: Since Web Service Modeling On-tology (WSMO) is used for service discovery, Cloud services information is

IEEE TRANSACTIONS ON CLOUD COMPUTING 1

Compatibility-aware Cloud ServiceComposition Under Fuzzy Preferences of

UsersAmir Vahid Dastjerdi, Member, IEEE and Rajkumar Buyya, Senior Member, IEEE

Abstract—When a single Cloud service (i.e., a software image and a virtual machine), on its own, cannot satisfy all the userrequirements, a composition of Cloud services is required. Cloud service composition, which includes several tasks such asdiscovery, compatibility checking, selection, and deployment, is a complex process and users find it difficult to select the bestone among the hundreds, if not thousands, of possible compositions available. Service composition in Cloud raises even newchallenges caused by diversity of users with different expertise requiring their applications to be deployed across differencegeographical locations with distinct legal constraints. The main difficulty lies in selecting a combination of virtual appliances(software images) and infrastructure services that are compatible and satisfy a user with vague preferences. Therefore, wepresent a framework and algorithms which simplify Cloud service composition for unskilled users. We develop an ontology-based approach to analyze Cloud service compatibility by applying reasoning on the expert knowledge. In addition, to minimizeeffort of users in expressing their preferences, we apply combination of evolutionary algorithms and fuzzy logic for compositionoptimization. This lets users express their needs in linguistics terms which brings a great comfort to them compared to systemsthat force users to assign exact weights for all preferences.

Index Terms—Cloud Computing; Cloud Service Composition; Ontology; Service Level Agreement; Quality of Service

F

1 INTRODUCTION

IN order to deliver their solutions, application ser-vice providers can either utilize the Platform-

as-a-Service (PaaS) offerings such as Google AppEngine and OpenShift or develop their own host-ing environments by leasing virtual machines fromInfrastructure-as-a-Service (IaaS) providers like Ama-zon EC2 or GoGrid. However, most PaaS serviceshave restrictions on the programming language, de-velopment platform, and databases that can be usedto develop applications. Such restrictions encourageservice providers to build their own platforms usingIaaS service offerings.

One of the key challenges in building a platform fordeploying applications is to automatically compose,configure, and deploy the necessary application thatconsists of a number of different components. If weconsider the deployment requirements of a web appli-cation service provider, it will include security devices(e.g. firewall), load balancers, web servers, applicationservers, database servers, and storage devices. Settingup such a complex combination of appliances is costlyand error prone even in traditional hosting environ-ments [1], let alone in Clouds. Virtual appliances

• A.V Dastjerdi and R. Buyya are with the Department of Computingand Information Systems, The University of Melbourne, Australia.R. Buyya also serves as a Visiting Professor for the University ofHyderabad, India; King Abdulaziz University, Saudi Arabia; andTsinghua University, China.

provide an elegant solution for this problem. Theyare built and configured with a necessary operatingsystem and software packages to meet software re-quirements of a user.

In IBM smart Cloud, Amazon EC2, GoGrid,Rackspace, and other key players in the IaaS market,users first have to select their software solution (assetcatalogs, images, etc.) and then select the propervirtual machine configuration (e.g. instance type) tohost the software solution. Furthermore, a user mayrequire more than one virtual appliance and machine,and a composition of them that can meet all therequirements of users is required. However, the se-lection of the best composition is a complex task andnone of the providers provide any ranking system tochoose the best instance type and software solutionfor the deployment.

In addition, the best choices found for individualappliances cannot be simply put together as they maynot be compatible with the hosting environment. Forexample, if an appliance format is OVF it cannotcurrently be deployed on Amazon EC2. Moreover,there exist legal constraints imposed by countries suchas the USA on importing and exporting of appliancesfrom a provider to another. Dealing with all thesecomplexities is costly and aggravating for unskilledusers and encourages them to seek professional help.

In this paper, to simplify the process of selecting thebest virtual appliance and unit (computing instance)composition, a novel framework is presented. Theframework proposes:

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• An approach to help non-expert users with lim-ited or no knowledge on legal and virtual appli-ance image format compatibility issues to deploytheir services flawlessly. For this purpose, we firstautomatically build a repository of Cloud servicesin Web Service Modeling Language (WSML)[2] and then enrich it with experts’ knowledge(lawyers, software engineers, system administra-tors, etc.) on the aforementioned constraints. Theknowledgebase then is used for reasoning in analgorithm that identifies whether a set of Cloudservices consisting of virtual appliance and unitsare compatible or not.

• A Cloud service composition optimization tech-nique that allows non-expert Cloud users to settheir preferences using high level if-then rulesand get user friendly recommendations on thecomposition solution’s prominence. The majorityof end users avoid systems that incur complex-ity in capturing their constraints, objectives andpreferences. An example of such systems is theone which require users to assign weights totheir objectives. In this case, users have to finda way to prioritize their preferences and thenmap them to weights. After that, the systemhas to find out how precise users have gonethrough the process of weight assignment. Totackle this issue, a major objective of this researchis to offer ranking system for Cloud service (i.e.virtual appliance and machine) composition thatlet users express their preferences convenientlyusing high-level linguistic rules. Our system thenutilizes multi-objective evolutionary approachesand fuzzy inference system to precisely capturethe entered preferences for ranking purpose.

2 ARCHITECTURE

Our proposed architecture offers a unified solutionthat uniquely applies state of the art technologiesof semantic services, agent negotiation, and multi-objective and constraints optimization to satisfy therequirements of whole service deployment life cycle.The main goal of the architecture is to provide: easeof use for non-experts, semantic interoperability, moreprecise discovery and selection, more reliable ServiceLevel Agreement (SLA) monitoring, and automaticnegotiation strategy. The proposed architecture is de-picted in Figure 1 and its main components are de-scribed below:

1) User Portal: All services provided by the systemare presented via the web portal to clients. Thiscomponent provides graphical interfaces to cap-ture users’ requirements such as software, hard-ware, QoS requirements (including maximumacceptable latency between tiers, minimum ac-ceptable reliability, and budget), firewall, andscaling settings. In addition, it transforms user

Appliance Service

Repository

Virtual Unit Service

Repository

Software, QoS Requirements,Security constraints, andDeployment pattern

1

Composition Optimizer

Image Packaging DecommissioningFailure

Recovery

PlanningDeployment

Descriptor Manager

Monitoring and

SLA Management

Discovery &SLA Negotiation

Discovery Negotiator

Account Manager

Appliance Administration

Service

User Portal

Tra

nsl

ato

r

Tra

nsl

ato

r

m Service contracting and deployment management

l Deployment optimization

k Building semantically rich Cloud service repository

k Building semantically rich Cloud service repository

j Request

Infrastructure as a Service Providers

Fig. 1: Architecture’s main components that facilitatesQoS-aware deployment of user applications.

requirements to WSMO format in the form ofgoals which are then used for Cloud servicediscovery and composition. For more detailsregarding the format of goals, readers can referto our previous work [3]. Moreover, it containsan account manager, which is responsible foruser management.

2) Translator: Since Web Service Modeling On-tology (WSMO) is used for service discovery,Cloud services information is translated to WebService Modeling Language (WSML) format bythe Translator component. This component takescare of building and maintaining an aggregatedrepository of Cloud services and is explained indetail in Section 2.1.

3) Cloud Service Repositories: They are repre-sented by appliance and virtual unit servicerepositories in Figure 1 and allow IaaS providersto advertise their services. For example, an ad-vertisement of a computing instance can con-tain descriptions of its features, costs, and thevalidity time of the advertisement. From stan-dardization perspective, a common metamodelthat describes IaaS provider’s services has to becreated. However, due to the lack of standards,we developed our own metamodel [3] based onprevious works.

4) Discovery and Negotiation Service: This com-ponent maps user’s requirements to resourcesusing the ontology-based discovery technique.It acts in user’s interest to satisfy quality ofservice (QoS) requirements by selecting the setof eligible IaaS providers. The negotiation ser-vice uses a time-dependent negotiation strategythat captures preferences of users on QoS cri-teria to maximize their utility functions whileonly accepting reliable offers. These negotiation

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strategies are described in our previous work [4].5) Composition Optimizer: Once the negotiation

completed and eligible candidates are identified,the composition component, which is the focusof this paper, builds the possible compositionsand uses the compatibility checking compo-nent to eliminate incompatible candidates. ThenComposition Optimizer evaluates the composi-tion candidates using the users’ QoS preferences.The Composition Optimizer takes advantage ofmulti-objective algorithm and fuzzy logic to letusers express their preference conveniently, andefficiently selects the closest candidates to users’interests. Throughout the paper we show howontology-based compatibility checking, multi-objective algorithms, and fuzzy logic techniquescan work in harmony to provide an elegantsolution to the composition problem.

6) Planning: The planning component determinesthe order of appliance deployment on the se-lected IaaS providers and plans for the deploy-ment in the quickest possible manner.

7) Image Packaging: The Packaging componentbuilds the discovered virtual appliances and therelevant meta-data into deployable packages,such as Amazon Machine Image (AMI) or OpenVirtualization Format (OVF) [5] packages. Thenthe packages are deployed to the selected IaaSprovider using the deployment component.

8) Deployment Component: It configures and setsup the virtual appliances and computing in-stances with the necessary configurations suchas firewall and scaling settings. For example ina web application, specific connection detailsabout the database server need to be configured.

9) Deployment Descriptor Manager: This com-ponent persists specifications of required ser-vices and their configuration information suchas firewall and scaling settings in a formatcalled Deployment Descriptor. Besides, it in-cludes the mapping of user requirements to theinstances and appliances provided by the Cloud.The mapping includes instance description (e.g.name, ID, IP, status), image information, etc. Thismeta-data is used by the appliance administra-tion service to manage the whole stack of cloudservices even if they are deployed across mul-tiple Clouds. Formally described using WSML,the Deployment Descriptor is located in oursystem (as a third party service coordinator),and in a Cloud-independent format that is usedfor discovering and configuring alternative de-ployments in case of failures. An example of aDeployment Descriptor is shown in AppendixB (supplemental material). It identifies how fire-wall and scaling configurations have to be setfor Web server appliances. In addition, Deploy-ment Descriptor helps to describe the utility

function of users for provisioning extra Cloudservices when scaling is required. This helps tocreate scaling policies that utilize the optimiza-tion component on the fly to provision servicesthat maximizes the user’s utility functions. Forexample, providers that have the lowest price,latency, and highest reliability are going to beranked higher.

10) Appliance Administration Service: After thedeployment phase, this component helps endusers to manage their appliances (for examplestarting, stopping, or redeploying them). It usesthe Deployment Descriptor to manage the de-ployed services.

11) Monitoring and SLA Management: This com-ponent provides health monitoring of deployedservices and provides required inputs and datafor failure recovery and scaling. A monitoringsystem is provided by this component for fairlydetermining to which extent an SLA is achieved.More details on this component is provided inour previous paper [6].

12) Failure Recovery: It automatically backs up vir-tual appliance data and redeploys them in theevent of Cloud service failure.

13) Decommissioning: In the decommissioningphase, Cloud resources are cleaned up and re-leased by this component.

14) IaaS Providers: They are in both fabric and uni-fied resource level [7] and contain resources thathave been virtualized as virtual units. Virtualunit can be a virtual computer, database system,or even a virtual cluster. In addition to virtualunits, they offer virtual appliances to satisfysoftware requirements of users.

The first step in service composition is modeling theCloud services (based on cost, size, functionality, etc.)and QoS requirements of users. This step not onlyallows appliance and virtual unit providers to adver-tise QoS of their services, but also provides a wayfor end users to express their QoS preferences. In thiswork, WSMO is extended and used for appliance andvirtual unit QoS modeling. This allows us to modelcompatibility and legal constraints required to builda valid composition. However, the process of con-verting the Cloud service advertisements to WSMLis time-consuming and error-prone if it is carried outmanually. Automatic construction of WSMO-basedservice description from IaaS service advertisementsis described in the next section.

2.1 Construction of Semantic Cloud Services

Currently, there is no integrated repository ofsemantic-based services for virtual appliances andunits. The first step towards describing services andtheir QoS is to communicate with Clouds and theCloud monitoring services through their APIs and

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Cloud’s API call to acquire virtual appliances and units

information

Translator

Component

Monitoring service’s API call to acquire QoS Information of Cloud services

Repository of

semantically rich

Cloud services

Translator component automatically lifts functional and non-functional properties of Cloud services to ontology

instances

Sync

Component Integrity Check

Fig. 2: The process of translation of the virtual appli-ances and units descriptions to WSML.

gather required meta-data for building the repository.The process of metadata translation is demonstratedin Figure 2. The components involved in this processare:

2.1.1 Integrity CheckingThis component first merges output messages of APIcalls for acquiring Cloud services description us-ing Extensible Stylesheet Language Transformations(XSLT)1 and then compares them with the previouslymerged messages using a hash function. If the outputsof the hash function are not equal, the componenttriggers the Sync component to update the semanticrepository.

2.1.2 Sync ComponentThe goal of this component is to keep the semantic-based repository consistent with the latest metadataprovided by Cloud providers. As the synchronizationis computing intensive, it is avoided unless the in-tegrity checking component detects any inconsistency.It receives the output message that is required forsynchronization and finds the corresponding seman-tically rich services and updates them with the outputof the translator component.

2.1.3 Translator ComponentDuring the communication of a semantic-level clientand a syntactic-level web service, two directions ofdata transformations (which is also called grounding)are necessary: the client semantic data must be writtenin an XML format that can be sent as a request tothe service, and the response data coming back fromthe service must be interpreted semantically by theclient. We use our customized Grounding techniqueon WSDL operations (that are utilized to acquirevirtual appliance and unit metadata) output to seman-tically enrich them with ontology annotations. WSMOoffers a package, which utilizes Semantic Annotationsfor WSDL (SAWSDL) for grounding [8]. It provides

1. XSLT. http://www.w3.org/TR/xslt

two extensions attribute namely as Lifting SchemaMapping and Lowering Schema Mapping. LoweringSchema Mapping is used to transfer ontology to XMLand lifting Schema Mapping does the opposite. In ourtranslator component, the lifting mapping extensionhas been adopted to define how XML instance datathat is obtained from Clouds API calls is transformedto a semantic model.

As the first step in grounding, from output messageschema, the necessary ontology is created for virtualunits and appliances. The basic steps to build theontology from XML schema using WSMO ground-ing is explained by Kopecky et al. [8]. In this stepour contribution lies on building the ontology frommultiple output message schemas. It means that themonitoring service output message schema is usedto extend the ontology to encompass non-functionalproperties. This can be accomplished by merging twoschemas to construct an output message that describesthe format of the elements that has functional andnon-functional properties such as price and reliability.

Having the ontology available, the next step isto add the necessary Mapping URI for all elementdeclarations. For this purpose Modelreferences areused, which are attributes whose values are lists ofURIs that point to corresponding concepts in theconstructed ontology. Subsequently, we need to addschema mappings that point to the proper data lift-ing transformation between XML data and semanticdata. For this purpose, two attributes, namely lift-ingSchemaMapping and loweringSchemaMapping,are offered by SAWSDL. These aforementioned at-tributes are then utilized to point from Cloud virtualappliance meta-data schema to a XSLT, which showshow meta-data is transferred from XML to WSML.

We tested this approach for Cloud service reposito-ries with variety of sizes, and will present the exper-imental result in Section 5.2.2. The ontology listed inAppendix A (supplemental materials) was partiallycreated by the described translator component. Forexample, it shows how an appliance meta-data withID of "aki00806369" has been translated to WSMOformat.

Semantic service toolkits and libraries based onOWL-S and WSMO use XML based grounding. ThisXML mapping approach cannot deal with the grow-ing number of Cloud provider’s interfaces that usenon-SOAP and non-XML services. The main reasonthat we have used XML is to follow the path thatwas suggested by WSMO, standard libraries and doc-umentation provided by WSMO, and that major IaaSproviders currently have a full support for XML basedservices. For alternative approaches of grounding fornon-XML services readers can refer to studies con-ducted by Lambert et al. [9].

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3 EVALUATION OF COMPOSITION CRITERIA

The composition problem is to find the best combi-nation of compatible virtual appliances and virtualmachines that minimizes the deployment cost anddeployment time, and maximizes the reliability whileadhering to composability constraints. A formal de-scription of the problem is given below.

3.1 Provider and User Request ModelLet m be the total number of providers. Each providercan provide virtual appliances, virtual units or both,and is represented as shown in Equation (1).

Provider Pk : {{a} , {vm} , Cdext , Cdint}where 0 < k ≤ m

(1)

where a, vm, Cdext , Cdint denotes appliance, virtualmachine, Cost of external data transfer and Cost ofinternal data transfer respectively. A virtual appliancea can be represented by a tuple of five elements(Equation (2)): appliance type, cost, license type, com-patibility list, and size.

a : {ApplianceType, Cost, LicenseType,CompatibilityList, Size}

(2)

A virtual machine vm can be formally described as atuple with two elements as shown in Equation (3).

vm : {MachineType, Cost} (3)

The user request for the appliance composition canbe translated into a weighted graph G (V,E) whereeach vertex represents a server that consists of avirtual appliance running on a virtual machine. Servercorresponding to vertex v is represented by Equation(4).

Sv = {av, vmv} ,∀ v ∈ V (4)

Each edge e {v, v′} ∈ E indicates that correspondingservers communicate. The Data Transfer Rate betweenthese connected vertexes (i.e. one server to another) isgiven by the weight associated to edge e.

3.2 CompatibilityWhen multiple Cloud services (i.e. virtual appliancesand units) are composed together, they should becompatible with each other. In this work, we considerlegal and image format compatibility constraints.However, it should be noted that in reality there willbe other compatibility constraints such as compatibil-ity between the products installed on the appliances.• Virtual appliance image format compatibility:

Before we finalize the deployment plan, we haveto find out whether the image formats of cho-sen set of virtual appliances are compatible withthe destination virtual unit provider. As it is

illustrated in the first row of Table 1, a sampleof ontology-based reasoning on the built knowl-edgebase (as shown in Appendix A) shows thatimage with the ID of "aki00806369" is compatiblewith the large instance type provided by AmazonEC2.

• Legal requirements: In Cloud infrastructure, vir-tual machines can be deployed in data centerslocated in different parts of the world. However,there are legal requirements, for example USrestrictions on exporting encryption technology[10], that prevent the export and deployment ofsoftware developed in one country to another.Hence, we need to ensure that the virtual ap-pliances can be legally deployed on the selectedvirtual units. The second row of the Table 1presents a query which sets appliance with the IDof "aki00806369" compilable to only virtual unitsprovided by Clouds located in a same countrywhere the appliance provider is situated.

To evaluate the compatibility requirements, first theOntology-based vocabulary is created using WSML,as shown in the Appendix A [line 225], where com-patibility constraints are imposed by experts in theform of axioms in the ontology or alternatively byreasoning on the ontology. For example, the ontologylisted in Appendix A shows how an axiom (set by anexpert) enforces that appliances can only be deployedon virtual units provided by Cloud providers that arelocated in the same country as the appliance provider.After building the required ontology, we can exploitthe advantages of Description Logic (DL) to query theknowledgebase and check compatibility constraints ofcomposition candidates. A WSML-DL query sample,given in Table 1, shows which virtual unit is com-patible (legally and regarding image format) to theappliance with the ID of "aki00806369". Our objectiveis to achieve full compatibility among the appliancesin the composition. Based on the compatibility con-straints considered in our work, the compatibility (C)can be calculated based on Equation (5).

C =

0 if there exists at least one pair ofincompatible services;1 otherwise.

(5)

In addition, Algorithm 1 illustrates the process to eval-uate the compatibility of the services in a composition.The algorithm checks the compatibility constraint (bysending the related query to WSML-reasoner) for eachpair of appliance and virtual unit in the composition,and only compositions where all constraint queriesare satisfied would be returned as valid. We useWSMO discovery component for service discoveryand implemented the Algorithm 1 to discard sets ofincompatible discovered services.

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TABLE 1: Compatibility reasoning for Cloud service composition.

Query String Result?x[isCompatipleWith hasValue ?y] :- ?x memberOfvirtualAppliance and ?x [hasName hasValue ?v] and?v=aki00806369 and ?y memberOf virtualUnit and?y[hasProvider hasValue?pvu] and ?pvu [supportVmFormathasValue ?supportedFormat] and ?x[hasFormat hasValue?format] and ?format[hasName hasValue ?formatName]and ?supportedFormat[hasName hasValue ?supportedFor-matName] and ?supportedFormatName=?formatName.

?formatName: "AMI" ?v:"aki00806369" ?pvu: Amazon-California ?p: AmazonCalifornia?supportedFormatName: "AMI"?format: AMI ?supportedFormat:AMI ?y: largeInstance ?x:aki00806369

?x [isCompatipleWith hasValue ?y]:- ?x memberOfvirtualAppliance and ?x [hasName hasValue ?v] and?v=aki00806369 and ?x[hasProvider hasValue ?p] and?y memberOf virtualUnit and ?y[hasProvider hasValue?pvu] and ?p[hasCountry hasValue ?capp] and ?pvu[hasCountry hasValue ?cvu] and?capp[hasName hasValue?cappName] and ?cvu[hasName hasValue ?cvuName] and?cappName=?cvuName.

?v: "aki00806369" ?pvu: Ama-zonCalifornia ?p: AmazonCalifornia?capp: USA ?cappName: "USA"?cvu: USA ?cvuName: "USA" ?y:largeInstance ?x: aki00806369

Input: Composition c, Constraint List (cl) such asImage Format and Legal Compatibility

Output: Composition Validityif CompositionValidity(c, cl) Exists in Cache then

ValidComposition =GetCompositionValidityFromCache(c, cl);

endValidComposition=True;foreach Appliance a and Virtual Unit vu In c do

foreach Constraint t in cl doif Compatibility (t, a, vu) Exists in Cachethen

ValidComposition =GetCompatibilityFromCache(t, a, vu);

endelse

ValidComposition =CheckCompatibilitybyReasoning(t, a,vu);

endInsertCompatibilitytoCache(t, a, vu);

endif ValidComposition=False then

break;end

endInsertCompositionValiditytoCache(c, cl);return ValidComposition;

Algorithm 1: Compatibility evaluation algorithm

3.3 Cost

The costs involved in procuring and using virtualappliances can be categorized as follows:

• Acquisition Cost: Costs involved in purchasingthe virtual appliance, such as licensing cost, costof the virtual machine and any costs associatedwith deployment such as the data transfer coststo transfer the appliances to the virtual machineat the IaaS provider.To build a server Sv , let us assume appliance avis obtained from provider Pk and virtual machinevmv is obtained from provider Pl. If this serverrun for a duration of T (lease period), the Equa-tion (6) shows the Acquisition Cost.

AqCost (Sv) = Cost (av,Pk)× T+

Cost (vmv,Pl)× T +AppTransCost (k, l)

(6)

where Cost (av,Pk) is the cost of appliance per

unit of time, Cost (vmv,Pl) is the cost of virtual

machine per unit of time, and the cost of appli-ance transfer from appliance provider k to virtualunit provider l is given by Equation (7).

AppTransCost (k, l) =

0 if k = l;Size (av,Pk

)×Cdext (Pl) if k 6= l.

(7)

• Ongoing Cost: This will include the costs ofrunning the virtual appliance, such as the cost ofdata transfers. In this work we consider only thecosts associated with data transfers as ongoingcosts.Let v, v′ be two vertexes (servers) on provider l,connected by edge e {v, v′}. The data transfer cost

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between the two servers is given by Equation (8):

TransCost (e {v, v′}) = Size(Datae{v,v′}

)× Cdint

(8)• Decommissioning Cost: Decommissioning cost

primarily includes archival and removal costs ofthe data at the end of the application life cyclesuch as the data sanitisation, and henceforth asshown in Equation (9) will depend on the size ofthe data stored. The amount of data stored willvary from server to server.

DecomCost (Sv) = CostPerUnit× SizeOfDatav(9)

Based on the costs calculated above, Total Cost (TC)can be computed as shown in Equation (10),

TC =∑v∈V

AqCost (Sv) +∑

e∈Ev,v′∈V

TransCost (e {v, v′})

+∑v∈V

DecomCost (Sv)

(10)

3.4 Deployment TimeVirtual appliances significantly minimize the time re-quired to build and configure the necessary indepen-dent components. However, size of virtual appliancesranges from a few megabytes to tens of gigabytesdepending on the applications installed on them andwill impact the time required to transfer and deploythe appliances from the appliance provider to thevirtual machine provider. Hence, we consider thedeployment time as one of the composition objectives.The time required to deploy a given appliance avobtained from provider Pk on a virtual machine vmv

obtained from provider Pl is given by Equation (11),

DT (av,Pk, vmv,Pl

) =

InitT ime (vmv,Pl

) if k = l;InitT ime (vmv,Pl

) +Size(av,Pk)

DataTransferRate(Pk,Pl)if k 6= l.

(11)

where InitT ime (vmv,Pl) is the time required to ini-

tialize the appliance on the virtual unit. As appliancescan be deployed in parallel, Total Deployment Time(TD) will be given by Equation (12),

TD = Max {DT (av,Pk, vmv,Pl

)} (12)

3.5 ReliabilityFor measuring Cloud providers reliability, we intro-duce SLA Confidence Level (SCL), which is a metricto measure how reliable services of each provider arebased on the SLAs and their performance history.SCL values are computed by a third party that is

responsible for monitoring the SLA of providers basedon Equation (13):

SCL =

q∑j=1

(Ij × SCLj) (13)

Where the SCLj is SLA confidence level for QoScriteria j of a Cloud service; Ij is the importance ofthe criteria j for the user; q is the number of monitoredQoS criteria.

We utilized the beta reputation system [11] to assessthe SCL for each criterion. The reason is that theMonitoring Outcome (MOjt) of a particular qualityof service criteria j in the period of t in the SLAcontract can be modeled as shown in Equation (14),and therefore it is a binary event. Consequently, thebeta density function, which is shown in Equation(15), can be efficiently used to calculate posterioriprobabilities of the event. As a result, the mean orexpected value of the distribution can be representedby Equation (16).

MOjt = {SLAnotviolated, SLAviolated} (14)

f (x|ρ, τ) =Γ (ρ+ τ)

Γ (ρ) Γ (τ)xρ−1 (1− x)

τ−1

where 0 ≤ x ≥ 1, ρ < 0, τ > 0, and ρ and

τ are beta distribution parameters.

(15)

µ = E (x) = ρ/ (ρ+ τ) (16)

As mentioned earlier in Section 2, in our architec-ture a component is responsible for monitoring SLAcontracts. If we assume that the monitoring compo-nent has detected that SLA violation occurred v timesfor provider of p (for total number of n monitoredSLAs). Consequently, considering that p = n − v + 1τ = v + 1 and , the SCL is equal to the probabilityexpectation that the SLA is not going to be violatedand is calculated as shown in Equation (17).

SCLj =n− v + 1

n+ 2(17)

We modeled availability for SCL generation, as cur-rent Cloud providers only include "availability": intheir SLAs. The reliability in our work is consideredas a user constraint for each Cloud service.

3.6 Overall ObjectivesThe final objective is to find a fully compatible servicecomposition that minimizes the deployment cost andtime, and improves the reliability while adhering tocompatibility constraints as shown in Table 2.

4 COMPOSITION OPTIMIZATIONIn our problem we consider three user objectives,the lowest cost, quickest deployment time, and the

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TABLE 2: Virtual appliance composition objectives.

Criteria Metric Type RequirementCompatibility (C) C Constraint To be equal to 1Total Cost (TC) $ Objective To be minimizedTotal Deployment Time (TD) mS Objective To be minimizedTotal Reliability (TR) SCL Objective To be maximized

Fuzzy Ranking

EngineMulti-objective

Fitness

Evaluation

Engine

User

Preference

s

Pareto

Front

List

Ranked Pareto

Front List

App1

Y

App 1:

Candidate

List

App2

App3

Appn

IaaS 1:

Candidate List

IaaSVa

ria

ble

s (

Gen

es)

Fig. 3: Cloud service composition optimization approach.

highest reliability. This makes it infeasible to find anoptimal composition as these objectives can conflictwith each other. One way to address this problemis to convert the appliance composition problem toa single-objective problem by asking users to giveweights for all the objectives. However, this approachis error-prone and impractical, as not all the usershave the knowledge to accurately assign weights toobjectives. Furthermore, since the composition solu-tions will depend on the capability of users to assignproper weight to the objectives, additionally we haveto find a way to evaluate the knowledge of usersabout each objective to ensure the accuracy of theapproach. As shown in Figure 3, we have tackledthese challenges in two steps:

• First, we find the Pareto front [12] composi-tion solutions using different multi-objective al-gorithms (OMOPSO, NSGA-II, and SPEA-II). Wehave used Jmetal [13] for this purpose which al-lows us to define problems by defining each gene(variable) to point to Cloud service candidates asshown in Figure 3. Then, we defined necessaryfitness functions for all the defined objectives andchoose the algorithm to solve the problem.

• Second, we help users to describe their pref-erences using high level "if-then" rules, whichbuilds our fuzzy engine rule-base to rank thePareto front acquired from the previous step. Bydoing this we acquire Pareto front once and thenfilter it according to the user preferences. This hasa great advantage when compared to the methodoffered in [14] as our method does not requireto search the solution space each time the userpreferences change.

Evolutionary algorithms have been effectively ap-plied for solving optimization problems. Among themNSGA-II [15] and SPEA-II [16] outperform manyother genetic optimization algorithms [15]. Neverthe-less, recently other meta-heuristics that work basedon swarm intelligence such as Particle Swarm Opti-mization (OMOPSO) [17] are also used to tackle multi-objective optimization problems. In this work, weperform a comparative study among the algorithmsNSGA-II, SPEA-II, and OMOPSO for the appliancecomposition problem to determine which of them willbe suitable for our problem.

Fuzzy logic based ranking: Our proposed fuzzyinference engine includes three inputs and one output.Inputs of the system are normalized DeploymentTime (DT), Deployment Cost (DC), and Reliability ofcomposition, which are all described based on thesame membership functions in Figure 4a. Output ofthe fuzzy engine as shown in Figure 4b representshow desirable the current set of inputs are based onthe fuzzy rule-based indication. It shows the mem-bership function for output by which we allow thegradual assessment of the membership of elementsin a set. For example, the value "0" in output meansthe solution is highly undesirable whereas the value"1" shows that the solution is highly desirable. Fuzzyrules should be defined by the user to describe theirpreferences. For example a rule can be defined as:

if DT is low and DC is low and Reliability is high,composition is highly desirable.

Table 3 shows sample rules that can be expressedby users. In this work we use a fuzzy engine based onMamdani inference system [18] with Centroid of areadefuzzification strategy. Readers can refer to [19] fordetailed information on fuzzy inference systems. Once

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TABLE 3: Sample high level rules set by users.

DC DT Reliability Composition DesirabilityLow Low Low UndesirableLow Low High Highly DesirableHigh Low High Not sure

rules and defuzzification strategy are defined, a fuzzyinference system can map the inputs of fuzzy engineto its output. Figure 5 demonstrates how deploymenttime, cost, and reliability are mapped to compositiondesirability when all rules are set.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.2

0.4

0.6

0.8

1

Deg

ree

of M

embe

rshi

p

Low Mid High

(a) Input fuzzy sets.

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

Deg

ree

of M

embe

rshi

p

Highly Undesirable Undesirable Neutral Desirable Highly Desirable

(b) Output fuzzy sets.Fig. 4: Fuzzy engine input and output fuzzy sets.

5 PERFORMANCE EVALUATIONIn order to realize and evaluate the proposed ap-proach, a number of components and technologies areutilized. Most importantly, multi-objective algorithmsare implemented in jMetal [13]. After that, ParetoFront composition solutions from Jmetal have beenpassed to our fuzzy-logic based ranking components,which utilizes jFuzzyLogic [20] to define the member-ship functions and rules.

5.1 Case Study and Data CollectionWe consider a case study of a web-based collabo-ration application for evaluating performance. Theapplication allows users to store, manage, and sharedocuments and drawings related to large constructionprojects. The service composition required for thisapplication includes: Firewall (x1), Intrusion Detection(x1), Load Balancer(x1), Web Server (x4), ApplicationServer (x3), Database Server (x1), Database ReportingServer (x1), Email Server (x1), and Server Health Mon-itoring (x1). To meet these requirements, our objectiveis to find the best Cloud service composition.

00.2

0.40.6

0.81

00.2

0.40.6

0.81

0.1

0.15

0.2

0.25

0.3

0.35

0.4

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0.5

DTDC

Com

posi

tio

n D

esir

ab

ilit

y

(a) Mapping of DT and DC to composition desirabil-ity.

00.2

0.40.6

0.81 0

0.20.4

0.60.8

10.1

0.2

0.3

0.4

0.5

0.6

0.7

ReliabilityDT

Com

posi

tio

n D

esir

ab

ilit

y

(b) Mapping of DT and reliability to compositiondesirability.

00.2

0.40.6

0.81 0

0.20.4

0.60.8

1

0.1

0.2

0.3

0.4

0.5

0.6

0.7

ReliabilityDC

Com

posi

tion

Des

irab

ilit

y

(c) Mapping of DC and reliability to compositiondesirability.

Fig. 5: Mapping from QoS criteria values to composi-tion desirability value.

5.1.1 Cloud Service Provider Information

Cloud service providers details, such as the promisedavailability and monitored availability (for calculat-ing SCL), data transfer costs, and data throughputbetween two Cloud service providers (to estimate thetransfer time of virtual appliances) are obtained usingthe CloudHarmony service2. When the required datais not available from them, it is directly obtained fromthe Cloud service provider. Similar to the virtual unitand appliance directory, the module to obtain theCloud service provider information also can be usedto update and extend the information in future.

2. CloudHarmony. http://cloudharmony.com/

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5.2 ResultsThere are three main experiment results presented inthis section: 1) an investigation of performance of thetranslator component, 2) a performance comparisonbetween the OMOPSO, NSGA-II, and SPEA-II algo-rithms for the real case study, and 3) examination ofthe effectiveness of fuzzy inference system for han-dling imprecise user preferences. NSGA-II and SPEA-II algorithms use a population of size of 100, andmaximum number evaluations of 25000. OMOPSO isconfigured with 100 particles, with a maximum of 100leaders and maximum number of iterations of 250. Wehave carried out 40 independent runs per experimentand then statistically analyzed results.

5.2.1 Performance of the Translation ApproachMajor Cloud providers have large repository of virtualappliance and unit services. For example, size of Ama-zon Web Service repository3 alone is greater than 10.6megabytes. To increase the efficiency of the translationapproach we only synchronize when the translationservice is triggered by integrity checking component.We increased the number of services in the repositoryby merging repositories from various Cloud providersto investigate the scalability of our approach in termsof execution time needed for the translation. Foreach case of repository size, we repeated the exper-iment 30 times and the results are plotted in Figure6. Regression analysis shows that there is positiveand linear relationship between the repository sizeand the translation time. The evidence confirms thatthe regression coefficient is 0.6621, which suggeststhat if the data size to be translated increases by amegabyte, translation time increases roughly by 0.6second. Consequently, the synchronization functioncan be executed online in an acceptable time even if aconsiderable percentage of virtual appliance and unitproperties are updated.

5.2.2 Performance of the Optimization ApproachPerformance of single-objective optimization algo-rithms can be evaluated by analyzing the best valueachieved by the algorithms. However, in the case ofmulti-objective optimization, it is not practical. Thereare quality indicators, which can be used to evalu-ate the quality of the obtained set of solutions, anddetermine the convergence and diversity propertiesof algorithms. In our experiments, the algorithms arecompared using Execution Time; Inverse GenerationalDistance (IGD) [16], which determines convergence ofalgorithms; Spread, which determines diversity [21];and Hypervolume, which determines both. In addi-tion, it has been complemented by the application ofstatistical tests to ensure the significance of the results.

3. The virtual appliance repository can be obtained by callingDescribeImages service from S3.amazonaws.com/ec2-downloads/ec2.wsdl

Fig. 6: Execution time of translation for differentrepository sizes.

Otherwise, the drawn conclusions may be incorrectas the differences between the algorithms could haveoccurred by chance. As in our problem the Paretofronts are not known, applying the aforementionedquality indicators is not possible. To tackle this issueand as a common approach, we build a referencefront by collecting all the results of 100 runs of thealgorithms. This helps us to compare the relativeperformance of algorithms (Jmetal [13] provides anautomatic way of obtain the reference front).

We start with describing the Hypervolume [22]indicator which has been widely used and calculatesthe volume of dominated portion of the objectivespace. The indicator is strictly Pareto-compliant [23]which means if it values a solution higher than theother, then that solution set dominates the other one’s.In addition to Hypervolume, we have also used othertwo widely used and recommended indicators [21],namely IGD and spread. As shown in Equation (18),IGD works out the average distance of the obtainedsolution points from the optimal Pareto fronts (refer-ence front in our case). Where the di is the Euclideandistance between the obtained solution points and theclosest member of optimal Pareto front and n is thenumber of points in the optimal Pareto front. Hence,when the achieved solution is in the optimal Paretofront the IGD is equal to 0.

IGD =

√∑ni=1 di

2

n(18)

In addition, spread is a metric to calculate thebroadness and calculated based on Equation (19).

Spread =df + dl +

∑N−1i=1

∣∣di − d∣∣df + dl + (N − 1)d

(19)

where di is the Euclidean distance between consec-utive solutions in the obtained solutions. df and dlare the Euclidean distance between the boundary so-lutions of the obtained pareto front set. When spreadis equal to zero, it means that the obtained solutions

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TABLE 4: Statistical comparison of algorithms.

95% CI for Dif-ference

ANOVA Test

Execution time Spread IGD HypervolumeNSGA-II-SPEA-II (-2469.5,-2579.4) (0.0476,0.1820) Not Significant Not SignificantNSGA-II-OMOPSO (420.4, 531.1) (0.2002,0.3355) (0.003222,0.009074) (0.00599,0.01848)SPEA-II-OMOPSO (2944.8, 3055.5) (0.0854,0.2206) (0.002913,0.008765) (0.00372,0.01620)

are well diverse.The spread, IGD, Hypervolume, and execution time

of three algorithms are compared using Analysis ofVariance (ANOVA) test, as the quality values arenormally distributed and there is no strong evidenceto indicate that variance is not constant. From theANOVA table the P-value is less than 0.001, whichstrongly suggests that there are differences in meanspread, IGD, Hypervolume, and execution time be-tween the algorithms. The algorithm that obtainssmaller spread is capable of acquiring a set of non-dominated composition solutions with better diver-sity. In addition, if an algorithm achieves smallervalue for IGD, it is better in converging to Pareto-optimal front. However, algorithms with larger valueof Hypervolume are more desirable.

In our experiment, when Hypervolume is usedfor comparison, OMOPSO outperforms NSGA-II andSPEA-II as shown in Table 4 and Figure 7b. In ad-dition, if spread is used for diversity comparison,as shown in Table 4 and Figure 7c, 95% confidenceinterval (CI) for the difference between NSGA-II -OMOPSO and SPEA-II - OMOPSO are (0.2002, 0.3355)and (0.0854, 0.2206) respectively. This shows the bettersuitability of OMOPSO in achieving higher diversity.In addition, SPEA-II outperforms NSGA-II in terms ofSpread. Moreover, as Figure 7a illustrates OMOPSOhas the lowest execution time. Furthermore NSGA-IIperforms better than the SPEA-II in terms of executiontime. For the convergence comparison, IGD has beenconsidered. As shown in Figure 7d and in Table 4,95% CI for differences between NSGA-II - OMOPSOand SPEA-II - OMOPSO are reported as (0.003222,0.009074) and (0.002913, 0.008765) respectively, whichshows OMOPSO is better in converging to Pareto-optimal front.

Figure 8a shows the output of NSGA-II for thecase study appliance composition without consideringuser preferences. In this case users would receive a setof non-dominated composition solutions. It indicatesthat all the composition solutions have their ownoutstanding characteristics and none of the pointscould dominate the others.

In reality, Cloud users at least have some vagueidea regarding their objectives which can be capturedby asking them to set high level linguistic rules.To emulate the user behavior, in our experiment,27 sample fuzzy rules are designed similar to theone described in Section 4. Based on these rules, byapplying fuzzy inference system, we mapped points

in Figure 8a to a number between 0 (least desirable)and 1 (most desirable). That number was used to colorthe points in a way that if the composition solution ismore appealing to the user, the corresponding pointis darker. As shown in Figures 8b and 8c when thenumber of preferences defined by the user using theif-then rule increases, the solution’s prominence alsobecomes clearer. As it can be seen in Figure 8d, whenall the 27 rules are set (none of them set as "not sure"),the majority of points have distinct colors, howeveras the number of rules set to "not sure" increasesmore and more solutions will have the same color.As Figure 8d shows two points at the center are thedarkest and therefore are the most appealing solu-tions. Using these techniques non-expert Cloud usersare now able set their preferences using high levelif-then rules and get user friendly recommendationson the solution’s prominence. This experiment alsoshows how the system could be helpful for end userwith different levels of knowledge of the preferences.It is worth mentioning that traditionally when endusers are asked to assign weights to the objectives,they are supposed to have full understanding of theirpreferences.

6 RELATED WORKKonstantinou et al. [24] proposed an approach to plan,model, and deploy Cloud service compositions. Intheir approach, the solution model and the deploy-ment plan for the composition in Cloud platform aredeveloped by skilled users and executed by unskilledusers. Likewise, in our system set of compatibilityconstraints from experts were captured which wouldbe utilized to simplify the process of deploymentfor end users by eliminating invalid compositionssolutions. However, as they also mentioned, theirwork lacks an approach for appliance selection andtheir placement on the Cloud which is offered by ourwork. Similarly Chieu et al. [25] proposed the useof composite appliances to automate the deploymentof integrated solutions. However in their work, QoSobjectives are not considered.

Similarly another work has utilized IntuitionisticFuzzy Set (IFS) for ranking service compositions in thecontext of Grid and SOA [26]. It does not deal withusers’ constraints such as compatibility and whenthe problem is NP-hard (like our service composi-tion problem) the execution time is not acceptable.Furthermore, in comparison with the work whichconsidered evolutionary approach such as NSGA-II

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OMOPSOSPEA-IINSGA-II

4500

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2500

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1500

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500

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tion

Tim

e (

ms)

(a) Execution time.

OMOPSOSPEA-IINSGA-II

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Hy

per

volu

me

(b) Hypervolume.

OMOPSOSPEA-IINSGA-II

1.5

1.4

1.3

1.2

1.1

1.0

0.9

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read

(c) Spread.

OMOPSOSPEA-IINSGA-II

0.035

0.030

0.025

0.020

0.015

0.010

0.005

0.000

IG

D

(d) IGD.

Fig. 7: Comparison of algorithms using quality indi-cators.

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DCDT

Reliability

(a) NSGA-II pareto front appliance composition solu-tions without any user preferences.

(b) Coloring NSGA-II output using fuzzy Logic based on9 user preference rules.

(c) Coloring NSGA-II output using fuzzy Logic based on18 user preference rules.

(d) Coloring NSGA-II output using fuzzy Logic based on27 user preference rules.

Fig. 8: Appliance composition optimization results.

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for service composition [12] our approach improvesthe composition solution diversity and convergenceand decreases the execution time.

Unified Cloud Interface (UCI) provides ontology4

model for modeling Amazon EC2 services. Mosaicproject [27] is proposed to develop multi-Cloud ori-ented applications. In Mosaic, Cloud ontology playsand essential role, and expresses the application’sneeds for Cloud resources in terms of SLAs and QoSrequirements. It is utilized to offer a common access toCloud services in Cloud federations. However, noneof these ontologies focus on modeling of compatibilityof Cloud services.

There are several existing approaches [28], [29], [30],[31] that are capable of dealing with incompatibleservices. However, many of them only focused oncompatibility of Input and Output (I/O) of servicesand did not consider incompatibilities that are causedby regulations and other factors that are not relatedto service functionalities.

In addition, OPTIMIS [32] main contribution isoptimizing the whole service life cycle, from serviceconstruction, deployment, to operation in Cloud envi-ronments. The considered QoS criteria in OPTIMIS aretrust, risk, eco-efficiency and cost. The evaluation ofCloud provider is accomplished through an adoptionof Analytical Hierarchy Process (AHP). In comparisonwith our approach for appliance composition, worksthat applied Analytical Hierarchy Process (AHP) andMulti-Attribute Utility Theory (MAUT) [33], can onlyperform well when the number of given alternativesis small and the number of objectives is limited. Incontrast, our approach can deal efficiently with a largenumber of Cloud services in the repository.

While there are other studies [34], [35], [36], [41]that focus on appliance selection and deploymentproblem, we are not aware of any work that providesa framework for composing and deploying multiplevirtual appliances with the focus on automatic com-patibility checking and QoS-aware ranking.

7 CONCLUSIONS AND FUTURE DIRECTIONS

In order to tackle Cloud service composition chal-lenges, we presented an ontology-based approach todescribe services and their QoS properties, whichhelped us to build a composition with a set of com-patible services. Our system helps non-expert userswith limited or no knowledge on legal and imageformat compatibility issues to deploy their servicesfaultlessly. In addition, we proposed a technique tooptimize the service composition based on user pref-erences such as deployment time, cost, and reliability.The approach exploits the benefits of evolutionaryalgorithms such as OMOPSO, NSGA-II, and SPEA-II for optimization and fuzzy logic to handle vague

4. OWL Ontology provided by OCI https://code.google.com/p/unifiedcloud/source/browse/trunk/ontologies/

preferences of users. Results show that for the pro-posed case study, we can effectively help an unskilleduser to identify the appliance compositions which areclosest to their preferences.

Integer Linear Programming (ILP) [37], Pseudo-Boolean Optimisation (PBO) [38], and Multiple Ob-jective Ant Colony Optimization (MOACO) [39] areall competitive algorithms, and with the best of ourknowledge, for this particular problem, there is no in-depth comparison between all three and OMOPSO.Therefore, providing such a comparison can be con-sidered as another possible future work.

Moreover, Cloud services have specific characteris-tics and QoS dimensions which have to be identified.Specifically, defining criteria which are able to modelenergy and carbon emission efficiency, reliability, andtrust of a Cloud service are increasingly attractiveto users. For example, methods to evaluate reliabil-ity and trust of providers from user feedbacks andmonitoring services together can be further studied.This consists of collecting required raw data fromtrusted sources and statistically analyzing and aggre-gating them. In addition, user experience is anotherimportant benchmark for Cloud service providers. Re-cently, crowdsourcing is being used to create collectiveknowledge to assess QoS. It requires an investigationon discovering the crowd that can evaluate a serviceefficiently, delegating evaluation tasks to crowds, andcalculating the accuracy of the assessments.

The current implementation of the translator com-ponent supports XML-based Cloud services. Emerg-ing specifications such as Open Cloud ComputingInterface (OCCI) [40] aims at providing a standardway for describing Cloud resources. Therefore, inves-tigating approaches that can semantically enrich thesenew formalisms can be taken as a future direction tofurther enhance the translator component.

ACKNOWLEDGMENTS

We thank Yoganathan Sivaram, Adel Nadjaran Toosiand Rodrigo N. Calheiros for their constructive sug-gestions on this article.

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Amir Vahid Dastjerdi is a Research Fellowwith the Cloud Computing and DistributedSystems (CLOUDS) Laboratory at the Uni-versity of Melbourne. His current researchinterests include Cloud service coordination,scheduling, and resource provisioning usingoptimization, machine learning, and artificialintelligence techniques.

Rajkumar Buyya is Future Fellow of theAustralian Research Council and Director ofthe Cloud Computing and Distributed Sys-tems (CLOUDS) Laboratory at the Universityof Melbourne, Australia. He is also servingas the CEO of Manjrasoft, a spin-off com-pany of the University, commercializing itsinnovations in Cloud Computing. He has au-thored over 450 publications and four textbooks including "Mastering Cloud Comput-ing" published by McGraw Hill and Morgan

Kaufmann, 2013 for Indian and international markets respectively.He is one of the highly cited authors in computer science andsoftware engineering worldwide (h-index=78 and 28000+ citations).