A Multi-Criteria Decision Support Model for Cloud Computing Virtual Server Product Selection

Year 2023, Volume: 38 Issue: 4, 939 - 953, 28.12.2023

Onur Koşar , Mehmet Atak

https://doi.org/10.21605/cukurovaumfd.1410269

Abstract

Cloud computing has become a preferred option due to the ease of management of the services it offers. Virtual servers offered in Cloud Computing can be configured according to the needs of the customers. The selection of the targeted product among the virtual servers turns into a multi-criteria decision making problem due to the large number of options and the large number of criteria that need to be evaluated together. In this study, a model is proposed in which the criteria weights are determined by Entropy method and the options are ranked by VIKOR method. The effectiveness of the model is compared with the case where the criteria weights are calculated by Analytic Hierarchy Process with expert opinion. In the developed test environment, different customer demand scenarios randomly generated within Amazon EC2 product configuration boundaries were applied. The results of the methods where the proposed method and criteria were calculated with AHS and ranked with VIKOR by taking expert opinion were compared and it was observed that the same product was recommended 81.21% of the time. It is suggested that the model can be considered as an alternative option for decision makers at the management level who do not have sufficient technical knowledge within the scope of the cloud computing migration problem.

Keywords

Cloud computing, Decision theory, Multi criteria decision making

Bulut Bilişim Sanal Sunucu Ürün Seçiminde Çok Kriterli Bir Karar Destek Modeli

Abstract

Bulut bilişim sunduğu servislerin yönetim kolaylığı ile sıkılıkla tercih edilen bir seçenek haline gelmiştir. Bulut Bilişim’de sunulan sanal sunucular müşterilerin ihtiyacına göre belirlenebilmektedir. Sanal sunucu seçenekleri arasından hedeflenen ürünün seçimi, seçenek sayısının fazlalığı ve bir arada değerlendirilmesi gereken kriterlerin çokluğu sebebi ile çok kriterli karar verme problemine dönüşmektedir. Bu çalışmada karar vericilerin sanal sunucu seçiminde kriter ağırlıklarının Entropi yöntemi ile belirlendiği ve seçeneklerin VIKOR yöntemi ile sıralandığı bir model önerilmiştir. Modelin etkinliği kriter ağırlıklarının uzman görüşü alınarak Analitik Hiyerarşi Süreci ile hesaplandığı durum ile karşılaştırılmıştır. Geliştirilen test ortamında Amazon EC2 ürün konfigürasyon sınırları arasında rastgele oluşturulan farklı müşteri talebi senaryoları uygulanmıştır. Önerilen yöntem ve kriterlerin uzman görüşü alınarak AHS ile hesaplandığı ve VIKOR ile sıralandığı yöntemlerin sonuçları karşılaştırılmış ve %81.21 oranında aynı ürünün önerildiği gözlemlenmiştir. Bulut bilişime göç problemi kapsamında modelin yeterli teknik bilgiye sahip olmayan yönetim kademesindeki karar vericiler için alternatif bir seçenek olarak değerlendirilebileceği önerilmektedir.

Keywords

Bulut bilişim, Karar teorisi, Çok kriterli karar verme

References

• 1. Qian, L., Luo, Z., Du, Y., Guo, L., 2009. Cloud Computing: An Overview. 1th International Conference, CloudCom, Beijing, China, 626-631.
• 2. Sunyaev, A., 2020. Internet Computing. Springer International Publishing, New York, 407.
• 3. Rani, B.K., Rani, B.P., Babu, A.V., 2015. Cloud Computing and Inter-Clouds–Types, Topologies and Research Issues. Procedia Computer Science, 50, 24-29.
• 4. Cardoso, A., Moreira, F., Escudero, D.F., 2018. Information Technology Infrastructure Library and the Migration to Cloud Computing. Universal Access in the Information Society, 17(3), 503-515.
• 5. Ahmad, N., Naveed, Q.N., Hoda, N., 2018. Strategy and Procedures for Migration to the Cloud Computing. 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Skopje, 1-5.
• 6. Adel, A., Reza, S., David, J., 2013. Migration to Cloud Computing-The Impact on IT Management and Security. 1th International Workshop on Cloud Computing and Information Security, 9-11 November 2013, Shangai.
• 7. Sefraoui, O., Aissaoui, M., Eleuldj, M., 2014. Cloud Computing Migration and IT Resources Rationalization. International Conference on Multimedia Computing and Systems (ICMCS), Marrakesh, 1164-1168.
• 8. Moghaddam, F.F., Rohani, M.B., Ahmadi, M., Khodadadi, T., Madadipouya, K., 2015. Cloud Computing: Vision, Architecture and Characteristics. 6th Control and System Graduate Research Colloquium (ICSGRC), Shah Alam, 1-6.
• 9. Xiao-hui, L., Xin-fang, S., 2013. Analysis on Cloud Computing and its Security. 8th International Conference on Computer Science and Education, Colombo, 839-842.
• 10. Moravcik, M., Segec, P., Kontsek, M., 2018. Overview of Cloud Computing Standards. 16th International Conference on Emerging eLearning Technologies and Applications (ICETA), High Tatras, 395-402.
• 11. Lei, Qing, Yingtao J., Mei Y., 2014. Evaluating Open Iaas Cloud Platforms Based Upon Nist Cloud Computing Reference Model. 17th International Conference on Computational Science and Engineering, Chengdu, 1909-1914.
• 12. Samreen, F., Elkhatib, Y., Rowe, M., Blair, G. S., 2016. Daleel: Simplifying Cloud Instance Selection Using Machine Learning. IEEE/IFIP Network Operations and Management Symposium (NOMS’2016), 25-19 April 2016, İstanbul.
• 13. Kritikos, K., Horn, G., 2018. IaaS Service Selection Revisited. 7th IFIP WG 2.14 European Conference (ESOCC 2018), Como, 170-184.
• 14. Shannon, C.E., 1948. A Mathematical Theory of Communication. The Bell System Technical Journal, 27(3), 379-423.
• 15. Opricovic, S., Tzeng, G.H., 2004. Compromise Solution by MCDM Methods: A Comparative Analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455.
• 16. Saaty, T.L., 1991. Some Mathematical Concepts of the Analytic Hierarchy Process. Behaviormetrika, 18(29), 1-9.
• 17. Matlab Matematiksel Hesaplama Programı, 2023. https://www.mathworks.com/products/matlab.html, Erişim tarihi: 10.08.2023, Ankara.
• 18. Amazon EC2 Sanal Sunucu Ürün Listesi, 2023. https://aws.amazon.com/tr/ec2/, Erişim tarihi: 10.08.2023, Ankara
• 19. Ueno, H., Hasegawa, S., Hasegawa, T., 2010. Virtage: Server Virtualization with Hardware Transparency. Euro-Par Parallel Processing Workshops (HPPC’2009), 25-28 August 2009, Delft.
• 20. Ueno, H., Hasegawa, T., Yoshihama, K., 2011. System Performance Improvement by Server Virtualization. The World Congress on Engineering, London, 2-7.
• 21. Uddin, Mueen, Asadullah S., Adamu A., Imran A., 2014. Implementation of Server Virtualization to Build Energy Efficient Data Centers. Journal of Power Technologies, 94(2), 1-10.
• 22. Desai, A., Oza, R., Sharma, P., Patel, B., 2013. Hypervisor: A Survey on Concepts and Taxonomy. International Journal of Innovative Technology and Exploring Engineering, 2(3), 222-225.
• 23. Tanaka, T., Tarui, T., Naono, K., 2009. Investigating Suitability for Server Virtualization Using Business Application Benchmarks. 3rd International Workshop on Virtualization Technologies in Distributed Computing, 15-19 June 2009, Barcelona.
• 24. Lv, H., Dong, Y., Duan, J., Tian, K., 2012. Virtualization Challenges: a View from Server Consolidation Perspective. 8th ACM SIGPLAN/SIGOPS Conference on Virtual Execution Environments, London, 15-26.
• 25. Winarno, I., Ishida, Y., 2015. Simulating Resilient Server Using XEN Virtualization. Procedia Computer Science, 60, 1745-1752.
• 26. Jin, Y., Wen, Y., Chen, Q., 2012. Energy Efficiency and Server Virtualization in Data Centers: An Empirical Investigation. IEEE INFOCOM Workshops, 25-30, Orlando.
• 27. Rashid, A., Chaturvedi, A., 2019. Virtualization and its Role in Cloud Computing Environment. International Journal of Computer Sciences and Engineering, 7(4), 1131-1136.
• 28. Anand, A., Chaudhary, A., Arvindhan, M., 2021. The Need for Virtualization: When and Why Virtualization Took Over Physical Servers. 2019 Advances in Communication and Computational Technology (ICACCT), Singapore, 1351-1359.
• 29. Prodan, R., Ostermann, S., 2009. A Survey and Taxonomy of Infrastructure As a Service and Web Hosting Cloud Providers. 10th IEEE/ACM International Conference on Grid Computing, Banf, 17-25.
• 30. Longo, F., Ghosh, R., Naik, V. K., Trivedi, K. S., 2011. A Scalable Availability Model for Infrastructure-as-a-Service Cloud. 41st IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Hong Kong, 335-346.
• 31. Whaiduzzaman, M., Gani, A., Anuar, N.B., Shiraz, M., Haque, M.N., Haque, I.T., 2014. Cloud Service Selection Using Multi Criteria Decision Analysis. The Scientific World Journal, 1-10.
• 32. Sun, L., Dong, H., Hussain, F.K., Hussain, O.K., Chang, E., 2014. Cloud Service Selection: State-of-the-Art and Future Research Directions. Journal of Network and Computer Applications, 45, 134-150.
• 33. Han, S.M., Hassan, M.M., Yoon, C.W., Huh, E.N., 2009. Efficient Service Recommendation System for Cloud Computing Market. 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, Seoul, 839-845.
• 34. Ur Rehman, Z., Hussain, O.K., Hussain, F.K., 2013. Multi-criteria IaaS Service Selection Based on QoS History. 27th IEEE International Conference on Advanced Information Networking and Applications (AINA), Barcelona, 1129-1135.
• 35. Ur Rehman, Z., Hussain, O.K., Hussain, F.K., 2012. Iaas Cloud Selection Using MCDM Methods. 9th IEEE International Conference on e-Business Engineering, Hangzhou, 246-251.
• 36. Fattah, S.M.M., Bouguettaya, A., Mistry, S., 2020. Signature-Based Selection of IaaS Cloud Services. IEEE International Conference on Web Services (ICWS), Beijing, 50-57.
• 37. Ghule, D., Gopal, A., 2018. Comparison Parameters and Evaluation Technique to Help Selection of Right IaaS Cloud. 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Gorakhpur, 1-6.
• 38. Chauhan, N., Agarwal, R., Garg, K., Choudhury, T. 2020. Redundant IAAS Cloud Selection with Consideration of Multi Criteria Decision Analysis. Procedia Computer Science, 167, 1325-1333.
• 39. Saha, M., Panda, S.K., Panigrahi, S., 2021. A Hybrid Multi-Criteria Decision Making Algorithm for Cloud Service Selection. International Journal of Information Technology, 13, 1417-1422.
• 40. Do Chung, B., Seo, K. K., 2015. A Cloud Service Selection Model Based on Analytic Network Process. Indian Journal of Science and Technology, 8(18), 1-5.
• 41. Lee, Y.H., 2014. A Decision Framework for Cloud Service Selection for SMEs: AHS Analysis. SOP Transactions on Marketing Research, 1(1), 51-57.
• 42. Grgurević, I., Kordić, G., 2017. Multi-criteria Decision-Making in Cloud Service Selection and Adoption. 5th Int. Virtual Res. Conf. Tech. Disciplines, Zagreb, 8-12.
• 43. Gireesha, O., Kamalesh, A.B., Krithivasan, K., Sriram, V.S., 2022. A Fuzzy-Multi Attribute Decision Making Approach for Efficient Service Selection in Cloud Environments. Expert Systems with Applications, 206, 117526.
• 44. Jatoth, C., Gangadharan, G.R., Fiore, U., Buyya, R., 2019. SELCLOUD: a Hybrid Multi-Criteria Decision-Making Model for Selection of Cloud Services. Soft Computing, 23, 4701-4715.
• 45. Ahmadi, J., Toroghi Haghighat, A., Rahmani, A. M., Ravanmehr, R., 2022. A Flexible Approach for Virtual Machine Selection in Cloud Data Centers with AHS. Software: Practice and Experience, 52(5), 1216-1241.
• 46. Ramamurthy, A., Saurabh, S., Gharote, M., Lodha, S., 2020. Selection of Cloud Service Providers for Hosting Web Applications in a Multi-Cloud Environment. IEEE International Conference on Services Computing (SCC), Beijing, 202-209.
• 47. Bibi, U., 2018. Cost Aware Resource Selection in IaaS Clouds. International Journal of Advanced Computer Science and Applications, 9(8).
• 48. Abdel-Basset, M., Mohamed, M., Chang, V., 2018. NMCDA: A Framework for Evaluating Cloud Computing Services. Future Generation Computer Systems, 86, 12-29.
• 49. Soltani, S., Elgazzar, K., Martin, P., 2016. QuARAM Service Recommender: a Platform for IaaS Service Selection. 9th International Conference on Utility and Cloud Computing, Shanghai, 422-425.
• 50. Yamato, Y., 2017. Performance-Aware Server Architecture Recommendation and Automatic Performance Verification Technology on IaaS Cloud. Service Oriented Computing and Applications, 11, 121-135.
• 51. Jiang, Q., 2012. Virtual Machine Performance Comparison of Public IaaS Providers in China. IEEE Asia Pacific Cloud Computing Congress (APCloudCC), Shenzhen, 16-19.
• 52. Yamato, Y., 2018. Server Structure Proposal and Automatic Verification Technology on IaaS Cloud of Plural Type Servers. International Journal of Informatics and Information Systems, 1(2), 97-106.
• 53. Cunha, M., Mendonça, N.C., Sampaio, A., 2017. Cloud Crawler: a Declarative Performance Evaluation Environment for Infrastructure‐as‐a‐Service Clouds. Concurrency and Computation: Practice and Experience, 29(1), e3825.
• 54. Michael, N., Ramannavar, N., Shen, Y., Patil, S., Sung, J.L., 2017. Cloudperf: A Performance Test Framework for Distributed and Dynamic Multi-Tenant Environments. 8th ACM/SPEC on International Conference on Performance Engineering, L'Aquila, 189-200.
• 55. Sachdeva, N., Kapur, P.K., Singh, G., 2016. Selecting Appropriate Cloud Solution for Managing Big Data Projects Using Hybrid AHS-Entropy Based Assessment. International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH), Greater Noida, 135-140.
• 56. Kumar, R.R., Kumari, B., Kumar, C. , 2021. CCS-OSSR: a Framework Based on Hybrid MCDM for Optimal Service Selection and Ranking of Cloud Computing Services. Cluster Computing, 24(2), 867-883.
• 57. Clausius, R., 1879. The Mechanical Theory of Heat. Macmillan, London, 373.
• 58. Shannon, C., 1948. A Mathematical Theory of Communication. Bell System Tech. Journal, 27, 379-423
• 59. Aomar, R.A., 2010. A Combined AHS-Entropy Method for Deriving Subjective and Objective Criteria Weights. International Journal of Industrial Engineering, 17(1), 12-24.
• 60. Wu, R.M., Zhang, Z., Yan, W., Fan, J., Gou, J., Liu, B., Wang, Y., 2022. A Comparative Analysis of the Principal Component Analysis and Entropy Weight Methods to Establish the Indexing Measurement. PloS one, 17(1), e0262261.
• 61. Mukhametzyanov, I., 2021. Specific Character of Objective Methods for Determining Weights of Criteria in MCDM Problems: Entropy, CRITIC and SD. Decision Making: Applications in Management and Engineering, 4(2), 76-105.
• 62. Ayçin, E., Güçlü, P., 2020. BIST Ticaret Endeksinde Yer Alan İşletmelerin Finansal Performanslarının Entropi ve MAIRCA Yöntemleri ile Değerlendirilmesi. Muhasebe ve Finansman Dergisi, 85, 287-312.
• 63. Odu, G.O., 2019. Weighting Methods for Multi-Criteria Decision Making Technique. Journal of Applied Sciences and Environmental Management, 23(8), 1449-1457.
• 64. Huang, J., 2008. Combining Entropy Weight and TOPSIS Method for Information System Selection. IEEE Conference on Cybernetics and Intelligent Systems, Chengdu, 281-1284.
• 65. Saaty, T.L., 1990. How to Make a Decision: the Analytic Hierarchy Process. European Journal of Operational Research, 48(1), 9-26.
• 66. Ameen, R.F.M., Mourshed, M., 2019. Urban Sustainability Assessment Framework Development: The Ranking and Weighting of Sustainability Indicators Using Analytic Hierarchy Process. Sustainable Cities and Society, 44, 356-366.
• 67. Ishizaka, A., Labib, A., 2011. Review of the Main Developments in the Analytic Hierarchy Process. Expert Systems With Applications, 38(11), 14336-14345.
• 68. Darko, A., Chan, A.P.C., Ameyaw, E.E., Owusu, E.K., Pärn, E., Edwards, D.J., 2019. Review of Application of Analytic Hierarchy Process (AHS) in Construction. International Journal of Construction Management, 19(5), 436-452.
• 69. Fidan, Ü., Atak, M., 2020. Analitik Hiyerarşi Süreci ve Veri Önişleme Yoluyla Türkiye’nin Güç Sistemi Portföyünün Planlanması. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 35(4), 1031-1046.
• 70. Borade, A.B., Kannan, G., Bansod, S.V., 2013. Analytical Hierarchy Process-Based Framework for VMI Adoption. International Journal of Production Research, 51(4), 963-978.
• 71. Palcic, I., Lalic, B., 2009. Analytical Hierarchy Process as a Tool for Selecting and Evaluating Projects. International Journal of Simulation Modelling (IJSIMM), 8(1).
• 72. Subramanian, N., Ramanathan, R., 2012. A Review of Applications of Analytic Hierarchy Process in Operations Management. International Journal of Production Economics, 138(2), 215-241.
• 73. Saaty, T.L., 2004. Decision Making-the Analytic Hierarchy and Network Processes (AHS/ANP). Journal of Systems Science and Systems Engineering, 13, 1-35.
• 74. Saaty, T.L., 2008. Decision Making with the Analytic Hierarchy Process. International Journal of Services Sciences, 1(1), 83-98.
• 75. Lin, Y.C., Chen, T., 2020. A Multibelief Analytic Hierarchy Process and Nonlinear Programming Approach for Diversifying Product Designs: Smart Backpack Design as an Example. Journal of Engineering Manufacture, 234(6-7), 1044-1056.
• 76. Opricovic, S., Tzeng, G.H., 2007. Extended VIKOR Method in Comparison with Outranking Methods. European Journal of Operational Research, 178(2), 514-529.
• 77. Ou Yang, Y.P., Shieh, H.M., Leu, J. D., Tzeng, G. H., 2009. A VIKOR-Based Multiple Criteria Decision Method for Improving Information Security Risk. International Journal of Information Technology and Decision Making, 8(02), 267-287.
• 78. Mardani, A., Zavadskas, E.K., Govindan, K., Amat Senin, A., Jusoh, A., 2016. VIKOR Technique: A Systematic Review of the State of the Art Literature on Methodologies and Applications. Sustainability, 8(1), 37.
• 79. Sofiyabadi, J., Kolahi, B., Valmohammadi, C., 2016. Key Performance Indicators Measurement in Service Business: a Fuzzy VIKOR Approach. Total Quality Management and Business Excellence, 27(9-10), 1028-1042.