Service Measurement Index-Based Cloud Service Selection Using Order Preference by Similarity to Ideal Solution Based on Intuitionistic Fuzzy Values

2022 ◽  
pp. 225-238
Author(s):  
T. Thasni ◽  
C. Kalaiarasan ◽  
K. A. Venkatesh
Keyword(s):  
Service Selection ◽  
Cloud Service ◽  
Ideal Solution ◽  
Intuitionistic Fuzzy ◽  
Order Preference ◽  
Measurement Index

scholarly journals A Heterogeneous QoS-Based Cloud Service Selection Approach Using Entropy Weight and GRA-ELECTRE III

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Mingming Liu ◽  
Yifan Shao ◽  
Chunxia Yu ◽  
Jiacheng Yu
Keyword(s):  
Service Selection ◽  
Cloud Service ◽  
Heterogeneous Data ◽  
Cloud Services ◽  
Entropy Measure ◽  
Entropy Weight ◽  
Electre Iii ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Selection Approach

With the development of cloud computing, more and more resources are provided in the form of cloud services. Then how to select suitable cloud service for users without professional knowledge has become an important issue. Existing cloud service selection models are usually considered as QoS-aware evaluation focused models. In practice, the QoS attributes have problems like subjectivity, vagueness, and uncertainty, and a range of formats are involved to describe QoS attributes. Therefore, it is necessary to consider the heterogeneous formats of QoS attributes in cloud service selection process. The aim of this paper is to develop a novel cloud service selection approach using entropy weight and GRA-ELECTRE III that can handle heterogeneous QoS attributes simultaneously. In the proposed approach, heterogeneous QoS attributes are handled simultaneously by being transformed into intuitionistic fuzzy numbers; the relative weights of QoS attributes are calculated objectively by the extended entropy measure method under intuitionistic fuzzy environment; and cloud services are evaluated by GRA-ELECTRE III integrated method under intuitionistic fuzzy environment. Experimental results show that the proposed approach has good stability and discrimination in dealing with heterogeneous data and can effectively avoid compensation between attributes.

scholarly journals A Selection Method Based on MAGDM with Interval-Valued Intuitionistic Fuzzy Sets

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Gai-Li Xu ◽  
Shu-Ping Wan ◽  
Xiao-Lan Xie
Keyword(s):  
Fuzzy Sets ◽  
Service Selection ◽  
Cloud Service ◽  
Intuitionistic Fuzzy Sets ◽  
Cloud Services ◽  
Gray Relational Analysis ◽  
Attribute Weights ◽  
Intuitionistic Fuzzy ◽  
Magdm Problems ◽  
Interval Valued

As the cloud computing develops rapidly, more and more cloud services appear. Many enterprises tend to utilize cloud service to achieve better flexibility and react faster to market demands. In the cloud service selection, several experts may be invited and many attributes (indicators or goals) should be considered. Therefore, the cloud service selection can be regarded as a kind of Multiattribute Group Decision Making (MAGDM) problems. This paper develops a new method for solving such MAGDM problems. In this method, the ratings of the alternatives on attributes in individual decision matrices given by each expert are in the form of interval-valued intuitionistic fuzzy sets (IVIFSs) which can flexibly describe the preferences of experts on qualitative attributes. First, the weights of experts on each attribute are determined by extending the classical gray relational analysis (GRA) into IVIF environment. Then, based on the collective decision matrix obtained by aggregating the individual matrices, the score (profit) matrix, accuracy matrix, and uncertainty (risk) matrix are derived. A multiobjective programming model is constructed to determine the attribute weights. Subsequently, the alternatives are ranked by employing the overall scores and uncertainties of alternatives. Finally, a cloud service selection problem is provided to illustrate the feasibility and effectiveness of the proposed methods.

scholarly journals Qualitative SMI based Cloud Service Selection using Intuitionistic Fuzzy TOPSIS

2020 ◽  
Vol 9 (1) ◽  
pp. 1289-1296
Keyword(s):  
Service Providers ◽  
Service Selection ◽  
Multicriteria Decision Making ◽  
Cloud Service ◽  
Fuzzy Topsis ◽  
Cloud Services ◽  
Cloud Provider ◽  
Intuitionistic Fuzzy ◽  
Network Connection ◽  
Intuitionistic Fuzzy Topsis

Cloud Computing allows access to a public resource pool on demand and easy network connection for the same. Due to the popularity and profits of using Cloud Services, many organizations are moving to Cloud .So selecting a suitable and best Cloud Provider is a challenge for all the users. Many ranking approaches had been proposed for solving this multicriteria decision making problem like AHP, TOPSIS etc. But many of the works focused on quantitative QoS attributes .But qualitative attributes are also important in the case of many application scenarios where the user may be more concerned about the qualitative attributes. CSMIC has released Service Measurement Index attributes for effectively comparing the Cloud services. The comparison of Cloud Service providers based on SMI attributes which are qualitative in nature by using a ranking approach that handles fuzziness in the dataset is the objective of this paper. The proposed approach uses the MCDM algorithm called Technique for Order Preference by Similarity to ideal Solution and uncertainty is handled by Intuitionistic fuzzy values. The qualitative SMI attributes are used as criteria for ranking the Cloud Services.

TERAPAN KOMBINASI METODE TOPSIS DAN ANALYTICAL HIERARCHY PROCESS PADA PEREKOMENDASIAN PENERIMA BEASISWA PENINGKATAN PRESTASI AKADEMIK (Studi Kasus pada STMIK PPKIA Tarakanita Rahmawati)

2017 ◽  
Vol 8 (2) ◽  
pp. 663 ◽  
Author(s):  
Muhammad Fadlan ◽  
Muhammad Muhammad ◽  
Hadriansa
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Analytical Hierarchy Process ◽  
Ideal Solution ◽  
Microsoft Excel ◽  
Order Preference ◽  
Hierarchy Process

Beasiswa Peningkatan Prestasi Akademik (PPA) sebagai salah satu bentuk dukungan pemerintah Republik Indonesia terhadap dunia pendidikan. Beasiswa yang disalurkan oleh pemerintah melalui Perguruan Tinggi yang ada di Indonesia ini, penyeleksian dan penetapan penerimanya sepenuhnya diserahkan kepada pihak Perguruan Tinggi yang bersangkutan. Tahap inilah yang sangat rentan terjadinya kecurangan. Pada objek penelitian  yang diteliti hingga saat ini proses penyeleksian masih dilakukan dengan menggunakan Microsoft Excel, hal ini tentu saja kurang efektif dan efisien, serta rentan akan terjadinya kesalahan bahkan kecurangan. Untuk itu, diperlukan suatu metodologi dan aplikasi yang tepat dalam melakukan penyeleksian penerima beasiswa tersebut. Decision Support System digunakan sebagai solusi untuk melakukan perekomendasian penerima beasiswa, khususnya dengan menggunakan Metode Technique  for  Order  Preference  by  Similarity  to  Ideal  Solution  (TOPSIS)  dan  Analytical  Hierarcy Process (AHP). Penggunaan kombinasi dua metode tersebut dilakukan agar memiliki tingkat akurasi yang baik jika dibandingkan  dengan menggunakan satu metode. Hasilnya,  aplikasi  decision support system dengan penerapan kombinasi metode Topsis dan AHP berhasil di rancang dan di ujicoba, serta sukses dalam perekomendasian penerima beasiswa PPA dengan menghasilkan data alternatif mahasiswa yang terurut mulai dari nilai preferensi yang paling tinggi 0.764 hingga terendah 0.189. Hasil ini dapat menjadi rekomendasi bagi pengambil keputusan dalam mengambil keputusan yang efektik, efisien dan dapat dipertanggung jawabkan.

Radiator threat evaluation with missing data based on improved technique for order preference by similarity to ideal solution

2021 ◽  
pp. 1-10
Author(s):  
Yu-Heng Xu ◽  
Si-Yi Cheng ◽  
Hu-Biao Zhang
Keyword(s):  
Missing Data ◽  
Prior Knowledge ◽  
Attribute Reduction ◽  
Estimation Algorithm ◽  
Weighted Averaging ◽  
Ideal Solution ◽  
Attribute Weights ◽  
Order Preference ◽  
Improved Technique ◽  
Threat Evaluation

To solve the problem of the missing data of radiator during the aerial war, and to address the problem that traditional algorithms rely on prior knowledge and specialized systems too much, an algorithm for radiator threat evaluation with missing data based on improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been proposed. The null estimation algorithm based on Induced Ordered Weighted Averaging (IOWA) is adopted to calculate the aggregate value for predicting missing data. The attribute reduction is realized by using the Rough Sets (RS) theory, and the attribute weights are reasonably allocated with the theory of Shapley. Threat degrees can be achieved through quantization and ranking of radiators by constructing a TOPSIS decision space. Experiment results show that this algorithm can solve the incompleteness of radiator threat evaluation, and the ranking result is in line with the actual situation. Moreover, the proposed algorithm is highly automated and does not rely on prior knowledge and expert systems.

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