ARank: A Multi-agent Based Approach for Ranking of Cloud Computing Services

Jahani, Arezoo; Derakhshan, Farnaz; Khanli, Leyli Mohammad

doi:10.12694/scpe.v18i2.1283

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…Jahani and Mohammad Khanli [47] proposed (NSGA SR) to select candidate services and rank them for the multi-objective optimization problem. However, a rank [48] is developed later for improving the ranking process NSGA_SR [49] and also for finding candidate services meeting user's requirements.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

et al. 2021

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The growing demand for cloud technology brings several cloud service providers and their diverse list of services in the market, putting a challenge for the user to select the best service from the inventory of available services. Therefore, a system that understands the user requirements and finds a suitable service according to user-customized requirements is a challenge. In this paper, we propose a new cloud service selection and recommendation system (CS-SR) for finding the optimal service by considering the user’s customized requirements. In addition, the service selection and recommendation system will consider both quantitative and qualitative quality of service (QoS) attributes in service selection. The comparison is made between proposed CS-SR with three existing approaches analytical hierarchy process (A.H.P.), efficient non-dominated sorting-sequential search (ENS-SS), and best-worst method (B.W.M.) shows that CR-SR outperforms the above approaches in two ways (i) reduce the total execution time and (ii) energy consumption to find the best service for the user. The proposed cloud service selection mechanism facilitates reduced energy consumption at cloud servers, thereby reducing the overall heat emission from a cloud data center.

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Section: Related Workmentioning

confidence: 99%

Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

et al. 2021

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“…Similarly, Sahar et al [19] and Abid et al [13] have used specific QoS parameters for their evaluation and missing other parameters. Arezoo et al [10], Saurabh et al [6], Parhi et al [17], and Sahar et al [19] provide selection for one type of [30], Abid et al [13], and Ibrahim et al [2], all are providing selection for all types of services but have different limitations as mentioned in Table 3. Therefore, in view of the evaluation assessment, we infer that the proposed technique is more efficient and reliable in terms of feedback biasness, fixation of parameters and types of services.…”

Section: A Comparison and Performance Evaluationmentioning

confidence: 99%

Internet Service Selection Using Service Association Factor (SAF)

Rabbani

Aslam

2019

ITC

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The modern organizations are equipping themselves with modern current regime of online application infrastructures through cloud, fog, and edge computing. In the presence of several opportunities, the selection criterion for such Internet-based services becomes vital, especially, when there is no supporting information available. Existing recommender systems provide services by evaluating the quality of service parameters, k-mean clustering, and fuzzy logic techniques on customer's feedback. However, these schemes typically rely on customers' feedback and do not provide any information on the interrelationship between the services. Feedback may be self-generated or biased and leading to improper recommendation to service seekers. To resolve the issue, we propose an innovative service association factor method that calculates the value of interrelationships among services appearing together as a package. This technique is implemented based on an intelligent agent that evaluates the values of the associations on standards and quality attributes. It enables the users to select the best services on their preferences. The proposed agent works on fog environment near to the customers. The technique is tested on leading cloud vendors. The results show that the system meets the desires of service seekers in all service models in an efficient manner.

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“…Agility is the factor that indicates the impact of the cloud ERP service upon the user ability to change direction, strategy or tactics quickly with minimal disruption (Radulescu and Radulescu, 2017; Zoie, 2017; Costa et al , 2013). It allows users to change or expand their service charge free (Jahani et al , 2017). It affords the organizations the capability of changing and expanding within a short time without incurring many expenses.…”

Section: Literature Reviewmentioning

confidence: 99%

Prioritizing the factors affecting cloud ERP adoption – an analytic hierarchy process approach

Meghana

Mathew

Rodrigues

2018

IJOEM

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Purpose In today’s competitive world, the adoption of cloud enterprise resource planning (ERP) has become one of the critical criteria for running a successful business. In this regard, knowing the factors that influence cloud ERP adoption plays a significant role. The purpose of this paper is to rank the factors influencing the cloud ERP adoption in multi-national companies (MNCs) in India. This research would enable the cloud ERP vendors to identify the expectations of MNC customers and develop suitable strategies. Design/methodology/approach The research is being conducted among the cloud ERP consultants of designation middle managers and above from 29 different ERP consulting firms located in India. Analytical hierarchical processing approach was used to rank the factors influencing the cloud ERP adoption in MNCs located in India. The primary factors considered for this study are usability, assurance, agility, security and privacy, and vendor-related factors. Findings The three most preferred criteria were usability, assurance and agility. The five most preferred sub-criteria were data accessibility, availability, user friendliness, scalability, and data backup and recovery. Research limitations/implications The study is limited to the perspective of cloud ERP consultants in India. Practical implications The outcome of this research provides cloud ERP vendors with a better understanding of factors that influence the adoption of cloud ERP in MNCs so that they can develop a focused strategy. From the MNCs point of view, the study provides a guideline on what factors they should consider while going for a cloud ERP implementation. Originality/value The research model is developed based on the cloud ERP literature, and the research results and findings would add value to the body of knowledge.

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ARank: A Multi-agent Based Approach for Ranking of Cloud Computing Services

Cited by 4 publications

References 28 publications

Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

Internet Service Selection Using Service Association Factor (SAF)

Prioritizing the factors affecting cloud ERP adoption – an analytic hierarchy process approach

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