Review Article A Study on Strategic Provisioning of Cloud...

Review ArticleA Study on Strategic Provisioning of Cloud Computing Services

Md Whaiduzzaman,1 Mohammad Nazmul Haque,2

Md Rejaul Karim Chowdhury,3 and Abdullah Gani1

1 Center for Mobile Cloud Computing Research (C4MCCR), University of Malaya, 50603 Kuala Lumpur, Malaysia2 School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW 2308, Australia3 Department of Electrical & Computer Engineering, University of Western Ontario, London, ON, Canada N6A 3K7

Correspondence should be addressed to MdWhaiduzzaman; [email protected]

Received 11 April 2014; Revised 24 May 2014; Accepted 24 May 2014; Published 15 June 2014

Academic Editor: Antonio Puliafito

Copyright © 2014 MdWhaiduzzaman et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Cloud computing is currently emerging as an ever-changing, growing paradigm that models “everything-as-a-service.” Virtualisedphysical resources, infrastructure, and applications are supplied by service provisioning in the cloud.The evolution in the adoptionof cloud computing is driven by clear and distinct promising features for both cloud users and cloud providers. However, theincreasing number of cloud providers and the variety of service offerings have made it difficult for the customers to choose the bestservices. By employing successful service provisioning, the essential services required by customers, such as agility and availability,pricing, security and trust, and usermetrics can be guaranteed by service provisioning. Hence, continuous service provisioning thatsatisfies the user requirements is a mandatory feature for the cloud user and vitally important in cloud computing service offerings.Therefore, we aim to review the state-of-the-art service provisioning objectives, essential services, topologies, user requirements,necessary metrics, and pricing mechanisms.We synthesize and summarize different provision techniques, approaches, andmodelsthrough a comprehensive literature review. A thematic taxonomy of cloud service provisioning is presented after the systematicreview. Finally, future research directions and open research issues are identified.

1. Introduction

Cloud computing is the distributed computing model thatprovides computing facilities and resources to users in anon-demand, pay-as-you-go model [1]. The aim of the cloudcomputing model is to increase the opportunities for cloudusers by accessing leased infrastructure and software applica-tions anywhere and anytime [2]. Therefore, cloud computingoffers a new type of information and services that broadensthe brand new vision of information technology (IT) services.The recent publicity surrounding cloud computing and atthe same time the rise of smart mobile device help us toenvision mobile cloud computing (MCC) [3]. MCC is adistributed computing model combining cloud and mobilecomputing [4, 5]. The objective of MCC is to enhancethe computing and processing power of mobile devices byoffloading tasks to cloud data centres [6–8]. In the cloud,resources are hosted as software, database services, virtualservers (virtual machines), hardware, complete work flows,

or complex configurations of distributed computing systemsand applications for provisioning [9, 10]. These resources areprovisioned as services and offered to the customer by thecloud service provider (CSP). Therefore, the CSP leveragescloud services in two forms: service and cloud provider. Acloud provider is the entity that offers and upholds a cloudand may offer internally developed services on the cloud. Aservice provider is an entity that crafts and preserves servicesfor running and publishing in the cloud [11–13].

Service is a type of relation between the service providerand the service user. Therefore, service provisioning is theprocess of enabling the service customers to access the pre-defined resources and enjoy the required provisioned services[14–16].The resources are in the forms of hardware, software,computation, and storage. Essentially, the primary interac-tion between the customer and service provider depends onservice provisioning [17]. Thus, service provisioning plays avital role for both the CSP and the cloud users. The CSPmust provide the required and promised services, promised

Hindawi Publishing Corporatione Scientific World JournalVolume 2014, Article ID 894362, 16 pageshttp://dx.doi.org/10.1155/2014/894362

2 The Scientific World Journal

in the service level agreement (SLA), such as quality ofservices (QoS), availability, and pricing, and the servicesmustsatisfy the user requirements, such as on-demand availability,scalability, elasticity, security, and exact billing [18–20].

One of the objectives of service provisioning is faircomparison among the available services. Thus, the user cancompare the different cloud service offerings according totheir needs and prioritize them based on several predefineddimensions [21]. However, the services provisioned to theuser should meet certain service compliances and policies.Hence, the CSP assures the client of data protection, confi-dentiality, and security by complying with international com-pliance authorities, such as theNational Institute of Standardsand Technology (NIST), EuropeanNetwork and InformationSecurity Agency (ENISA), health insurance portability andaccountability act (HIPAA), and cloud security alliance(CSA). In addition, service provisioning should maintain theQoS, SLA, and user service requirements. All of these factorsenable the user to compare the provisioned services, predictthem, and rank them according to the user’s needs to selectthe best available services. Therefore, from the customer’spoint of view, service provisioning is a very important aspectof cloud services [22, 23].

This systematic review of cloud service provisioningenhances the knowledge for the general reader and newresearchers. Hence, this review has a significant impact in thisdomain. Therefore, we reviewed the state-of-the-art of theart service provisioning objectives, essential services, topolo-gies, user requirements, necessary metrics, and pricing; wesynthesize and summarize different provision techniques,approaches, and models through a comprehensive literaturereview. A thematic taxonomy of cloud service provisioning ispresented after the systematic review. Finally, future researchdirections and open research issues are identified.

The rest of this paper is organized as follows: Section 2expresses the service provisioning background with defini-tion, topology, and taxonomy. Section 3 discusses the objec-tives of service provisioning. Section 4 offers an overviewof key areas of service provisioning. Section 5 introducesthe service provisioning requirements and Section 6 dealswith metrics. Section 7 focuses on several models, methods,and provisioning techniques. Section 8 explains the futureresearch issues and challenges. Finally, Section 9 provides theconclusion. For better understanding for the general readers,we present a list of acronyms of commonly used wordsthroughout this paper in the Abbreviations Section.

2. Service Provisioning Definition,Topology, and Taxonomy

Cloud service provisioning is a manner of providing cus-tomers access to resources to complete the desired tasksrequired by the customer.The hardware, software, or compu-tational tasks can be the form of provisioned resources [24].In topological perspective, service provisioning is dividedinto two parts: single cloud and intercloud. A single cloudcomputing data center is used by the client who brings severalchallenges. The unavailability of cloud service can leave

thousands of customers relying solely on limited essential andpaid resources. Grozev and Buyya [25] introduce and presenttaxonomies of federated cloud architectures, mechanism ofapplication brokering, and the current environments. For-mally, intercloud computing is defined as in [26]:

“a cloud model that, for the purpose of guaranteeingservice quality, such as the performance and availability ofeach service, allows on-demand reassignment of resourcesand transfer of workload through an interworking of cloudsystems of different cloud providers based on coordinationof each consumer’s requirements for service quality providersSLA and use of standard interfaces.”

The state-of-the-art thematic taxonomy of service provi-sioning is presented by classifying several vital key issues forfurther discussion. Figure 1 shows the taxonomy of serviceprovisioning selection, comprising approaches, objectives,requirements, metrics, techniques, services, and topologies.There are several approaches to service selection, includingbrokerage based, SLA matching, and policy based, heuristic,and holistic. The techniques involved in different serviceprovisioning approaches include the genetic algorithm, gametheory, and multicriteria decision-making. The main objec-tives are availability, scalability, comparison capability, andthe CSP’s compliance with the relevant regulations. In addi-tion, the primary requirements include QoS, web services(WS), security aspects, pricing, and the elastic capability ofthe services.The essentialmetrics are divided into three broadaspects: storage, network, and computation. Provisioning isfundamentally based on three core service models: infras-tructure as a service (IaaS), platform as a service (PaaS), andsoftware as a service (SaaS). It may derive from a single cloudor within the multicloud via the interaction of several serviceproviders.

By combining cloud and internet of things, SalvatoreDistefano et al. envision and propose a new concept “cloud ofthings” (CoT) [27].They aggregated heterogeneous resourcesand tailored thing-like semantics by enabling things as a ser-vice. Moreover, they model the cloud service computing as asensing and actuation as a service (SAaaS), thus provisioningservices by the sensors and actuators [28]. An architecture isdesigned for pervasive ICT structure generating the nearbyenvironment data by sensors and actuators through cloud[29].

3. Objective of Service Provisioning

The strategic objectives of provisioning cloud services have aparamount importance. We describe the major objectives asfollows.

3.1. Fair Comparison. One of the objectives of service provi-sioning is the fair comparison among the available servicesor with the CSP. Generally, users compare different cloudofferings according to their priorities and along severaldimensions to select whatever is appropriate to their needs.However, it is a difficult task to perform an unbiased compar-ison and evaluation of all services. Several challenges mustbe addressed to develop an evaluation model that precisely

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Cloud service provisioning

Objective Services Approaches

MCDA

AI/machinelearning

Matching

Statisticalbased

QoS

Requirements Metrics

PaaS

IaaS

SaaSFaircomparison

Prediction

RankSecurityand trust

Topologies

Compliances

Single cloud

Intercloud

Cost perbenchmark

Scalinglatency

Benchmarktime

Responsetime

Cost peroperation

Consistencytime

Throughput

Availability

Price

Trust degree

Broker based

Rank based

Stability

Softcomputing

SeaaS

Executiontime

Agility

SLA

Policy

Figure 1: Taxonomy of cloud service provisioning.

measures the service level of each cloud provider. This studyaims to provide a comparable service analysis for the clouduser to choose among desired services [30].

3.2. Compliance. Service provisioning should comply withappropriate policies. The assurance of service compliancecomes from the service providers [31]. The CSP assures thecustomer of their compliance policies such as data protection,data confidentiality, and necessary data security by com-plying with the international compliance authority. NIST,ENISA, HIPAA, ISO 27001, and CSA are several complianceauthorities who provide guidelines to establish the currentcloud compliance security standards for the industry. Inthe Abbreviations Section, the details of the acronyms arepresented.

3.3. Prediction. Prediction is important in cloud service pro-visioning. A service user should be ensured of the elasticityand scalability of the services, even during peak hours orwhen the user suddenly makes an unusually high demand onthe resources [32]. In this situation, one of the objectives ofthe service provisioning selection is that the request shouldbe instantly fulfilled by the service provider. Therefore, theuser should be assured of the available required resources ondemand with the predictable elastic and scalable services.

3.4. Rank. Selecting the best and most appropriate serviceis a vital factor for the cloud service user. Selecting servicesdepends on comparing and ranking them suitably [31]. Areasonable and acceptable ranking system helps the cloudcustomer to make decisions about service selection. There-fore, the cloud service ranking system is an important aspectof a fair cloud service comparison and selection process.However, there is a lack of comparison of services acrossproviders due to a lack of common comparable criteria orattributes.

4. Major Services of Service Provisioning

In cloud computing, in the perspective of resource allocationand service provisioning, the services layers are dividedinto several working layers [33]. There are then four servicelayers: the application layer (SaaS), the platform layer (PaaS),the infrastructure layer (IaaS), and security as a service(SecaaS) layer. For the list of acronyms, please refer to theAbbreviations Section. Each of these layers provides a specificservice for users, which are explained as follows [34].

4.1. Infrastructure as a Service (IaaS). Infrastructure as aservice, defined as providerswho offer computing and storageresource capacity via vitalization, allowing physical resources


to be assigned and split dynamically. A typical applicationcould be an on-line alternative to a word processor orspreadsheet. Several types of virtualization occur in this layer.Along with other resources, it includes computing, network,hardware, and storage. At the bottom layer of the framework,infrastructure devices and hardware are virtualised andprovided as a service to users to install the operating system(OS) and to operate software applications. Therefore, thislayer is called infrastructure as a service (IaaS). The ElasticComputing Cloud of Amazon (Amazon EC2) and storage byboth Elastic Book Store (EBS) and Simple Storage Services(S3) are typical services of this layer.

4.2. Platform as a Service (PaaS). Platform as a service,defined as a provider that offers an additional layer of abstrac-tion above the virtualised infrastructure. The provided soft-ware platform trades off restrictions on the type of softwarethat can be deployed for built-in scalability PaaS includingmobile operating systems such as Android, iPhone, Symbian,and other OSs, as well as database management and IMS.This layer contains the environment for distributing storage,parallel programming design, the management system fororganising distributed file systems, and other system man-agement tools for cloud computing. Program developers arethe primary clients of this platform layer. Entire platformresources such as program testing, running,maintaining, anddebugging are delivered by the platform directly from thislayer. Hence, this form of services in the platform layer istermed platform as a service (PaaS). Classic examples of theseservices include Google App Engine and Microsoft Azure.

4.3. Software as a Service (SaaS). Software as a service,defined as a provider who supplies remotely run soft-ware packages to consumers via the Internet on a utility-based pricing model. Analytical, interactive, transaction, andbrowsing facilities are included in the application layer. SaaSdelivers several simple software programs and applicationsas well as customer interfaces to the end users. Thus, in theapplication layer, this type of service is called software as aservice (SaaS). By using the client software or browser, theuser can connect to services from providers via the Internetand pay fees according to the services consumed, in a pay-as-you-go model. Customer relationship management (CRM)from Salesforce is one of the early SaaS applications. Amongother services, Google provides online office tools such asdocumentation, presentations, and spreadsheets, which areall part of SaaS.

4.4. Security as a Service (SecaaS). The agility offered bythe on-demand provisioning of computing resources andthe ability to align information technology with businessdemands are valuable; however, clients are also very anxiousabout the security risks of cloud computing and the costof direct control over the security of systems. Althoughvendors have attempted to satisfy this demand for securityby offering security services in a cloud platform, the selectionprocess is still completed [35]. These issues have led to therestricted adoption of cloud-based security services, but the

future looks bright for SecaaS, with Gartner predicting thatcloud-based security service will be more than triple inmany segments. To support both cloud customers and cloudproviders, CSA has adopted a new research project to providegreater clarity in the area of SecaaS. It refers to the provisionof security applications and services from the cloud to cloud-based infrastructure and software or from the cloud to thecustomers on premise systems. SecaaS will allow enterprisesto make use of security services in new ways that would bemore costly if provisioned locally.

5. Service Provisioning Requirements

There are several types of service provisioning fromwhich wecan make need-based selections, as discussed below.

5.1. Agility and Availability. Agility is one of the great advan-tages of cloud computing, which enables an organizationto expand and change its resources quickly without muchexpenditure. Agility in SMI is measured as a rate-of-changemetric, showing how quickly new capabilities are integratedinto IT as needed by the business. When considering a cloudservice’s agility, organisations want to understand whetherthe service is elastic, portable, adaptable, and flexible. A virtu-alised optical network is proposed as a key to implementingincreased agility and flexibility in a cloud computing environ-ment by providing any-to-any connectivity with the appro-priate optical bandwidth at the appropriate time. Jinno andTsukishima [36] proposed a concept for a virtualised opticalnetwork (VON), which is achieved through virtualizationin the optical domain as a key to implementing increasedagility and flexibility in a cloud computing environmentby providing any-to-any connectivity with the appropriateoptical bandwidth at the appropriate time.

Hirzalla [37] explored their impact on business agilityand software development by sharing best practices andlessons learned through an interactive session that offersinsights from previous field engagements. More specifically,they addressed how to realize business agility requirementsthrough the potential synergies between SOA and cloudcomputing. The concept of business agility originates fromthe manufacturing industry in the 1980s. In contrast to otherconcepts such as flexibility, agility refers to the ability of a firmto adapt swiftly to changing environments. Thus, businessagility can be defined as the ability to sense and respondto opportunities and threats in an efficient, effective, andtimely manner. Consequently, agility is crucial for firms,especially in volatile environments, to stay competitive. Inthis regard, IT plays an important role in sensing andresponding capabilities [38]. Peng et al. [39] proposed aframework for resource provisioning in the cloud throughnetwork vitalization. It delivers optimized resources, on-demand scalability, and flexible future CSPs based on theconcept of an IaaS framework and IP network virtualisation.

5.2. Pricing. The SLA resource allocator acts as the interfacebetween the data centre/CSP and external users/brokers. It


supports SLA-oriented resource management. After receiv-ing a service request, the service request examiner and admis-sion control mechanism interpret the submitted request interms of QoS requirements and ensure resource availability.Then, it requests VMs for resources and determines theallocated VMs. The charges for the service request aredetermined by the pricing mechanism based on submissiontime, pricing rates, or the availability of resources. The actualusage of resources is tracked by the accounting mechanism.In addition, the VM monitor tracks the availability of VMs,the dispatcher begins the execution, and the service requestmonitor mechanism tracks the execution progress of theservice requests [1].

Rosenthal et al. [40] express the concern that some usershave had unpleasant surprises regarding the costs associatedwith the unexpectedly heavy use of cloud resources andexpecting some cloud vendors to offer suitable throttlingservices. A remarkable increase of cloud computing serviceofferings has enabled technologies for service composition,by developing efficient pricing models to foster the resourceallocation process and evaluate the services used. Weinhardtet al. [41] present a multidimensional procurement auctionfor composite services: a model for service value networksbased on a graph structure where a path is allocated by theauction mechanism through a network containing price andconfigurations of the offerings.

Saure et al. [42] proposed a reservation system withfinite computing resources over an infinite horizon, wherea set of incumbent users submit reservation requests forcomputing resources ahead of time. The multinomial logit(MNL) framework is used to model customer substitutionbehavior by adjusting the resource prices in tokens per unit oftime and per computing resource. A class of pricing policiescalled time-of-use (ToU) is considered, and a simple andintuitive algorithm is proposed to formulate the problem.The evaluation showed that the optimal ToU policy out-performs single pricing strategies for customer satisfactionby 3–8%, on average. Teng and Magoules [43] propose anew resource pricing and allocation policy where users canpredict the future resource price while satisfying budget anddeadline constraints. By using game theory, the resourceprice gradually converges to an equilibrium state based ondynamic games where the users can receive a Nash equilib-rium allocation proportion without the other competitors’bidding information. The experiments were implemented ina CloudSim simulation.

In cloud computing, resources are provisioned by reser-vation and on-demand plans. However, a reservation planis cheaper than an on-demand plan. Due to the uncertaintyof the customer’s future demands, it is difficult to minimizecosts. To address this problem, Chaisiri et al. [44] proposedan optimal cloud resource provisioning (OCRP) algorithmusing a stochastic programmingmodel.TheOCRP algorithmprovisions the computing resources for multiple provision-ing stages based on the demand and price uncertainty.By using a deterministic equivalent formulation, sample-average approximation, and Benders’ decomposition, theOCRP algorithm can successfully minimize the total costof resource provisioning in cloud computing environments.

Samimi and Patel [45] presented a comparative review of gridand cloud computing economic and pricing models usingtariffs and charging. They considered several factors, such asregulations, tax laws, service level agreements, and return oninvestments, and reviewed the latest economic and pricingmodels for grid and cloud computing.

The current pricing scheme for cloud computing hassignificant constraints. Spot instance, the first deployedauction-style pricing model of Amazon EC2, cannot han-dle untruthful bidding in resource allocation. Hence, [46]addressed the problemof cloud resource pricing by proposinga suite of computationally efficient and truthful auction-stylepricing mechanisms. The proposed algorithms can increaserevenue by achieving truthfulness without collusion or (𝑡; 𝑝)-truthfulness tolerating a collusion group of size 𝑡 with aprobability of at least 𝑝. Sharma et al. [47] designed andsimulated a cloud resources pricing model using financialoption theory where the cloud resources are treated as realassets. Then, using the financial model, the cloud resourcesare priced.The compoundedMoore’s law is applied to handlethe pricing of resources at the required QoS, which can thenmanage a realistic cloud pricing problem. Recently, cloudcomputing has emerged as amodel in support of “everything-as-a-service.” Motivated by this idea, [48] proposed a per-sonalised pricing strategy for cloud computing, which isdefined as pricing as a service (PraaS).Thismethod efficientlymeets the demands of the customer and simultaneouslymaximises the revenue of the providers. The experimentalresults show that this pricing method is appropriate for theon-demand use of cloud resources and guarantees morerevenue for the cloud providers. Li et al. [49] combinedthe computing and resource swarm algorithms to evaluatethe resource price adjustment by the cloud bank model.The pricing strategy includes two important models: theinitial price model (IPM) and the resource swarm algorithmprice adjustment model (RSAPAM). Hence, resources willreach the most reasonable price over time. Ren and vander Schaar [50] proposed a joint optimization of schedulingand pricing decisions for delay-tolerant batch maximize tomaximize the service provider’s long-term profit. Dynamicsetting and provably efficient dynamic scheduling and pricing(Dyn-SP) algorithms are developed without the necessity ofpredicting future information. The results indicate higheraverage revenue with the same average queuing delay.

5.3. Security and Trust. Research interest has therefore arisenin developing software engineering techniques to supportsystems based on the cloud, to enable software engineers toidentify security and privacy requirements and to select asuitable cloud service provider based on such requirements[51]. Mouratidis et al. [52] propose a novel framework tosupport the elicitation of security and privacy requirementsand the selection of a cloud service provider. It incorporatesa modelling language and provides a structured process thatsupports elicitation of security and privacy requirementsand the selection of a cloud provider based on the serviceprovider’s satisfaction of the related security and privacyconcerns. This framework illuminates the organisational


context by identifying goals, actors, tasks, resources, andplans to identify and analyse the privacy constraints, securityand privacy goals, threats, and vulnerabilities relevant to acloud based system.

Dasgupta and Rahman [53] present a cloud securityinsurance framework to estimate the coverage of differentcloud services. For cloud security insurance, security cov-erage is important and relevant, as the cost of deployingspecial protection, detection, and response tools varies andrequires well-organised coverage estimation. Hence, an intel-ligent search and optimization technique for determiningthe appropriate combination of tools to provide multileveldefence for cloud services is necessary. A framework isdeveloped for calculating security coverage focusing on thesecurity techniques and solution tools of different cloudservices. A Java software program named MEGHAND isdeveloped to implement and test this insurance securitymodel and tested for different cloud security models. Thisframework reports the cloud security coverage problem forrisk analysis and for developing insurance models for thecloud. This coverage estimator (MEGHNAD) is primarilyuseful for cloud insurance providers in meeting customersecurity expectations while satisfying all service require-ments. The cloud provider, however, must use the recom-mended security products from amultivendor andmix themseamlessly for the desired security coverage. Rigorous riskassessments are needed to ensure the map of policies andprocedures. The genetic algorithm optimizing method usedin MEGHNAD is scalable to accommodate tool-specificconfiguration settings, required standards, and compliancesfor a fine-grained coverage estimate. Arias-Cabarcos et al.[54] introduce a federated identity management (FIM),identified by researchers and experts as a vital securityenabler for implantation of cloud computing. However, thecurrent frameworks are limited by the complexity of thedynamic federation between interclouds. A key requirementis trust management to foster collaboration and analyse theFIM process. A set of new metrics is defined to allow thenovel form of risk measurements. A generic hierarchical riskaggregation system and cloud-based service provisioning arementioned as contributions. A set of risk metrics is derivedthat can serve as an aggregation model for risk calculation.The AHP method is used for independent analysis.

One critical problem is the location of major problems incomplex cloud application scenarios; therefore, [55] proposea ranking-based framework named FTCloud for buildingfault-tolerant cloud applications. Two ranking algorithms areproposed to identify the significant components within theenormous amount of cloud data. Then, they find the bestsuitable fault-tolerance strategy for each individual compo-nent. FTCloud consists of two parts: ranking and optimalfault tolerance selection. A component graph is prepared forcomponent invocations, which then pass through componentranking, where the ranking algorithms are employed. Basedon the ranking results, the important components are identi-fied, and the most suitable fault-tolerance strategy is selected.Finally, the ranking of the selected fault-tolerance compo-nents returns to the system designer to build authenticated

cloud applications. However, the software component graphsused here are limited [56].

To assess the security risks related to cloud computingplatforms, a quantitative risk and impact assessment frame-work (QUIRC) is presented by Saripalli and Walters [57]. Itdefines risk as a combination of the probability of a securitythreat event and its severity, measured as its impact. Here, sixkey security objectives (SO) are identified for cloudplatforms,and the typical attack vectors and events are mapped intothese six categories. For assessing security risks, the Wide-band Delphi method is proposed to collect the information.The advantage of QUIRC is that it offers fully quantitativeand iterative convergence for the dependable comparativeassessment of the relative robustness of different cloudprovider offerings and approaches. A quantitative frameworkis presented for analyzing and assessing the risks and impactsto the security of cloud-based software deployments, and theadvantages of the approach are elucidated. They introducemethods of risk assessment based on probability and impactand the QUIRC framework. Traditional threat modelling canbe related to the QUIRC computations via the identificationof threat events. To build more reliable input data for QUIRCanalysis based on industry verticals and expert knowledge,a Wideband Delphi method is proposed. A quantitativeapproach provides vendors, customers, and regulatory workgroups with the ability to assess the relative robustnessof different cloud vendor offerings and approaches in adefensible manner. Moreover, it helps to alleviate the fear,ambiguity, and uncertainty related to cloud platform securityissues by ensuring they will be handled effectively. However,its limitation is that it requires the careful collection of inputdata for probabilities of events, which requires collectiveindustry SME inputs.

Ouedraogo andMouratidis [58] highlight the importanceof an informed choice of CSP to reduce the exposure toinsecurity in a cloud context. A well-defined approach calledC.A.RE (complete-auditable-reportable) is proposed, whichhelps to determine CSP security by assessing its completenessand possible risks and vulnerability for the cloud servicecustomer (CSC). Hence, reliability and availability are twoimportant aspects from a user perspective. DoS, naturaldisasters, and equipment outages are potential threats againstcloud services availability and reliability. The assurance ofsecurity is defined as when an entity meets the objective ofsecurity.The underpinnings of the C.A.RE approach the needof mutual auditability and multiparty trust consideration.The “complete” phase: this phase concerns accumulating thenecessary evidence to complywith and support the alignmentbetween the policies implemented by the CSP and thesecurity requirements of the CSC. These security needs andcompliance include regulations, laws, security policies, stan-dards, and best practices. Here, a certification is treated as abadge of security assurance for the CSP, for example, ISO/IEC270001 certification for a data centre security certificate. Thecompleteness is practically determined by considering theset of security requirements identified by the nature of therelevant CSC application.Then, the security mechanism for acertain provider ismapped and analysed to determine towhatextent the security requirements are met. The “auditable”


phase and its dependent metrics: this phase supplementsthe completeness metric. The necessary components of theaudit are the coverage, depth, rigor, and independence of theverification. The “reportable” phase: according to the scopeof the SLA, the CSP conveys security issues or informationin a timely manner to the CSC. The completeness indicatesthe suitability of the CSP’s security concerns to the CSC.The second feature ensures the auditability of the securitydeployed by the CSP. Finally, reporting provides the securitytransparency information to the CSC. We present the cloudservice requirements in Table 1.

5.4. Quality of Service. As cloud computing is gaining muchattention in the last few years, the idea of IT services throughInternet on-demand and pay-as-you-go model also changes.Thus, the exponential escalation of such service and selectingthe optimal service provider based on quality of service(QoS) become vital. Salama et al. [59] propose the idea ofintegrated QoS assured utility model to address the problemof cloud service provider selection based on a multidimen-sional QoS approach, for satisfying the best utilization ofconsumer requirement. The proposed mathematical modelassists decision makers in selecting the optimal cloud ser-vice provisioning, incorporating customer’s minimumneeds,quality of service, and business profitability and performancecriteria. Bao and Dou [60] use finite state machine (FSM)to recommend the legal invocation orders of these servicesand an improved tree-pruning-based algorithm is proposedfor creating the web service composition tree (WSCT) foroptimal service selection. First, an improved tree-pruning-based algorithm is introduced to construct the compositiontree and then the SAW technique is adopted for serviceselection.

Zhao et al. [61] address four main issues to solve theservice selection problems from multiple service providerssuch as scalability, flexibility, multiple QoS facility, and auto-matic user preference support. Therefore, service providersshould have the high degree of credibility and service quality.Hence, the trust degree implies the reliability or availabilitylevel. Again, Zheng et al. [62] introduce a personalized QoSranking prediction system framework, CloudRank, whichrequires no extra service invocations for QoS ranking incloud services. The past users’ experiences are exploited forranking based approach and accumulate and identify thepreferences between a pair of services to obtain a rankingservice. Chan and Chieu [63] propose a mechanism byevaluating specific performance and QoS attributes basedon singular value decomposition (SVD) to select the bestservice provider for a user applicationwith a set of predefinedrequirements. A user sends a request for the requirementto execute the application into the cloud service providermapper which dynamically provides the service provideraccording to application demands.

Li et al. [64] describe amethod to achieve optimization inclouds by supporting the developers to enable runtime opti-mization employing an optimization algorithm. It maximizesprofits in the cloud constrained by QoS and SLAs withinthe large variety of workloads. Srivastava and Sorenson

[65] propose a technique that overcomes the restrictionand compares functionally equivalent services on the basisof the customers’ perception of the QoS attributes ratherthan the actual attribute values. Goscinski and Brock [66]propose a framework which provides the application of theresources via web services framework (RVWS) to offer higherlevel abstraction of clouds in the form of a new technologyto service provisioning resource publication, discovery, andselection based on dynamic attributes. Automatic servicepublishing, selection, and discovery of required services arestill one of the research priorities. In addition, this modelexplores the ways of publication, discovery, selection, and useof cloud resources as services without specialized knowledgeby using dynamic and current attributes through web serviceWSDL documents to help, discover, and select essentialservices and resources based on user requirements.

6. Metrics

A set of business-relevant key performance indicators (KPIs)that provide a standardised method to measure the businessservices are needed. The SMI framework provides a holisticview of the QoS needed by the customers for selecting aCSP based on accountability, agility, assurance of service,cost, performance, security and privacy, and usability. Thereare currently few publicly available metrics that definethese KPIs and compare cloud providers. SMI is the firsteffort in this direction. The defined high-level attributes areaccountability, agility, cost, performance, assurance, securityand privacy, and usability. The proposed metrics for cloudKPI are service response time, sustainability, DCiE and PUE(the most prominent metrics used to measure the energyefficiency of a cloud computing service), suitability, accuracy,interoperability, availability, stability, adaptability, usability,throughput and efficiency, and scalability [18].

Li et al. [67] proposed a metrics catalogue with multifoldusability. This catalogue can be used as a dictionary toconveniently look up suitable metrics for cloud deployment.The existing evaluation metrics in the catalogue can help indeveloping metrics for research. Moreover, the preliminarymetrics may help to better implement the evaluation of cloudservices. Li et al. [68] developed a set of tools to measurethe metrics under four major providers: AWS, AppEngine,Azure, and cloud servers. The devised tool is simple andcan be easily extended to other cloud platforms to measureapplication performance. In addition, Bojanova and Samba[69] propose to determine the relative efficiencies of thedifferent cloud computing models by measuring and ana-lyzing the following cloud computing infrastructure metrics:hardware costs, software costs, and real-time provisioningcosts. Real-time provisioning is integrated into the servicemanagement system by applying automated tools. Maiya etal. [70] introduce cloud manageability metrics by definingthe user’s role, major use cases that the user performs, andmetrics. Different platforms are used to validate this strategy.The proposed metrics are interfaces, documentation, time tolearn, number of steps, time taken, and ease of use. Finally,[71] propose the most relevant metrics and figures of merit


Table1:Re

quire

mentsof

cloud

servicep

rovisio

ning

requ

irements.

Requ

irements

Features

Solvingapproaches

Attributes

References

Agilityand

availability

Virtualised

optic

alnetwork(V

ON)

Approp

riateop

ticalband

width

atthe

approp

riatetim

eAny-to

-any

conn

ectiv

ityJin

noandTsuk

ishim

a[36]

Com

bining

SOAandclo

udcompu

ting.

Aninteractives

essio

nthatoff

ersinsights

from

previous

field

engagements

Realizingbu

sinessrequirements

Hirz

alla[37]

Afram

eworkforresou

rcep

rovisio

ning

Throug

hnetworkvirtualisation

Optim

isedresources,on

-dem

andscalability

Peng

etal.[39]

Pricing

Multid

imensio

nalprocurement

auctionforc

ompo

sites

ervices

Basedon

agraph

structure

Thea

uctio

nmechanism

Weinh

ardt

etal.[41]

Sing

lepricingstr

ategiesfor

custo

mer

satisfaction

Multin

omiallogit(M

NL)

fram

eworkisused

Pricingpo

liciesc

alledtim

e-of-use

(ToU

)Sauree

tal.[42]

Resource

pricingandallocatio

npo

licy

Future

resource

pricep

rediction

Gam

etheoryandim

plem

entedin

Clou

dSim

simulation

Teng

andMagou

les[43]

Usin

gtariff

sand

charging

Regulations,tax

laws,andSL

APricingmod

els

Samim

iand

Patel[45]

Jointo

ptim

isatio

nof

schedu

lingand

pricingdecisio

nsDyn

amicschedu

lingandpricing(D

yn-SP)

algorithm

sHigherrevenue

with

thes

ameq

ueuing

delay

RenandvanderS

chaar[50]

Securityand

trust

Securedclo

udservices

electio

nSecuredserviceframew

ork

Softw

aree

ngineerin

gMou

ratid

isetal.[52]

Insurancem

odelsfor

cloud

security

Clou

dsecurityinsuranceframew

ork

MEG

HAND

Dasguptaa

ndRa

hman

[53]

Federatedidentitymanagem

ent

Hierarchicalcloud

basedris

kaggregation

syste

mbasedservicep

rovisio

ning

Risk

metric

sAria

s-Cabarcose

tal.[54]

Rank

ingbasedfaulttolerant

fram

ework

FTclo

udmod

elRa

nkingwith

optim

alfaulttolerance

Zhengetal.[55]

Quantitativ

erisk

andim

pact

managem

ent

QURICfram

ework

WidebandDelp

himetho

dSarip

alliandWalters[57]

QoS

QoS-based

utilityop

timum

service

selection

Optim

izationwith

minim

umrequ

irements

Respon

setim

e,availability,andthroug

hput

Salamae

tal.[59]

Services

electioncarriedou

tbyCW

Stre

econ

strictio

nFSM,SAW

,treep

runing

algorithm

Possibleexecutionpath

[60]

Syste

mmod

elof

person

alized

user

supp

orttoop

timizeQ

oSsupp

ort.

Algorith

mTrust,respon

setim

e,price,andplatform

Zhao

etal.[61]

QoS

predictio

nsyste

m(C

loud

Rank

)consideringpastuser

experie

nce

KRCC

andrank

ingpredictio

nalgorithm

Preference

ofservices

Zhengetal.[62]


for the evaluation of customer cloud benchmarks. Thesemetrics are provisioning latency, provisioning throughput,and runtime performance, which are measured by latency,throughput, and bandwidth.The aforementionedmetrics canbe compared to the following attributes: scalability, stability,and reliability.

7. Models, Methods, and Techniques ofService Provisioning

The used methods, models, and techniques for successfuland efficient provisioning of services have a paramountimportance. For these discussions, we use several acronymsand please refer to the Abbreviations Section. Hence, wediscuss the criteria of service provisioning as follows.

7.1. Service ProvisioningModels. The servicemeasuring index(SMI) is a service measurement model based on businessmodel of the International Standard Organization (ISO).TheSMICloud model is proposed by Garg et al. [18] which letsusers compare different cloud offerings, according to theirpriorities and along several dimensions, and select whateveris appropriate to their needs. Several challenges are tackledin realizing the model for evaluating QoS and ranking cloudproviders. The SMICloud systematically measures all theQoS features proposed by cloud service measurement indexconsortium (CSMIC) and ranks the cloud services basedon these business services. Again, Li et al. [68] introduceCloudCmp, a systematic cloud service performance andcost comparator system. It assesses the elastic computing,storage, and networking services promised by cloud metrics,which have an impact on performance of customer appli-cations. CloudCmp safeguards the fairness, participation,and compliance of these assessments, compromising limitingmeasurement cost. By using CloudCmp, most of the cloudcustomers today can find offered services from cloud serviceprovider very widely in terms of efficiency and price andcalculate the need for a thoughtful provider selection. Hanet al. [72] present the cloud service selection frameworkwhich employed a recommender system (RS) that selectsthe best services from different cloud providers according tothe customer needs. The Cloud Provider (CP) registers theirservices in the web portal through CSRS system and usersput their requirements and get recommendation by the webportal. If a CP wants to register, the request is passed to therequest manager through the web portal after the evaluationprocess and then sends to resource register after calculatingthe S-rank and QoS values of CPs and storing in resourcerepository.

7.2. Brokerage Aided Provisioning. Sundareswaran et al. [73]describe the problems of finding the best price and servicesare addressed from the huge pool of services from the cloudservice providers. A novel brokerage based architecture isproposed to use a unique indexing technique for efficientservice selection by employing algorithms which aggregateand select the optimal option. In this model, the cloudbroker collects the service provider’s properties and similar

properties are analyzed and indexed by the cloud brokerand using it when receiving request from customer for thebest matched service. Le Duy et al. [74] introduce a newbenchmark to evaluate and compare cloud brokers. Cloudbroker challenge (CBC) explains the cloud providers, cloudconsumers, and goals with five variety levels of complexities.CBC benchmark is useful for evaluation and comparisonof unbiased brokers and feasible for real-life cloud brokers.The design and development of software agents for cloudservice discovery, service negotiation, and service composi-tion play an important role. Sim [75] introduces an agent-based paradigm for constructing software tools and test bedsfor cloud resource management. He develops Cloudle—anagent-based search engine for cloud service discovery to showeffective agent-based negotiation mechanisms and agent-based cooperative problem solving techniques effectivelyadopted in automatic cloud service composition. Moreover,agent-based problem solving techniques such as acquain-tance networks and the contract net protocol are employed byGutierrez-Garcia and Sim [76] for creating a self-organizingservice composition framework. In this model, cloud servicecomposition framework provides generic agent behaviours tohandle ad hoc web service workflow specifications. Finally,self-organizing service composition is supported by contractsamong cloud participants which mapped to service levelagreements in cloud computing environments. Amato et al.[77] in the mOSAIC project design knowledge based rep-resenting resources and domain concepts of semantic webontologies and rule based support tool, the semantic engine.It aids the user to abstract the requirements in a vendorindependent way to compare the different offers of providerswith the user proposal and retrieves the best offer.

7.3. Policy Ensured SLA. In cloud computing, contractsbetween users and traders are recognized as service levelagreements (SLAs), mentioning the terms and conditionsof service usage. Service level agreements are establishedbetween service consumers and providers and define anumber of obligations and rights for both sides. However, theincreasing number of service offerings is so rapid and there isa lack of a standard for specification; manual service selectionis an expensive task, averting the successful implementationof on demand ubiquitous computing. Therefore, automaticmethods for matching SLAs are essential. Redl et al. [78]propose a method to select semantically equal SLA elementsfrom differing SLAs by employing several machine learningalgorithms. In addition, this method enables autoselection ofoptimal service offerings for cloud services. A framework ispresented to automatic SLA management by a simulation-based study to establish several significant advantages of thisapproach for cloud customers. Research on SLAmanagementfocuses on SLAs with rights for consumers and obligationsfor providers, keeping the two parties balanced interest.Spillner and Schill [79] present a solution of monitoring dataat runtime and feeding it back into the service registry toadjust descriptions and make contract template derivation asa more realistic process. Emeakaroha et al. [80] introduce


detection SLA violation infrastructure (DeSVi), the novelarchitecture for monitoring and detecting SLA violations incloud computing infrastructures. The main components ofthe architecture are the automatic VM deployer, responsiblefor the allocation of resources and for mapping of tasks,application deployer, responsible for the execution of userapplications, and LoM2HiS framework, which monitors theexecution of the applications and translates low-level metricsinto high-level SLAs. However, this proposed system iscapable of monitoring only a single cloud data center. Onthe one hand, SLA violation should be prevented to avoidcostly penalties and on the other hand providers have toefficiently utilize resources to minimize cost for the serviceprovisioning. Few approaches are limited to simpleworkflowsand single task applications. Bouchenak [81] introduces asystematic and synchronized integration by the definition ofSLA aware cloud and explains the automated cloud controlfor building SLA aware dynamic elastic clouds. Finally,Chi et al. [82] describe a framework by employing noveldata structure. Pearson and Sander [83] introduce a policybased mechanism of service provider assessing the risk-based semiautomated system which drastically reduces thetransaction to lower the cost of selecting desired CSP. Thisensures compliance and trustworthiness of service providers.

7.4. Heuristic and Holistic Perspective. Song et al. [84] intro-duce a framework for task selection and allocation to enhanceresource utilization for PCP by exploiting an adaptive filter.To optimize the goal of a heuristic algorithm for optimizingthe tradeoff between QoS of the tasks and utilization ofresources a tradeoff metric is introduced. A VM-basedoverall resource structure for computing resource utilizationis presented. Simulation study shows that algorithmperformsbetter than other existing algorithms [85, 86]. Moreover,Beloglazov et al. [87] define an architectural frameworkand principles for energy aware heuristics provision datacenter resources to client applications in cloud computing.Energy efficient resource allocation policies and schedulingalgorithms are introduced by considering QoS expectationsand power usage characteristics.The approach is validated bya performance evaluation study using the CloudSim toolkit.Again, Casalicchio and Silvestri [88] propose autonomic ser-vice provisioning and resource management of cloud-basedsystems especially for IaaS providers. Thus, this system hasfour alternatives implementation which has different degreeof control on the various components of the autonomic cycle.

Ferrer et al. [30] incorporate the service provider (SP) andinfrastructure provider (IP) with toolkit which optimize thewhole service life cycle. Each core component of the toolkitprovides common services, which are needed for servicedeployment and execution. Again, the criteria followed by IPare past performance and legal and security aspects. The cal-culation is conducted by considering a value for each criterionfrom 0 to 1. Finally, the assessment is achieved by implement-ing by Dempster Shafey analytical hierarchy process (DS-AHP) [89] as the service provisioning system is important inthe user’s perspective of cloud service performance. Hence,

for network virtualization it is a vital attribute of next-generation Internet-based service provisioning approach tointegrating networking and cloud computing. Duan [90]introduces a holistic approach of the application of the SOAin network virtualization for composing network and cloudservices and studied modelling and performance analysis onnetwork virtualization for composite network-cloud serviceprovisioning.

7.5. Cloud Service Provisioning Based on MCDM. Multicrite-ria decisionmaking (MCDM) is a well-established area in thefield of operations research and has proven its effectivenessin addressing different complex real-world decision-makingproblems. Rehman et al. [91] present a comparative case studyinvolving infrastructure as a service cloud and use MCDMtechniques to select the best service on the basis of actual per-formance measurements by a third party monitoring serviceagainst five different criteria. We present a comparative studyof service provisioning techniques in Table 2.

Analytical hierarchy process (AHP) is a tool for decisionmakers to be able to do more informed decisions regardingtheir investment in such technologies. The AHP is a mul-tiobjective, multicriteria decision-making approach, whichemploys a pair-wise comparison procedure to arrive at a scaleof preferences among a set of alternatives. AHP enhancesthe decisionmaking towards transform subjective judgmentsinto objective measures [100]. In AHP, an input is asked togive ratios for each pairwise comparison between issues foreach criterion in a hierarchy and also between the criteria[101]. The pairwise comparison results are displayed in ahierarchy with a weight for each criterion, providing bothqualitative as well as quantitative characteristics here. Inaddition, the technique is for order preference by similarity(OPS) to an ideal solution (TOPSIS) to aid service consumersand providers for analyzing available services with fuzzyopinions. Fuzzy TOPSIS methods are now popular in dealingwith imprecise information.

7.6. Algorithmic Techniques. In the area of multiobject opti-mization, genetic algorithm is a famous search method.Genetic algorithm based search is employed to find out thebest service variant among the current context by Vanrompayet al. [102]. Then, the choice services are deployed in an opti-mal way which serves the demands of the service running onmobile systems. Their model has several requirements suchas deployment of an intelligent artificial learningmechanism,scalable variants, resource constraints of mobile devices,bandwidth limitation, and run time QoS properties thatshould be considered. Zhao et al. [103] propose an optimizedtask scheduling algorithm based on genetic algorithm toschedule independent and divisible tasks to adapt to differentcomputation and memory requirements. The algorithm isin the heterogeneous system, and dynamic scheduling isalso considered and accordingly GA is designed to solvecombinational optimization problem. Again, Dutta and Joshi[95] propose a genetic algorithm based approach to costbased multi-QoS job scheduling model in cloud computing.It guarantees the best solution in finite time. A genetic


Table2:Com

paris

onof

servicep

rovisio

ning

techniqu

es.

Servicep

rovisio

ning

techniqu

esFeatures

Solvingapproaches

Attributes

References

Algorith

mic

Aclo

ud-based

compu

tingservices

schedu

lingwith

collabo

rativ

eQoS

requ

irements

Binary

integerp

rogram

ming

metho

dOptim

izationandfairn

ess

Weietal.[92]

Con

sideringqu

alitativ

eeffectso

fcostand

strategy

mod

elNashequilib

rium

underd

ifferent

form

ulations

Capacityandprob

ability

Raoetal.[93]

Runtim

emanagem

entinservice

provision

ingin

IaaS

Distrib

uted

algorithm

Equilib

rium

efficiency

Ardagna

etal.[94]

Costb

ased

multi-QoS

jobschedu

ling

mod

elSoftdeadlin

eand

penalty

cost

Bette

rscheduling

DuttaandJoshi[95]

MCD

M

Task

oriented

resourcesa

llocatio

nRe

ciprocalandindu

cedbias

matrix

Band

width,taskcosts

,and

time

Ergu

etal.[96]

Adistr

ibuted

resource

managem

ent

Con

sideringSL

AandQoS

Realizinguser

needs

Khadd

aj[97]

Adynamicautono

mou

sresou

rce

managem

entand

scalability

PROMET

HEE

architecture

Suitablefor

larged

atac

enters

Yazire

tal.[98]

SLAandPo

licybasedbrokering

Fora

utosele

ctionof

SLAfro

mdifferent

offerings

Machine

learning

algorithm

sSL

Amapping

Redl

etal.(2012)[78]

Know

ledgeb

ased

sourcesa

ndservices

inmOSA

ICproject

Semantic

web

Ontologiesrule

basedsupp

orttoo

lRe

quire

mentsandservices

Amatoetal.[77]

Com

pare

andevaluateclo

udbroker

byCB

Cbenchm

ark

Clou

dservices

election(C

SS)

algorithm

Query

encoding

,k-nearest

neighb

orLe

Duy

etal.(2012)[74]

Bestoff

eringselectionby

brokerageb

ased

architecture

Indexing

techniqu

eB+-tre

eEn

codedandanalyzed,ind

exkey

Sund

areswaran

etal.(2012)[73]

Mon

itorin

ganddetectingSL

Aviolation

DeSVi

architecture

Lowlevelm

etric

stohigh

level

SLAs

Emeakaroha

etal.(2012)[80]

SLAaw

arec

loud

consideringby

data

structureS

LAtre

eSL

aaS,SL

Atre

eSL

Aaw

arep

rovisio

ning

Bouchenak[81],C

hietal.[82]

Apo

licybasedmechanism

ofservice

provider

selection

Assessin

gris

kby

semiautom

ated

syste

mLo

wcostwith

trustand

compliance

PearsonandSand

er[83]

Heuris

ticandho

listic

Energy

awareh

euris

ticsp

rovisio

nof

data

center

resources

Energy

efficientallo

catio

npo

liciesa

ndalgorithm

sPo

wer

usage,QoS

Beloglazov

etal.[87]

Four

architecturalschemas

fora

uton

omic

resource

allocatio

nFo

uralternatived

egreeo

fcon

trol

Autono

micmanagem

ent

Casalicchio

andSilvestri[88]

Optim

ized

servicelife

cycle

for

depend

ableadaptiv

edyn

amicservice

provision

ing

Dem

psterS

hafeyanalytical

hierarchyprocess(DS-AHP)

Pastperfo

rmance,m

aintenance,

security,andlegal

Ferrer

etal.[30]

Accessingfederatedarchitecture

dynamically

Metab

rokerin

gconcept

Heterogeneous

IaaS

aggregation

Kertesze

tal.[99]


algorithm has been developed to provide a better schedulingin a cloud environment. Analysis and a number of resultsshow that it ensures a good profit for the different cloudproviders. The real execution time of job in different systemas well as soft deadline and penalty cost in the algorithm isalso considered.

Wei et al. [92] present a game-theoretic method forscheduling cloud-based computing services with collabora-tive QoS requirements. Game theory is employed to solvethe problem of resource allocation. A binary integer pro-gramming method is proposed to solve the independentoptimization, and an evolutionary mechanism is designedto minimize their efficiency losses. The algorithms considerboth optimization and fairness into account which finallyreveal that Nash equilibrium always exists for game whichhas feasible solutions for resource allocation. Again, Rao et al.[93] propose a game-theoretic approach for the provisioningand operation of the infrastructure by considering qualitativeeffects of cost and strategy model. The Nash equilibriumunder different formulations computes in polynomial timeand derives provisioning choices to ensure the capacity Cwith probability PS. In addition, Ardagna et al. [94] introducea game theory based model for the run time managementin service provisioning problem especially for IaaS providercapacity among multiple competing SaaS.

8. Research Issues and Challenges

This section presents several important open issues andresearch challenges as well as research directions for success-ful service provisioning deployment and implementation.

Service availability becomes more important in adynamic environment. Applications require intensiveinteraction between the end-user and the cloud service.Hence, service disruption, network congestion, poor sig-nal, and node failure are highly undesirable in serviceprovisioning. When a node moves frequently within thenetwork or changes its point of attachment, many mobilecloud applications demand optimal service through themost suitable node. Again, the scalability of services is achallenging aspect of distributed application processing incloud computing. Remote application processing is deficientin the centralised management of the distributed platform. Achallenging issue in local distributed application frameworks(APFs) is the unavailability of centralised resources. Whena remote service provider is unavailable, remote servicesbecome inaccessible, which hinders the objectives ofavailability of services in a distributed computing paradigm.In addition, the QoS requirements are also evolving with theevolution of the cloud, and therefore service provisioningrequires highly reliable good service quality. Similar servicesand functionalities are provided by the different CSPs,which makes it difficult for customers to select the best andmost appropriate service. Optimal provider selection basedon predetermined quality of service (QoS) requirementsbecomes vital. Moreover, the users’ past experiences areexploited in a ranking-based approach to accumulate andidentify the preferences between pairs of services to obtain a

service ranking. Thus, this approach gets benefits from pastuser experience. However, user response does not alwaysreflect true feedback. It is necessary to adjust and justify thefeedback using statistical techniques to make it as error-freeas possible. Furthermore, SLAs are legally binding for bothparties, recognised as the terms and conditions of using theservice. Because of the fast-growing number of promisingservice offers and the lack of a standard specification ofservices, manual service selection is an expensive task,preventing the successful implementation of ubiquitouscomputing on demand. Therefore, automatic methods formatching SLAs are essential. Finally, several mathematicaland statistical methods and model-based issues are proposedand validated. Service provisioning requires a precise andefficient artificial intelligence learningmechanism, and in theplanning stage the number of variants, resource constraints,bandwidth limitations, and properties should of course beconsidered with respect to run time, hardware, and software.

9. Conclusion

Cloud computing is currently an emerging paradigm thatenvisions a new paradigm of “everything-as-a-service,”hence, virtualizes physical resources, infrastructure, andapplications which are being provided through service pro-visioning in the cloud. The growing adoption of cloudservices suggests clear and distinct promises within the cloudindustry. Due to the increasing number of cloud providersand the variety of service offerings, it has become difficultfor new customers to choose the best provisioned services.Therefore, we have clearly identified service provisioningtechniques, mechanisms, and several approaches that mustbe understood to evaluate the provisioned services in termsof user requirements and costs. Hence, continuous serviceprovisioning that satisfies the user requirements is a manda-tory feature for the cloud user and vitally important incloud computing service offerings. Therefore, we reviewedthe state-of-the-art service provisioning objectives, essentialservices, topologies, user requirements, necessary metrics,and pricing mechanisms. In addition, we synthesized andsummarized different provisioned techniques, approaches,andmodels through a comprehensive literature review.More-over, a proposal of the thematic taxonomy of cloud serviceprovisioning is presented. Finally, open research issues arecategorized and identified for future research directions.

Abbreviations

AHP: Analytical hierarchical processCC: Cloud computingCSA: Cloud security allianceCSMIC: Cloud service measurement index

consortiumENISA: European Network and Information

Security AgencyFSM: Finite state machineGA: Genetic algorithmHIPAA: Health insurance portability and

accountability act


IaaS: Infrastructure as a serviceIVF: Intervalued FuzzyMCC: Mobile cloud computingMCDM: Multicriteria decision makingNIST: National Institute of Standards and

TechnologyPaaS: Platform as a serviceQoS: Quality of servicesRVWS: Resources via web services frameworkSAW: Simple additive weightingSecaaS: Security as a serviceSLA: Service level agreementSMI: Service measurement indexSOA: Service oriented architectureSaaS: Software as a serviceTOPSIS: Technique for order preference by similarity

(OPS) to an ideal solution (TOPSIS)VM: Virtual machineVON: Virtualized optical network.

Conflict of Interests

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

Acknowledgments

This work is fully funded by the Malaysian Ministry of Edu-cation under the High Impact Research Grant of Universityof Malaya UM.C/625/1/HIR/MOE/FCSIT/03.

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