Selection problem of cloud solution for big data accessing ...dline.info/fpaper/jdim/v14i6/jdimv14i6_3.pdf · mechanisms, such as big data and Cloud computing. According to Gartner

368 Journal of Digital Information Management Volume 14 Number 6 Decemober 2016

Selection problem of cloud solution for big data accessing: fuzzy AHP-PROMETHEE as a proposed methodology

Omar Boutkhoum1*, Mohamed Hanine1 and Tarik Agouti1, Abdessadek Tikniouine1

1 Department of Computer ScienceLaboratory of Information Systems EngineeringFaculty of the Sciences Semlalia, Cadi Ayyad UniversityMarrakesh, Morocco{[email protected]*, [email protected], [email protected], [email protected]}

Journal of DigitalInformation Management

ABSTRACT:At present, many organizations try to gainvalue from their exponential growth of data by implementingnew available technologies, processes and governancemechanisms, such as big data and Cloud computing.According to Gartner and Mackinsey predictions, big datahas been largely adopted in 2013. Effectively, nowadaysthe challenge now is how to ensure an effective analysisand management of large-scale data by minimizing allthe costs related to accessing and processing those data.The huge commitment of hardware and processingresources often needed when using big data, is one ofthe limitations that must be taken into consideration.Since the technology is permanently subject to advancesand development, the question for many businesses ishow they can benefit from big data using the power oftechnique flexibility that Cloud computing can provide. Inthis paper, we propose a decisional methodology basedon Fuzzy Analytic Hierarchy Process (FAHP) andPROMETHEE (Preference Ranking Organization METHodfor Enrichment Evaluations) for comparing, ranking andselecting the most suitable Cloud computing toaccommodate and access big data. Due to the varyingimportance of the used criteria, we develop fuzzy AHPsoftware based on extent analysis method to assign theimportance weights to evaluation criteria, while thePROMETHEE process exploits these weighted criteriaas input to evaluate and rank the decision alternatives.

Subject Categories and DescriptorsI.5 [Pattern Recognition]: Fuzzy set H.2 [DatabaseManagement]

General TermsFuzzy, Big Data, Cloud computing

Keywords: Cloud computing, Big data, Fuzzy AHP, Fuzzy set,PROMETHEE, Decision support system.

Received:11 September 2016, Revised 10 October 2016, Accepted 16October 2016

1. Introduction

Big data and cloud computing are among the technologi-cal revolutions of the time, leading to a major transforma-tion on current IT and imposing signiûcant impacts onscientiûc research, public administration, and so on. Infact, the increasing need of treating huge quantity of datain short times has encouraged the enormous develop-ments of architectures commonly referred to as big dataprocessing systems. In 2013, the American informationtechnology research (Gartner Inc.) listed the ‘‘Top 10 Stra-tegic Technology Trends For 2013’’ and ‘‘Top 10 Critical

Journal of Digital Information Management Volume 14 Number 6 December 2016 369

Tech Trends For The Next Five Years’’, and big Data islisted in both of them. The term ‘‘big Data’’, as explainedby Snijders et al. [1], is used to describe data sets thatare too large, moving too fast and are complex to be workedwith using commonly-available tools. Big data is typicallya massive volume of unstructured, semi structured andstructured data created from distinct organized andunorganized applications, activities and channels suchas digital video, images, sensor data, log files, emails,Tweeter and Facebook, etc. This exponential growth indata [2] [3] means that the frontier is vast. So, if we can’tstore the data, we can’t analyze them. This is why manyorganizations notice that the data they own and how theyuse it can make them distinguished from others. Accordingto the survey of Philip Chen and Zhang [4], around 50% of560 enterprises think big Data will help them in increasingoperational efficiency.

Depending on Gartner, IDC and Mackinsey predictions,20% of global 1000 organizations will establish a strategicfocus on ‘information infrastructure’ by 2015. Indeed, theremarkable changes in the manners adopted byresearches, managers and businesses are due to the useof bigdata. According to this strong motivation, severalefforts have been dedicated to the theme of big data, forexample, in regard to high-throughput big dataapplications, Weichselbraun et al. [5] present a novelmethod for contextualizing and enriching large semanticknowledge bases for opinion mining. Renuet al. [6]explainhow to create data backbones for decision supportsystems using big data and knowledge discovery.Barbieratoet al. [7] investigate the use of Apache Hivequery language for NoSQL databases, as NoSQLbig dataapplications, to evaluate the performances of applications.Moreover, Dabore and Xhafa [8] describe and analyze allrequirements and challenges for next-generation big dataservices confronted in smart cities, with presentation ofnew platform called ‘CAPIM’ to collect and aggregatecontext information on a large scale, and trying to assistusers, citizens and city officials better understand trafficproblems in large cities.

In this context of new generation technologies, some otherstudies have already tried to discuss the subject of movingbig data to the cloud, as a new concept, attempting toimplement this coupling approach in many different areas.For example, Zhang et al. [9] propose two algorithmsstudying the cost-minimizing upload of massive geo-dispersed data for processing into the cloud. Purcell [10]explains that cloud computing, with its hardware andprocessing cost reduction, can offer the promise to smalland medium sized businesses for big data implementation.Demirkan and Delen [11] describe how to put analyticsand big data in the cloud by demonstrating theopportunities and challenges of engineering serviceoriented DSS in the cloud to enable scale, scope andspeed economies. Following these considerations, thecloud computing system acts as a required solution inthe evolution of BI technologies. As a result, severalcontributions (Whaiduzzaman et al. [12], Ruiz-Alvarez and

Humphrey [13])have tried to rank those solutions in termof services with the aim to select the best one for a well-defined use.

However, ranking, prioritizing and selecting the mostsuitable cloud computing to accommodate big data hasnot received much interest in the decisional research area,especially the cloud solutions including the possibility oftransferring and importing large amounts of data. Thiscauses a dilemma for organizations at the level of bigdata projects, and leads them to ask for the optimalchoice, in terms of cloud computing, for their computingneeds.In this context, we propose a decision methodologybased on fuzzy AHP and PROMETHEE in order to helpdecision-makers to make the right decision in rankingand choosing the best cloud computing.This will enablethem toachieve a competitiveness gains by migrating,accessing and processing their big data projects usingall resources and services of the cloud.

This work is organized as follows, section 2 presents somerelated work to the selection problem. Section 3 brieflyexplains the proposed methodology followed in order toreach our goal. Section 4 discusses the advantages ofcoupling big data and cloud computing. Section 5 isdevoted to empirical study illustrating the effectivenessand performance of our decisional approach. We end thepaper by a concluding section.

2. Related Work

The selection problem of cloud computing services is oneof the strategic preoccupation that have attracted manyresearchers (Whaiduzzaman et al. [12], Hussain et al.[14] and Qu et al.[15]). With the rapid evolution of decisionsupport systems, the BI experts estimate that putting bigdata on the cloud has become a real challenge thatbusinesses must take into consideration. For this reason,the selection of cloud solution is considered to be animportant research issue for big data projects.However, to our knowledge, only a little bit of attentionhas been focused on the idea of comparing, ranking andselecting the appropriate cloud solution to accommodatebig data projects.

In this section, we briefly survey some of existing literatureon the large number of contributions devoted to theproblem of selection. Most approaches proposeframeworksbased essentially on fuzzy AHP, PROMTHEEand TOPSIS methods.For instance, Do and Chen[16]present a framework combining fuzzy AHP and fuzzycomprehensive evaluation methods for teachingperformance evaluation. Jing et al. [17], present a novelapproach combining fuzzy and stochastic uncertainty intothe traditional AHP for evaluating ballast water treatmenttechnologies.

Furthermore, an integrated approach using the AnalyticNetwork Process (ANP) with the Balanced Scorecard tofind indicators that can be used to measure companies’


performance and competitiveness is presented by Poveda-Bautista et al. [18]. The integration of fuzzy AHP withother analysis methods especially TOPSIS methodologyis performed by many researchers, such as Taylan [19],trying to use FAHP and TOPSIS method to evaluate theconstruction projects selection and risk assessment.Also, the contribution of Patil and Kant[20]has proposeda fuzzy AHP-TOPSIS framework to identify and prioritizethe solutions of knowledge management adoption in supplychain.

Moreover, Kilic et al. [21]propose a hybrid methodologycombining fuzzy AHP with TOPSIS method for theselection of ERP systems. KARAMI and JOHANSSON[22] use Bayesian networks, sensor allocation, TOPSISand AHP methodologies to integrate automatic and manualranking of options.Other works such as Kabir and Sumi[23] have combined fuzzy AHP with PROMETHEE

methodology to evaluate power substation location.Furthermore, the integration of Analytic Network Process(ANP) and PROMETHEE is illustrated firstly by Peng andXiao [24] to select the best material for a given application,and Kilic [25]for better addressing the ERP selectionproblem. Several other researches such as (Kihoro et al.[26], Calabrese et al. [27] and Vahidi et al. [28]) haveused AHP or fuzzy AHP in their studies especially forhierarchically structuring the problem and assigning thepriority weight to the selected criteria taking into accounthuman thoughts in making the best decision.

3. Proposed Methodology

3.1 The Followed ApproachMany methods of multi-criteria decision-making analysishave been proposed in order to help decision makers totake the right decision. Indeed, in this article, we havechosen the AHP method, as the first process in our

Figure1. ProposedApproach


proposed approach, thanks to its hierarchical structureallowing the decomposition of the problem into sub prob-lems, determining after, the weight of each element toclassify it according to its relative importance. Concern-ing the second process of ranking alternatives, we havechosen the PROMETHEE method due to its interactivityand ability to order and classify complex and difficult al-ternatives.

The followed approach uses two major processes asexplained below (Figure 1):

Process I: This process occurs when decision makersdefine their objectives and specify all criteria needed todetermine their favorable choice. The FAHP process whichhandles the vagueness inherent in the decision makingprocess proceed firstly to structure hierarchically thesecriteria and convert the appreciations of decision makersassigned to each criterion to a precise value using theintegrated fuzzy set theory, then finally, calculate therelative importance/weights of the criteria.

Process II: The objective of this process is to evaluateand rank different actions considered in decision makingbenefiting from the clarity, simplicity and stability of thePROMETHEE method. The preference function for eachcriterion will be defined, and the weighted criteria specifiedin the first process are then considered as input in thisprocess which will allow us toidentify the candidatealternative taking into account the existing needs ofdecision makers.

3.2 Fuzzy AHPThe Analytic Hierarchy Process (AHP), initially introducedby Saaty [29], has becomea powerful and flexiblemethodology in solving complex decision problems. Infact, the AHP process consists in representing a decisionproblem by a hierarchical structure reflecting theinteractions between the various elements of the problem,then using pair-wise comparison judgments to identify andestimate the relative importance of criteria and alternatives.

However, the AHP method has some short comings (Yangand Chen [30]) due to its ineffectiveness when applied toan ambiguous problem with a high uncertainty. Therefore,several researchers such as (Boutkhoum et al. [31];Hanine et al. [32]; Hanine et al. [33];Singh and Tyagi[34];Noori[35]), introduce fuzzy logic into the pairwisecomparison of the AHP to compensate and deal with thistype of fuzzy decision problem.

One of the latest FAHP methodologies is based onChang’s extent analysis. It is relatively easier comparedto many other approaches of FAHP. Hence, in this paperwe prefer to utilize Chang [36] extent analysis method toevaluate the importance weight of each selected criteria.The theoretical fundamentals of Chang’s extent analysison FAHP were defined as follows (Gumus [37]):

Let X = {x1,x2, . . . ,xn} be an object set, and G ={u1,u2,u3,...,um} as a goal set, According to the principles

of Chang’s extent analysis, each object is consideredand extent analysis for each of the goal, gi is performedrespectively. It means that m extent analysis values foreach object can be obtained using the following signs:

1 2, ,...,i i i

mg g gM M M i = 1, 2, ..., n, (1)

wherei

jgM (j = 1, 2, ..., m) are triangular fuzzy numbers.

The followed steps of Chang’s extent analysis can beexamined as explained below:

Step 1: The value of fuzzy synthetic extent with respectto the ith object is defined as :

1

1 1 1i i

m n mj j

i g gj i j

S M M−

= = =

⎡ ⎤= ⊗ ⎢ ⎥

⎣ ⎦∑ ∑∑ (2)

The fuzzy addition operation of m extent analysis values mustbe performed for particular matrix to obtain

1i

mj

gj

M=∑ such that:

1 1 1 1

, ,i

m m m mj

g j j jj j j j

M l m u= = = =

⎛ ⎞= ⎜ ⎟⎝ ⎠

∑ ∑ ∑ ∑ (3)

Then, we perform the fuzzy edition operation of m extentanalysis values for a particular matrix to obtain

1

1 1i

n mj

gi j

M−

= =

⎡ ⎤⎢ ⎥⎣ ⎦∑∑ , such that :

1 1 1 1 1

, ,i

n m n n nj

g i i ii j i i i

M l m u= = = = =

⎛ ⎞= ⎜ ⎟⎝ ⎠

∑ ∑ ∑ ∑ ∑ (4)

And the inverse of the vector in Eq. (4) is computed suchthat

1

1 1

1 1 1

1 1 1, ,i

n mj

g n n ni j

i i ii i i

Mu m l

−

= =

= = =

⎛ ⎞⎜ ⎟⎡ ⎤⎜ ⎟=⎢ ⎥⎜ ⎟⎣ ⎦ ⎜ ⎟⎝ ⎠

∑ ∑∑ ∑ ∑ (5)

Step 2: The degree of possibility of M2 = (l2,m2,u2) ≥ M1 =(l1,m1,u1) is defined as

1 22 1( ) sup min( ( ), ( ))M M y xV M M x yμ μ

≥⎡ ⎤≥ = ⎣ ⎦ (6)

and it can be represented as follows:

22 1 1 2( ) ( ) ( )MV M M hgt M M dμ≥ = ∩ =

(7)

Where d is the ordinate of the highest intersection point

1 if m2 ≥ m10 if l1 ≥ u2l1-u2 otherwise

(m2-u2) - (m1-l1){


between μM1 and μM2.

To be able to compare M1 and M2 we need both the valuesof V(M1 ≥ M2) and V(M2 ≥ M1).

Step 3: The degree possibility for a convex fuzzy numberto be greater than k convex fuzzy numbers Mi (i = 1,2,. .. ,k) can be defined by

V(M ≥ M1, M2,…, Mk) = V[(M ≥ M1) and V(M ≥ M2) and …and M ≥ Mk)], = min V(M ≥ Mi), i=1, 2, …, k. (8)

Assume that, d’(Ai) = minV(Si ≥ Sk). (9)

For k = 1, 2, … , n; k ≠ i. Then the weight vector is givenby W’ = (d’(A1),d’(A2), …,d’(An))

T, (10)where Ai (i = 1, 2, … , n) are n elements.

Step 4: Via normalization, the normalized weight vectorsare W = (d(A1), d(A2), … , d(An))

T (11)

where W is a non fuzzy number.

3.3 PROMETHEEThe Preference Ranking Organization METHod forEnrichment Evaluations (PROMETHEE) was developedby Brans and Vincke[38] in 1985. It has been successfullyimplemented in many researchers’ fields; it is based on apair-wise comparison of alternatives for each criterion. Thismeans that the evaluation of alternatives is performed bydifferent criteria which have to be maximized or minimized(Behzadian et al. [39]). Basically, it can provide completeranking and ordering of alternatives when decision makersneed to choose the most appropriate options.

The PROMETHEE procedure can be described in thefollowing steps:

Assume that ai(i = 1, 2, . . . , m) is a set of m alternatives,and ωj (j = 1, 2, . . . , n) represent the weight of n criteria

Step 1: Calculate the evaluative differences of any twoalternative (ai, ak) with respect to criterion j, denoted asdj(ai,ak), i.e., dj(ai,ak) = fj(ai) - fj(ak).

Step 2: Choose and calculate the preference function foreach criterion:

p(ai,ak) = Fj(dj(ai,ak)), which means the preference ofalternative ai with regard to alternative ak.

Step 3: Determine the aggregated preference functionincorporating the weights:

π (ai, ak) = jFj (dj(ai, ak)) (12)

Step 4: Calculate the leaving and entering outrankingflows. The leaving flows as a measure for the weaknessof the alternative ai, and the entering flows as a measurefor the strength of the alternative ai.

φ−(ai) = (ai, ak)) (13)

(14)

Where h is the number of alternatives.

Step 5: Calculate the net outranking flow:

φ (ai) =φ +(ai) -φ -(ai) (15)

The final ranking of each alternative is obtained byclassifying the net outranking flow φ (ai) from the largestvalue to the lowest value.

4. Cloud Computing and Big Data

4.1 Cloud ComputingRecently, cloud computing has emerged as a newparadigm for hosting and delivering services over theInternet. The emergence of cloud computing represents afundamental change of information and communicationtechnology (ICT) services. The National Institute ofStandards and Technology (NIST) has defined cloudcomputing as a model for enabling convenient, on-demandnetwork access to a shared pool of configurablecomputing resources, e.g. networks, servers, storage,applications, and services that can be rapidly provisionedand released with minimal management efforts or servicesprovider interaction. Cloud adopters are driven by theprospects of increasing agility and gaining access to more

Table 1. Saas, PaaS and IaaS Commercial vendors

φ+(ai) = (ak, ai))

Service model Commercial vendors

SaaS Hotmail, Gmail, Yahoo Mail, TurboTax Online, Twitter, Facebook, Microsoft Office Live, Google Apps, Salesforce.com, Cisco WebEx web conferencing, …

PaaS Amazon Web Services (AWS), Microsoft Azure Services, Google App Engine platform, Salesforce’s Force.com, IBM Cloudburst, Rackspace cloud sites, …

IaaS Rackspace cloud servers, Elastic Block Storage (EBS) and Simple Storage Service (S3), Amazon EC2 (Elastic Com-pute Cloud), Joyent and Terremark, …


computing resource for less money. One of the expectedadvantages of cloud computing is the capability to deliverall the functionality of existing information technologyservices even as it dramatically reduces the upfront costsof computing that keep many organizations fromdeploying many cutting-edge IT services [40].

The North Bridge Venture Partners’ Annual Future of CloudComputing Survey [41] confirms in their report that thedemand for cloud services continues to grow with a doubletraffic in the number of requests per month. According tothis survey, software as a service (SaaS) is in use in earlytwo thirds of organizations with a growth of 15% from 2012,which explains that SaaS leads over IaaS (Infrastructureas a Service) and PaaS (Platform as a Service). In fact,the majority of organizations adopting SaaS are interestedin agility and innovation; those adopting PaaS areinterested in scalability, and companies who see capex(capital expenditure) to opex (operational expenditure) andagility as critical are adopting IaaS technologies.

A short list of commercial vendors for the services modelof the cloud computing is presented in the Table 1:

4.2 Key Players in the Cloud Computing EnvironmentIn the following table (Table 2), we present a list of somekey players that are currently leaders in the field of cloudcomputing. We also cite some of their main characteris-tics and contributions in terms of products, new servicesor innovations. Those key players will be taken into ac-count when ranking cloud computing in section 4 in orderto select the best cloud products to accommodate bigdata projects. We have made the list shorter, but havetried to be eclectic at the same time. Our aim is to highlightsome key players that incorporate tools of migrating andtransferring data in their cloud products. Those tools willsignificantly help in reducing the time requirements aswell as the potential network impact, which clearly showthe difference between weeks and months versus days toget data into the cloud.

4.3 Big Data on the CloudAs Tim Byers of Motley Fool explains in an interview atthe March 2013 South by Southwest (SXSW) Conference,that “big data and cloud computing are becoming one inthe same - cloud resources are needed to support bigdata storage and projects, and big data is a huge businesscase for moving to cloud”. In fact, as big data needs a lotof compute and massive storage, many enterprises worktoday on how they can use the power of technique flexibilityprovided by cloud computing to benefit from big data.Indeed, the relationship between these two technologies(Figure 2), as noticed by Hashem [36], is explained bythe fact that big data can provide the ability to use commodity computing for processing distributed queriesthrough multiple data sets and return, in a timely manner,the resultant sets. On the other side, cloud computingprovides the underlying engine across the use of Hadoopasa class of distributed data-processing platforms, it is alsoknown for bringing speed to innovation, rapidscalability and agility, and a lower total cost of ownershipto this relationship.

More precisely, as discussed by Talia [43], cloudcomputing provides an infrastructure that can serve as aneffective platform to address the variety and complexity ofdata types in order to perform big data analysis. In thiscontext, Bollierand Firestone[44] highlighted the abilityand potential of cluster computing to supply a hospitablebackground for data growth. Nevertheless, the lack of dataavailability, as argued by Miller [45], with an incorrect useof the analytical methods when treating offloaded decisionmay generate wrong and costly decisions. At this point,shipping all enterprise data to the cloud has become easierand faster using cloud provider import service (table 2). Infact, any enterprise can ship its disks containing its datadirectly to the cloud providers, and then, those data willbe loaded in one of their data centers. This last operationmust follow the same security practices when storing dataonline in the cloud.

Figure 2. Usage of cloud computing in big data (see [36])


No one knows the Internet quite like Google. it is thefastest growing cloud provider today, its foray into soft-ware-as-a-service applications for businesses is hasten-ing the industry’s move from packaged software to Web-hosted services. It was doing a bunch of stuff in the cloudincluding running a popular PaaS called Google App En-gine, offering Google Cloud Storage and launching a newbig data cloud app, Google BigQuery. And finally,in thelast year, Google made big waves in cloud computing bylaunching its own IaaS service, the Compute Engine.

Amazon has undoubtedly been one among the pioneersin the cloud arena, offering pay-as-you-go access to vir-tual servers and data storage space. Offers from AmazonWeb Services include the Elastic Compute Cloud (EC2),for computing capacity, and the Simple Storage Service(S3), for on-demand storage capacity. In addition to thesecore offerings, Amazon offers a database Web service(SimpleDB); a Web service for content delivery(CloudFront); and the Simple Queue Service (a hostedservice for storing messages as they travel between com-puters).

Rackspace has a long history of offering hosted datacenter services and is a trusted name in the enterprise. Ithelps organizations to create the infrastructurethat performs best for their business. Rackspace consistsof three major services: Cloud Servers, an Amazon EC2-like service that provides access to virtualized serverinstances; Cloud Files, a storage service; and Cloud sites,a platform for building Web sites.

HP Cloud is the set of cloud solutions that offers manycloud services all available from Hewlett Packard organi-zation (HP). It represents the combination of the anteriorHP Converged Cloud business unit and HP Cloud Ser-vices, which is the OpenStack technology. HP Helion Pub-lic Cloud, as a new feature, is committed to deliveringleading edge public cloud infrastructure, platform services,and cloud solutions for developers, ISVs, partners, ser-vice providers, and enterprises.

Data transfer tools

Using the Offline Disk Import, Google cloud storagehelp organizations in transferring their data set by send-ing Google physical hard drives that it loads into an emptyCloud Storage bucket. The data must be encrypted be-cause it’s loaded directly into Google’s network. Thisoption can be helpful for organizations if they are limitedto a slow, unreliable, or expensive Internet connection.

AWS Import/Export is a service used for transferring largeamounts of data from physical storage devices into AWS.This transfer is made directly to and from storage devicesthrough the Amazon‘s high-speed internal network andbypassing the Internet. For significant data sets, AWSImport / Export is often faster than Internet transfer andmore cost effective than upgrading the connectivity. Datamigration, content delivery and direct data exchange areamong the common use cases of the AWS Import / Ex-port.

Rackspace bulk import for cloud files is a simpler wayto get a lot of data into the cloud by sending Rackspacephysical media to be uploaded directly at the data cen-ters, where “migration specialists” connect the device toa workstation that has a direct link to Rackspace’s CloudFiles infrastructure. Rackspace provides continuous up-dates on the progress of the device and its data, and adedicated migration specialist offers Fanatical Supportthe whole way through.

HP Bulk Import is a new service reducing the time tomarket for applications requiring existing data by allowingusers to easily and quickly load their data into HP CloudBlock Storage or HP Cloud Object Storage. Like the otherservices, HP bulk import let users send and provide harddrives directly to HP’s data center, where data can berapidly uploaded and transferred.

Cloud computing company

Aspera is presented as a leader in high-speed delivery of data to the cloud. It is used in cases where the data is toolarge to transmit and access demands which will not allow the latency inherent in shipping data. Aspera providesseveral services such as the Aspera On-Demand Transfer Solutions which bring cost savings and efficiency gains toorganizations.It is used to move large volumes of data into, out of and within the cloud storage and computing environ-ments. Microsoft Windows Azure, Amazon AWS and Google are among the partners who have already signed acontract to use the Aspera On-Demand Transfer Solutions.

Table 2. Cloud Computing Key players

Table 3 shows the competitiveness gains that can beachieved by organizations upon adoption of bothtechnologies: big data and cloud computing.

5. Empirical Illustration: Which Cloud For Your Big

Data?

As explained before, the aim of this contribution is toinvestigate the ranking and selection of the most suitablecloud computing that include the possibility of transferring


Benefits of Cloud Computing

-Reduce spending on technology infrastructure-Unlimited Storage-Streamline processes-Reduce capital costs-More flexibility to get into new businesses-Disaster recovery-Improve accessibility-Increased collaboration-High efficiency in projects monitoring-Automatic Software Integration-Unified data service

Benefits of Big data

-More accurate data-Timely insights from the vast amounts of data-Perform risk analysis-Keeping data safe-Create new revenue streams-Identification of significant information that can enhance-decision quality-Improved marketing strategy and targeting-Improved business decisions-Reducing maintenance costs-Making cities smarter

Table 3. Summary of the major benefits of adopting big data and cloud computing

Figure 3. The hierarchical analysis structure of the problem

and importing large amounts of data. This will allowdecision makers to migrate, access and analyze theirbig data through the use of cloud computing resources.The rise of cloud computing has been a precursor andfacilitator to the emergence of big data. However, cloudplatforms take many forms and sometimes need to beintegrated with traditional architectures. This causes adilemma for organization at the level of big data projects,and leads them to ask for the optimal choice, in terms ofcloud computing, for their computing needs.

In fact, it is not sufficient to discover solely the richnessand importance of cloud computing especially in the IaaSService, but it is also important to evaluate which is themost suitable cloud computing system in terms of all theservices offered to accommodate and access big dataprojects. In this section, the hierarchical structure usedin this decision problem in order to determine the optimalcloud solution, consists of four levels: as mentioned inFigure 3. The objective is shown in the highest level anddivided into three main criteria, as suggested by thedecision group and some literature review, which are: E-Governance (EG), Business continuity (BC) and Security

(S), while on the third level, the sub criteria are organizedas follows:

EG [EG1, EG2, EG3];BC [BC1, BC2, BC3, BC4];S [S1,S2, S3].

The last level of hierarchy includes alternatives whichrepresent a specimen of five different products of cloudcomputing (most known in the cloud market) withoutmentioning them as follows: CC1, CC2, CC3, CC4, CC5.

5.1 Results and DiscussionsAt this stage, the weights of the selected criteria andsub-criteria are calculated using fuzzy-AHP, and used asinputs in the PROMETHEE process. Then, afterPROMETHEE calculations, evaluation of the alternatives(cloud computing) and selection of the most suitable oneis performed. At the conclusion of this section, our resultsare checked and analyzed in detail using sensitivityanalysis.

5.2 Fuzzy AHP ProcessAfter specifying all needed criteria by decision makers,we focus here to calculate the relative importance/weights


of those criteria. To simplify the calculation steps, weprovide a Java application based on extent analysismethod to obtain the relative weight for each criterion (seeAppendix Afor main criteria weight).

In this context, we present in the following(Tables 4 and6), an example of weight calculation for main criteria usingChang’s extent analysis approach, and Table 5 for linguisticcomparison variables and their equivalent fuzzy numbers.

Objective EG BC S

EG (1, 1, 1) (1, 3, 5) (3, 5, 7)

BC (1/5, 1/3, 1) (1, 1, 1) (1, 3, 5)

S (1/7, 1/5, 1/3) (1/5, 1/3, 1) (1, 1, 1)

TFN scale Linguistic Terms

(7, 9, 9) Very Good (VG)

(5, 7, 9) Good (Gd)

(3, 5, 7) Preferable (P)

(1, 3, 5) Weak advantage (WA)

(1, 1, 1) Equal (EQ)

(1/5, 1/3, 1) Less WA

(1/7, 1/5, 1/3) Less P

(1/9, 1/7, 1/5) Less Gd

(1/9, 1/9, 1/7) Less VG

Table 5. Fuzzy comparison measures (see Gumus[31])Table 4. Comparison matrix for the main criteria

Table 6. Pairwise comparison matrix for the sub-criteria.

V(Scol ≥ SRow) Criteria EG BC S

Column ≥ Row - 0.655 0.089

Column Row 1.00 - 0.481

Column Row 1.00 1.00 -

Table 7. V values result using Eq.7

The values of fuzzy synthetic extent (from Table 4) areevaluated using Eq. 2-5 as follows:

SEG=(5, 9, 13)*(12.454, 18.676, 25.533)-1

= (0.196, 0.482, 1.044)SBC= (6.200, 8.333, 11)* (12.454, 18.676, 25.533)-1

= (0,243, 0,446, 0,883)SS= (1,254,1,343, 1,533)*(12.454, 18.676, 25.533)-1

= (0.049, 0.072, 0.123)

Then these vectors will be used to calculate V values asshown in Table 7 using Eq. 7.

Thus, the weight vector (Eq. 10) from Table 7is calculatedas W’= (1, 0.648, 0.08), and the normalized weight vector

((Eq. 11) is obtained as Wt= (0.57, 0.38, 0.05)T

Following the same systematic approach for the otherevaluations, we get the priority weights correspondinglyfrom Table 6 as explained below:

For sub-criteria (EG1, EG2, EG3): WEG =(0.05, 0.38, 0.57)T

For sub-criteria (BC1, BC2, BC3, BC4): WBC = (0.33, 0.33,0.00, 0.33)T

For sub-criteria (S1, S2, S3): WS = (0.39, 0.00, 0.61)T

These final results of the first process (Table 8) takinginto account all judgments of decision maker’sshow thatthe e-governance criteria have the most important influ-ence when comparedto the other main criteria. The rea-

≥

≥


Criteria Local weight Global weight

EG 0.57 -

EG1 0.05 0.03

EG2 0.38 0.22

EG3 0.57 0.32

BC 0.38 -

BC1 0.33 0.13

BC2 0.33 0.13

BC3 0.00 0.00

BC4 0.33 0.13

S 0.05 -

S1 0.39 0.02

S2 0.00 0.00

S3 0.61 0.03

Table 8. Final criteria weight

son of given more attention to the governance criteria isthat the decisions are mainly interested in increasing theflexibility of governance and monitoring for a company whenusing a distributed decision systems such as cloudcomputing. The global weight of allsub criteria ‘EG3: 0.32’,‘EG2: 0.22’ explains this interests followed by thebusiness continuity criteria, which ensure the highavailability of data, and finally security criteria forencrypting those data.

These analysis results can be compared, for example,with other methodologies dealing with the selection

problems such as ([23], [20] and [19]) using fuzzy AHPas a procedure to determine the relative weights ofevaluation criteria, and fuzzy TOPSIS or PROMETHEEfor ranking alternatives.

5.3 PROMETHEE Process and Final ResultsAs explained in the proposed methodology, the weightsof importance assigned to all criteria using FAHP will beused as input in the PROMETHEE process to evaluateand rank alternatives. Indeed, the Geometrical Analysisfor Interactive Aid (GAIA) is used at this stage as avisualization method complementing the PROMETHEEranking methodology. We have decided to select V-shapepreference function and set the parameter value “p” of theV-shape function to 2 for all the evaluation criteria as shownin Table 10. This will help to get better graphicalvisualization of the final ranking taking into account allevaluation criteria.

The appreciations of decision makers to evaluatealternatives will be performed using linguistic scale forevaluation (Figure 4 & Table 9) as shown in table 10 & 11.

Linguistic Variable Membership functions

Very Insufficient (VI) (0.00, 0.10, 0.25)

Insufficient (I) (0.15, 0.30, 0.45)

Medium Importance (MP) (0.35, 0.50, 0.65)

Important (P) (0.55, 0.70, 0.85)

Very Important (VP) (0.75, 0.90, 1.00)

Table 9. Transformation for Fuzzy membership functions

Figure 4. linguistic scales for evaluation

Table 10. Evaluation matrix of alternatives with respect to each criterion using linguistic terms


Table 11. Evaluation matrix of alternatives using fuzzy membership functions

outranking flow as mentioned in Figure 6, which showedthat ‘CC1’ is the preferred solution with 0.4752 of netoutranking flow, followed by ‘CC3 (0.1436), ‘CC5 (0.0644),‘CC2’ (-0.1312) and finally ‘CC4’ (-0.5520).

According to the decision axis in red (Figure 5), thealternative CC1 is in the direction of ð axis which explainsthat it is the best compromise because it has the highestnet flow, as also explained in Figures 6 related to the finalnet outranking flows.

5.4 Sensitivity AnalysisRegarding the net outranking flows values, the conclusionof the final ranking of the cloud computing to access bigdata projects is provided in Figures(5-6). The mostappropriate cloud solution is the one with the highest net

Figure 5. GAIA Plane generated by visual PROMETHEE

Figure 6. Leaving, entering and net outranking flows

As suggested in several contributions such as (Mousaviet al.[46], Zhu et al. [47], Mosadeghi[48], Boutkhoum etal. [49]), the objective is to measure the influence of deci-sion makers’ risks and check for the possible changes


Figure 7. Walking weight for the five cloud solutions

that may affect the final result listed in Figure 6. For thisreason, a sensitivity analysis is performed through theuse of walking weights technique’s proposed byPROMETHEE II (Figure 7), which will help to obtain betterinsight from our integrated methodology.

Depending on this sensitivity analysis, ten combinationson which we gradually exchange the most importantcriterion’s weight (EG3) with another criterion’s weight andkeeping practically the other weights the same areinvestigated. As shown in Table 12, the original result isdescribed as the main combination. For each combination,the influence on the final ranking is examined. Hence, theresult of sensitivity shows that the ‘CC1’ remains the bestchoice in nearly all combinations except combination 2on which the criterion EG1 (monitoring and servicelevel)has become most important than EG3 (Possibilityto transfer or import data).And Combination 8 on whichthe weight of the most influential criteria such as EG3(Possibility to transfer or import data), EG2 (Dashboard

and Reporting), BC1 (IT capital expenditures), BC2 (On-demand capacity) are minimized, whereas, criteria suchas S1 (Confidentiality) whose weight does not have thesame importance as EG1 (see Table 8) aremaximized.‘CC4’ is ranked as the second best cloudsolution instead of ‘CC3’ in sevencombinations comparedto other solutions. Also, ‘CC2’ is ranked as the third bestcloud solution in fourcombinationsfollowed by ‘CC5’ asthe fourth in seven combinations and finally ‘CC3’ in thelast of ranking with six combinations as the fifth.

The final results of the sensitivity analysis demonstratethat the ranking of the cloud solutions has changedconsiderably on equally weighted criteria, and on thecontribution of each selected criterion. As a result, theconducted sensitivity analysis shows that the weightsaffect the ranking of alternatives, which will enable decisionmakers to enhance their decision-making process byadjusting scoring and weighting, and performingsensitivity analyses.

Table 12. Sensitivity analysis


Appendix A

6. Conclusion

The present study explores the use of fuzzy AHP andPROMETHEE for the selection of the appropriate cloudsolution to access and process big data projects. In fact,big data needs lots of compute and massive storage toprocess and analyze the data. In this work, we have triedto clarify the interest and importance of cloud computing,as a distributed decision system in providing persistent

storage and extremely scalable compute to accommo-date big data. The methodology we adopt here takes intoaccount both the linguistic and qualitative appreciation ofdecision makers, and consists of two processes, fuzzyAHP process is usedto hierarchically structure the prob-lem and generate the criteria weight, and PROMETHEEprocess for final ranking of alternatives. Based on the feed-back received from the decision makers before and afterassessing the validity of results, the combined methodol-


ogy worked very well in evaluating and ranking the mostsuitable cloud computing in a complex multi-criteriadecision situation. And the final result of the net outrankingflow shows that CC1 is the most preferred solution followedby CC3, CC5, CC2 and finally CC4.

For further studies, different multi-criteria decision makingtechniques such as TOPSIS, VIKOR and ELECTRE canbe used and comparison of the results can be presented.Perhaps, the serious limitation of this study is its narrowfocus on only 5 cloud computing products.

In short, the coupling of cloud computing with big datacould be a key tool in solving many decision problems,especially in the financial field, which will be the subjectof a forthcoming research.

AcknowledgmentsThe authors are grateful to Prof. M. Ait Mansour whosesuggestions and remarks on the primary version of thiswork were inspirational. The authors also warmly thankMr. R. Boulguid for pointing out many English correctionsthat lead to the improvement of the paper.

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