Analysing the SaaS Product Using Multifactor Framework

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1 Volume 118 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu Analysing the SaaS Product Using Multifactor Framework JANANI M Department of Computer Sciences Technology Karunya Institute of Technology and Sciences Coimbatore, India janani@karunya.edu.in KARTHIKEYAN P Department of Computer Sciences Technology Karunya Institute of Technology and Sciences Coimbatore, India karthikeyanp@karunya.edu Abstract Software as service (SaaS) provides already created a software application like service, storage service and finance application services to the cloud consumers, considering the cloud consumer requirements and cloud service providers specification selecting SaaS product is cumber some task for the consumer. In this paper we propose a SaaS product evaluation using a Multifactor Framework. Multifactor frame work uses reliability, availability performance and security to evaluate the SaaS product. This proposed system facilitates customer to choose suitable and the best SaaS product based on their specification. This frame work also give feedback to cloud provider to improve their SaaS product. Keywords SaaS; Multi-factor; MCDM; CSU; CSP; Performance; Security; Availability; Reliability I. INTRODUCTION Cloud computing consist of hardware and software resources which is one of the enticing technologies due to its flexible, scalable and cost efficient access to cloud service model (CSM). There are three main cloud service models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). IaaS is the lowest level cloud service which provides virtualized computing resources over the internet. PaaS is used to deliver a platform to the users. SaaS is a software licensing and delivery model over the web on a subscription basis. It has more advantages than IaaS and PaaS. It is reducing the software deployment issues, maintenance cost and improving the scalability and integration. Then the new upgrade package is less cost compare to traditional package. Currently several number of Cloud Service Providers (CSP) providing a wide range of SaaS services (Google Apps, Workday and Salesforce) to their customers. But the critical issue is to select an appropriate cloud service based on the customer s requirements. The customer s requirements may be anything related to their business functional or non-functional requirements. If it is non-functional requirement according to propose system it should satisfy the some quality factors such as performance, security, reliability, availability. Requirement will differ from customer to customer. Some customers they want to know about any one of the SaaS service. Some of them want to compare each and every SaaS service at last they will select the best one. It has become a challenge to determine which CSP they should use and what should be the selection criteria. Surveys related to SaaS service and SaaS selection have provided details about the present state and growth trends in terms of the adoption of SaaS Product selection. But the research work so far used different methods for evaluating each SaaS product factors individually based on their specification. While selecting SaaS service that evaluated data should be customizable and it should not be limited to individual factors. Few studies have discussed in depth the difficulty of deciding on a SaaS product. To solve this uncertainty of user to accept a new technology, this paper proposes a multifactor framework for analyzing the SaaS product factors such as reliability, performance, availability and security together As of now this has not been done in any SaaS adoption. Several SaaS Products are available for single business operation but it is very difficult for any potential customer to select the best SaaS product based on their various requirements. Customizability and optimization are two main factors that we want to achieve today in any SaaS product and the challenge in considering different factors simultaneously for fine tuning the SaaS product with respect to a highly customizable multi-factor framework is the need of current technological era. 777

2 The remainder of the paper is organized as follows: The next section depicts the related work. The Section 3 gives a brief description of the proposed framework. The Section 4 is the proposed algorithm and section 5 concludes the paper. II. LITERATURE SURVEY Functional and nonfunctional are the two types of quality of service which a user can have. Factors like user experience and security are not easily quantifiable. Moreover, deciding on the services which exactly match with all the functional and nonfunctional requirements is rather a complex process. To reduce the complexity, one of the most widely used mechanisms is Analytical Hierarchy Process (AHP) which is related to Multi Criteria Decision Making (MCDM). AHP can be adapted to multiple number of factors with multiple number of sub factors and it is used for problem solving [1]. AHP method is mainly used to deal with a very unbalanced scale of judgment. Success of the method depends on the decision maker s subjective judgment, selection and preferences. But the standard AHP still cannot reflect the human thinking style which is rather complex. FAHP was developed to solve the hierarchical fuzzy problem so that it won t be affecting the performance of the framework [4]. The framework proposed using four particular virtual team for evaluating and selecting a SaaS software package. Those are Virtual Team Benefit, Opportunity, Cost and Risk (VT-BOCR), Analytic Network Process (ANP) and infrastructure set up in VT became easier under the networked computing environment and it can facilitate customer to select SaaS software packages by using different distributed high performance computing resource. ANP is not making any assumption about inner independence or outer independence within a cluster [2]. The purpose of the framework is measuring the performance using Balanced Score Card (BSC) and Fuzzy Delphi Method (FDM). It facilitates to evaluate the long term performance of a firm. Both financial and non-financial metrics are included in performance evaluation. The performance perspective measures are determined by organizational visions and strategies. The main reason of the performance perspective evaluation is to measure organizational performance [4]. To find the security of cloud service among linear, non-linear equations and fuzzy logic systems, the security evaluation rules should assist the customers at least in two ways. First one assists the development of security metrics and the second one helps in providing a systematic way to analyze the multiple security indexes. Under the security the sub factors are transparency and malicious insider in the framework. A set of linear equations can be used to compute the security score and to get the optimal security index scores the non-linear equations and fuzzy logic systems are used. A fuzzy logic system can be used to explain non-linear mapping on an input dataset to produce bulk output data [6]. To evaluate security in SaaS adoption for core and non-core business operation an integrative framework survey method used for calculating wide variety of data from different industries. The disadvantage of the system is that the SaaS vendor s service quality is not up to the mark and this led to a decline in the number of people who has adopted SaaS. These findings stipulate that SaaS sellers should enhance their service quality and should provide their clients a good control over their SaaS. The survey of the impact of this method was limited only to the current customers and the samples were restricted to firms in a single geographical area [7] [13]. The objective of the analytical framework is to maximize the operating revenue and reduce the power consumption of SaaS clouds. It helps cloud service user to select SaaS services on time without any disturbance. So that the Lyapunov Optimization Technique was used which can concurrently make important control decisions. Join the shortest queue request routing strategy was used for managing the server load and reducing service delays of acknowledged request [3]. Dynamic Service Composition method is implemented to select an appropriate cloud service according to personalization configuration process by using service selection component. To generate the connection for each recommended service, an automatic configuration element is applied [8]. Goal, Question, Metric (GQM) method is used to evaluate the service and quantify their performance. The objective of this system is to analyze the performance evaluation of cloud security service based on a set of quantitative evaluation metrics. In a cloud broker side, objective evaluation can be easily automated. In order to identify the limitations and improve their performance real self-evaluation is used. Cloud Service User (CSU) can know the weight of service and requirement of service. The limitation of GQM method is that the CSPs should continuously asses the level of their security services [10]. Holistic Model function, auditability, governability and inter-operability (FAGI) approach is needed to manage security approach. To save time, cost and effort, SaaS risks can be properly managed through a holistic approach which will help CSUs to engage and select a trusted CSP and leverage the security capabilities from SaaS vendor [11]. Number of existing approach used separate methods for calculating each and every factor of the product. But proposed system approaching the one method (AHP) to evaluate the multifactor of the product. AHP is one of the MCDM technique which is used for decision making process. AHP is structured technique. It derives ratio scales from paired comparisons and allows some small inconsistency in judgments. It used for analyzing complex decisions based on product attributes and in the end it will be evaluate multifactor together. III. PROPOSED FRAMEWORK The framework proposed for SaaS product reliability, security, availability performance evaluation is named as Multifactor Framework. This framework evaluates comparative multi-parameter of the SaaS products based on the pair-wise preferences between the attributes provided by the customers using AHP (Analytic hierarchy process). Literature survey indicates that existing frameworks evaluate and select SaaS product with respect to only one main factor and one or two sub factors. But the proposed multifactor framework will help the user with multiple number of main factors and sub factors using pairwise comparisons and user based preferences. Figure.1 displays the hierarchical ordering of different factors in consideration. 778

3 1) User preference layer, 2) Multifactor evaluation layer, 3) Database layer. Fig: 1. Hierarchical ordering of different factors in consideration Performance of a SaaS product means how fast a cloud service provider can react to any service request from user. Then security is used as a quantitative way to compare and monitor the position of security and data privacy obtained by a cloud service user and current security level of a computing environment. Reliability is the capability of system to perform consistently without failure during any given condition and/or time. The percentage of time a customer can access the service depends on the particular period of time taken by cloud service provider s failure and pervious service experienced by cloud service users as well as availability. There is one case study which is the Software Reliability Growth Modelling (SRGM) Operational Reliability is able to evaluate by relating observed failures to total number of invocations and the potential reliability improvement can be significant based on SRGMs capability to withstand failure and fluctuations in the workload [16]. But at the same time the case study implies some negative impacts also. Some of them are failures related to business logic and the approach is not tested in any realistic industries. To overcome this problem, the proposed system gives a chance to the user to select business requirements along with user preferences. Based on user s preferences and requirements the evaluation will be calculated by the evaluation layer in the proposed framework. This same procedure will be followed for each factors. The purpose of the proposed system is to design a multi-factor framework for presenting the multifactor evaluation, updation, and ranking of SaaS product which facilitates customer to select SaaS product based on their requirements and preferences. The preferences are used to assign weights for the attributes. User attribute preferences, business requirement, certificate requirement and the shortlisted SaaS products are provided as input and the relative product reliability, security, performance, availability ranking is obtained as the output. Figure.2 displays the proposed framework. The framework has three layers: A. User preference layer The top level layer in multifactor framework consists of SaaS multifactor product list, customer requirement factors and SaaS product evaluation result. In SaaS multifactor product list n number of SaaS product names will be listed with tick box from that user can select required SaaS products. Then in customer requirement factors particular number of main factors and sub factors will be listed with tick box that is for choosing the factors based on user requirements and for giving preference to each main factor and sub factors according to their need. The evaluated result about the selected SaaS product with main factors and sub factors are will be shown in SaaS product evaluation result in that list will be according to user preference. It facilitates the customer to select multifactor based on their individual requirements or business requirements or organizational requirements. User can get evaluated result from next level layer based on existing customers feedback and weighing. So that they can easily get idea about the selected SaaS product. B. Multifactor evaluation layer (MEL) It is the middle level layer in multifactor framework which contains four modules, existing customer feedback and weighing and multifactor evaluation process. In this layer inputs are taken from existing customers and outputs are produced by MEL. There are four modules: Reliability Feedback & weigh module, Security Feedback & weigh module, Performance Feedback & weigh module. Availability feedback & weigh module. Module evaluations of factors and sub factors are done by using pairwise comparison and AHP technique. From existing customer it will collect feedback and weight about each and every product and pass through the weigh & updation module to database layer. To provide feedback that options will be available in feedback field. Such as excellent, good, best, poor, worst. Then to provide weight to each sub factors from their experience they can give float value. For example if a product s main factor (performance) has seven sub factors such as scalability, load balancing, web proxies, adaptability, elasticity, usability, interoperability in this case existing customers can provide values like 0.15, 0.09, 0.06, 0.04, 0.33, 0.17 and 0.16 respectively. The sum of sub factors should be 1. Then they can provide weight to each main factors also. Those are performance, security, reliability, availability. This main factor and sub factor weights will be used to do the pairwise comparison. This sum of the evaluation values will be a final result of the multifactor framework. 779

4 Input: An array of SaaS product (or) a single SaaS product for multifactor evaluation, factor preference and business requirement. Output: Comparative multifactor ranking of multiple SaaS product or single SaaS product. Flowchart: 1. a Multifactor Evaluation. The multi-product evaluation phase of the proposed system starts by reading the multi-product factor evaluation values. First check if multiproduct factor value exists and read the SaaS product array. If it doesn t exists then read single SaaS product. The next step is to read the factor preference value which is for the ranking of main factors and we can initialize factor preference as zero when the preferences are not provided by user. For arranging preference list assign [k][1]= i and [k][2]= j. Fig: 2. Proposed SaaS Framework C. Database layer (DL) This is the third level layer of the framework. The purpose of this layer is data storage. It provides all the data required for the evaluation process to MEL. The continuous feedback and weighing of SaaS product multifactor updation will be split in to each product s main factor from feedback and weight updation module and stored in this layer. It contains: Reliability feedback & weigh database, Security feedback & weigh database, Performance feedback & weigh database, Database of RSP. The following steps are followed for analyzing the SaaS product multifactor framework. Step1: SaaS products are listed in user preference layer (first layer) with factors and sub factors. Step2: From that list the user can select a product and the required number of factors (it contains sub factors) based on their requirement. Step3: In multifactor evaluation layer (middle level layer) the selected factors will proceed to the evaluation process. Step4: For evaluation process, the data are taken from database which is stored in database layer (third layer). Step5: Those databases are gathered from feedback & weigh module from existing customer in MEL. Step6: If evaluation process is finished then the evaluated result will be visible to the customer in user preference layer. Step7: The evaluated result will be stored into database layer for safety and recovery purpose. IV. PROPOSED ALGORITHM Purpose: To evaluate the multifactor of single SaaS product or to evaluate comparative multifactor ranking of multiple SaaS products selected. Flowchart: 1. a Multifactor Evaluation 780

5 If the factor preference value is ready then enter in to looping process. In looping process check if the i th element is not equal to the j th element and the i th element is greater than j th element. If it is true the process will continue up to n number of main factors. Using preference list, the calculated value for main factors are ranked and stored in main factor weight module. The purpose of this sub process is ranking the sub factors. In this the looping process will compare sub factor from one to another using the preference list. This process will continue up to M number of sub factors. Finally the calculated values are ranked and stored into sub factor weights. If factor preference values are not provided by user then assign equal weights to all main factors and sub factors as 1. After factor preference calculation that value will be stored into main factors weights and sub factors weights. After that the algorithm goes into next process of checking multi-product factor evaluation. If it is ready with n number of main factors and m number of sub factor then it will use relative multi-factor matrix and relative multifactor vector for generating and computing the result for all factors. In case multi-factor product evaluation is not ready then that process will multiply the sum of each sub factor performance and its weights for multi-factor evaluation and multiply the sum of each main factor performance and its weights for final evaluation. Flowchart: 2 Feedback Update. The process flow begins with the instantiation of values, which is the data of existing customer collected from various resources. Upon the completion of this, the database layer gets updated with standard data. Flowchart: 3 Factor ranking. Here, the process flow begins with condition checking of n number of products and assign 1 to all diagonal factors of comparison matrix. The next procedure starts with another one condition, where the preference list [I][1] is assigned as R, [I][2] assigned as C & [I][3] assigned as com_mat [R][C] (comparative matrix row and column). The next step does the inversion of comparative matrix row and column assigned as com_mat [C][R]. In the next procedure multiplication of comparative matrix will be stored into prod_mat (product matrix) and using that values each row sum value and total row sum value of product matrix calculation process will get completed. The next procedure will check the loop condition. Finally, each row sum value will be divided by the total row sum value, and it will be stored into EV_[I] (evaluation). Flowchart:1. b Multifactor Evaluation. Flowchart: 2 Feedback Update 781

6 multifactor evaluation, updating, and ranking of SaaS product which facilitates customer to select SaaS product based on their requirements. In other frameworks they have done the evaluation for only one main factor and more than one sub factor. But in this proposed system multiple main factor and sub factors are considered for evaluation. The main objective is to select a SaaS product among n number of SaaS products using its multifactor pairwise comparisons and preferences. This paper proposed a MCDM model for a cloud service selection using AHP. We focus on selecting a SaaS among cloud services. Finally the proposed approach is capable of covering a wide range of decision-making factors based on AHP. The framework will be useful for the providers as it can be used to assess the ranking of their product and also to understand the user expectations from a SaaS product. This will help them to enhance their services and quality of their product. Therefore the proposed method should fit the various requirements of different perspectives and can be applied to develop cloud services for both service providers and users. Flowchart: 3 Factor ranking V. CONCLUSION Selection of a SaaS product is the basic requirement to achieve the financial and operational benefits along with enhanced productivity and efficient innovation. Due to vast growth in cloud computing field many companies consider the possibility of introducing cloud services to improve their business performance, security, reliability and availability. Several SaaS Products are available for single business operation but it makes difficult to customer to select best product based on their requirements. Therefore it is a critical issue to select a suitable cloud service which meets all the business strategies and the objectives of companies. To achieve this issue, design a system for discovering the REFERENCES [1] Saurabh kumar garg, A framework for ranking the cloud computing services. Future Generation Computer System 29 (2013) [2] Daji eragu, A framework for SaaS software package evaluation and selection with virtual team and BOCR of analytic network process. J Supercomput (2014) 67: [3] Fangming liu, On Arbitaring Power- Performance Tradeoff in SaaS Clouds. IEEE TRANSACTIONS on parallel and distributed systems, VOL 25, NO.10, OCTOBER [4] Sangwon lee, Hybrid multi criteria decision making model for cloud service selection problem using BSC, fuzzy Delphi method and fuzzy AHP method. Wireless pers commun (2016) 86: [5] Vincent cho, An integrative framework of comparing SaaS adoption for core & non- core business operations. Inf Syst Front (2015) 17: [6] Syed rizvi, A security evaluation framework for cloud security auditing. J supercomput. [7] Sigi goode, Rethinking the role of security in client satisfaction with SaaS providers. Decision Support System70 (2015) [8] Haolong fan, An integrated personalization framework for SaaS based cloud service. Future Generation Computer Systems. Volume 53, December 2015, Pages [9] Domenico controneo, Automated root cause identification of security alerts: evaluation in SaaS cloud. Future Generation Computer systems. Volume 56, March 2016, Pages [10] Talal halabi, Towards quantification and evaluation of security of cloud service providers. Journal of Information Security and Applications 000 (2017) [11] Changlong tang, Selecting a trusted cloud service provider for your SaaS program. Computers & security 50 (2015) [12] Jaignesh.M, Performance evaluation of cloud service with profit optimization. Procedia Computer Science 54 (2015) [13] Shih-wei chou, Understanding the formation of the SaaS satisfaction from the perspective of security quality. Decision Support Systems. Volume 56, December 2013, Pages [14] Wei-wen wu, Mining significant factors affecting the adoption of SaaS using rough set approach. The Journal of System and Software 84 (2011) [15] Said el kathaali, Performance analysis of multicore VM & hosting clouds SaaS application.computer Standards and Interfaces. Available online 10 July [16] Mohammed. O, Measurement and prediction of SaaS reliability in the cloud IEEE Internationals Conference on Software Quality,Reliability and Security Companion. 782

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