An Intelligent Pricing System for Cloud Services aims at Increasing Implementation Simplicity and Flexibility
Subject Areas : ICTMahboubeh Zandieh 1 , Sepideh Adabi 2 , Samaneh Yazdani 3
1 - Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran,
2 - Islamic Azad University, North Tehran Branch
3 - Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran,
Keywords: Cloud computing, Auction, Resource allocation, SVM, AHP, Utilization, Success rate,
Abstract :
Most of the previous pricing models for cloud resources which are defined based on auction suffer from high implementation complexity in real cloud environments. Therefore, the main challenge for researchers is to design dynamic pricing models that can achieve three goals: 1) low computation complexity, 2) high accuracy, and 3) high implementation simplicity in real cloud environments. CMM (Cloud Market Maker) is one of the most popular dynamic pricing models that has two advantages of computation accuracy and the possibility to implement in the real cloud environments. This model calculates the bid price based on a linear function. In designing this linear function, the parameters: buyer’s urgency, number of competitors and number of opponents are considered. Despite the advantages of this pricing function, the importance ratio of the constructor parameters of it is considered the same in various market conditions. Ignoring this issue reduces both system flexibility and computation accuracy in tangible changes in the cloud market. Therefore, the authors of this paper focus on designing a new cloud market-aware intelligent pricing system (which developed in customer side of the market) to tackle the mentioned problem. At the same time, high implementation simplicity of the proposed system should be guaranteed. For this purpose, an agent-based intelligent pricing system by combining support vector machine (SVM) and hierarchical analysis process (AHP) techniques is proposed. Simulation results show the better performance of the proposed solution which is named as DPMA in comparison to CMM.
] R., Hassanzadeh, A., Movaghar, and H.R., Hassanzadeh, “A Multi-Dimensional Fairness Combinatorial Double-Sided Auction Model in Cloud Environment”, 8th International Symposium on Telecommunications (IST), IEEE, pp. 672-677, 2016.
[2] L., Dierks, and S., Seuken, “Cloud Pricing: The Spot Market Strikes Back”, Management Science, 2021, DOI: https://doi.org/10.1287/mnsc.2020.3907.
[3] C., Wu, A.N., Toosi, R., Buyya, and K., Ramamohanarao, “Hedonic Pricing of Cloud Computing Services”, IEEE Transactions on Cloud Computing, 9 (1), pp. 182-196 , 2021.
[4] N., Sultan, “Making Use of Cloud Computing for Healthcare Provision: Opportunities and Challenges”, International Journal of Information Management, 34(2), pp. 177– 184, 2014.
[5] S.R., Dibaj, A., Miri, and S.A., Mostafavi, “A Cloud Dynamic Online Double Auction Mechanism (DODAM) for Sustainable Pricing”, Telecommunication Systems, 75(4), pp. 461-480, 2020.
[6] P., Rad-Jahanbani, S., Adabi, and A., Rezaee, “A New Multi-agent Group-buying Auction for Automated VM-to-Customer Mapping”, Journal of Organizational Computing and Electronic Commerce, 31(1), pp. 35-58, 2020.
[7] R., Ananthakumer, K., Kartheeban, “Resource Allocation Using Dynamic Pricing Auction Mechanism for Supporting Emergency Demands in Cloud Computing”, Journal of Parallel and Distributed Computing, 2021, DOI:https://doi.org/10.1016/j.jpdc.2021.07.016.
[8] J., Rong, T., Qin, and B., An, “Competitive Cloud Pricing for Long-Term Revenue Maximization”, Journal of Computer Science and Technology, 34(3), pp. 645-656, 2019.
[9] L., Mashayekhy, M. M., Nejad, D., Grosu, and A.V., Vasilakos, “An Online Mechanism for Resource Allocation and Pricing in Clouds”, IEEE Transactions on Computers, 65(4), pp. 1172-1184, 2016.
[10] G.V., Prasad, A.S., Prasad, and Sh., Rao, “A Combinatorial Auction Mechanism for Multiple Resource Procurement in Cloud Computing”, IEEE Transactions on Cloud Computing, 6(4), pp. 904-914, 2015.
[11] S., Li, J., Huang and B., Cheng, “A Price-Incentive Resource Auction Mechanism Balancing the Interests Between Users and Cloud Service Provider”, IEEE Transactions on Network and Service Management, 18(2), pp. 2030-2045, 2020.
[12] S., Adabi, F., Alayin, and A., Sharifi, “A new flexible pricing mechanism considering price-quality relation for cloud resource allocation”, Evolving Systems, 12, pp. 541-565, 2021.
[13] B., Javed, P., Bloodsworth, R.U., Rasool, K., Munir and O., Rana, “Cloud Market Maker: An Automated Dynamic Pricing Marketplace for Cloud Users”, Future Generation Computer Systems, 54, pp. 52-67, 2016.
[14] R., Lu, Y., Liang, Q., Ling, Ch., Li, and, W., Wu, “Double Auction and Profit Maximization Mechanism for Jobs with Heterogeneous Durations in Cloud Federations”, Journal of Cloud Computing, 10(1), pp. 1-22, 2021.
[15] A., Abdiansah and R., Wardoya, “Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM”, International Journal of Computer Applications, 128(3), 2015.
]16[ قاسمیان، نفیسه، آخوندزاده هنزائی، مهدی، "مقایسه روش های شبکه عصبی مصنوعی، ماشین بردار پشتیبان و درخت تصمیم گیری در شناسایی ابر در تصاویر ماهواره¬ای لندست 8"، نشریه علمی-ترویجی مهندسی نقشه برداری و اطلاعات مکانی، دوره هفتم، شماره 4، آبان 1395 .
[17] M., Rahmani, M., Momeni, “Alzheimer Speech Signal Analysis of Persian Speaking Alzheimer’s Patients”, Computational Intelligence in Electrical Engineering, 11(1), pp. 81-94, 2020.
]18[ اشرفی اصفهانی، حمیدرضا، "روشی برای طبقه بندی داده¬ها با استفاده از الگوریتم¬های داده کاوی داده¬های بزرگ"، کنگره ملی تحقیقات بنیادین در مهندسی کامپیوتر و فن آوری اطلاعات،تهران، 1398 .
[19] V.S., Noble, “What is a Support Vector Machine?”, Nature Publishing Group , 24, pp. 1565-1567, 2006.
