A Multi Criteria Decision Making Method for Cloud Service Selection and Ranking

A Multi Criteria Decision Making Method for Cloud Service Selection and Ranking

Rakesh Ranjan Kumar (Department of Computer Science and Engineering, IIT (ISM), Dhanbad, India) and Chiranjeev Kumar (Department of Computer Science and Engineering, IIT (ISM), Dhanbad, India)
Copyright: © 2018 |Pages: 14
DOI: 10.4018/IJACI.2018070101
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This article describes how with the rapid growth of cloud services in recent years, it is very difficult to choose the most suitable cloud services among those services that provide similar functionality. The quality of services (QoS) is considered the most significant factor for appropriate service selection and user satisfaction in cloud computing. However, with a vast diversity in the cloud services, selection of a suitable cloud service is a very challenging task for a customer under an unpredictable environment. Due to the multidimensional attributes of QoS, cloud service selection problems are treated as a multiple criteria decision-making (MCDM) problem. This study introduces a methodology for determining the appropriate cloud service by integrating the AHP weighing method with TOPSIS method. Using AHP, the authors define the architecture for selection process of cloud services and compute the criteria weights using pairwise comparison. Thereafter, with the TOPSIS method, the authors obtain the final ranking of the cloud service based on overall performance. A real-time cloud case study affirms the potential of our proposed methodology, when compared to other MCDM methods. Finally, a sensitivity analysis testifies the effectiveness and the robustness of our proposed methodology.
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Cloud computing has emerged as upcoming computing paradigm that changes the way of computing, storage and service solution (Mell, & Grance, 2011; Lecznar, & Patig, 2011). In general, cloud computing provides virtual services over the network, through which users use the cloud services on a pay-per usage basis based upon their Quality of Service (QoS) requirement. The service provided by cloud computing to its user is classified into three categories namely Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS) (Buyya, Yeo, & Venugopal, 2009; Kldiashvili, 2014). Public clouds, private clouds and hybrid clouds are three deployment models of cloud computing. The flexibility and agility nature of cloud computing encourage to many leading enterprises like Microsoft,eBay, Amazon, IBM, Google and HP are trying to migrate their existing business to emerging cloud-based virtual service (Mahamme, Railkar, & Mahalle, 2017). Due to large number of cloud service provider offering similar type of service, user(s) have many option for selecting the best cloud services depend on their Qos requirement (Chen, Hung, & Zhang 2013; Zeleny, & Cochrane, 1973). To identify which service is the best for a cloud service user, Quality of Service (QoS) factor need to be evaluated (Tyagi, Som, and Rana 2017; Belagharb, & Boufaida, 2017). QoS represents a set of non-functional attributes of service such as response time, throughput, reliability and security. Furthermore, clients are not aware with how to optimize and estimate their requirements. Multiple QoS factors act as vital role in the cloud service, selection process, so this process considered as MCDM problem (Ardagna, & Wang, 2014; Klepac, 2015). Therefore, an efficient and accurate MCDM approach is required to assurance that the selected cloud services providers satisfy user(s) requirement and prioritize the cloud services based on their ability.

In order to address this problem, we suggest a hybrid MCDM method for QoS based optimal cloud services selection and ranking. In this context, we combine the AHP weight methodology with TOPSIS procedure. Here AHP weight concept has been utilized to assess the objective weights of evaluation criteria and avoids the influence of the subjective factor efficiently (Setiawan, 2014). TOPSIS method deal with any number of cloud alternatives and prioritize the final rank of the cloud service alternatives (Hwang, & Yoon 1981). Thus, the prime objective of this work is to develop an efficient methodology for selecting and ranking the cloud services that are very essential to guarantee that the chosen cloud services must be robust and satisfy the user’s requirement. Here, we summarize some of the distinctive contribution(s) of this paper:

  • Provide a efficient and accurate hybrid method which is capable of handling the complex cloud service selection problems;

  • It is a novel idea for dealing with QoS based cloud service, selection problem by incorporating AHP weight methodology with TOPSIS method;

  • Use real cloud service dataset to evaluate proposed methodology and validate the robustness and efficacy by performing the sensitivity analysis.

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