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A new flexible pricing mechanism considering price–quality relation for cloud resource allocation

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Abstract

Determining the true value of a bid received from a trading opponent with respect to different conditions of the negotiation market is an important challenge in negotiation-based cloud resource allocation. This means that, in some market conditions, the received bid is more valuable than it seems (and vice versa). The image constructed from the price bid of a trading opponent determined with respect to the market conditions is called TVB (True Value of Bid). Moreover, the pricing mechanism that supports the calculation of TVB by a resource provider is called FPM (Flexible Pricing Mechanism). In well-known cloud markets, where resource type instances are supplied with various QoS levels, the mentioned challenge becomes more complicated. This is because picking negotiation strategy based on the calculated TVB should not lead to the rental of resource type instances with a lower QoS at a price higher than that of the same resource type instances with a higher level of QoS. Therefore, the aim of this research is designing a flexible pricing mechanism that supports price–quality relation in the cloud markets where resource type instances with various QoS levels are supplied. The simulation results indicate that the proposed negotiators outperform two other negotiators in names EMDA (Enhanced Market Driven Agent) and FNSSA (Fuzzy Negotiation Strategy Selection Agent).

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References

  • Adabi Se, Adabi Sa (2015) A BBO based procedure for evolving fuzzy rules of relaxed-criteria negotiation in grid resource allocation. IJCSI Int J Comput Sci 12(4):17–35

    Google Scholar 

  • Adabi Se, Mosadeghi M, Yazdani S (2018) A real-world inspired multi-strategy based negotiating system for cloud service market. J Cloud Comput Adv Syst Appl 7(17):1–40

    Google Scholar 

  • Adabi S, Movaghar A, Rahmani AM, Beigy H (2013) Market_based grid resource allocation using new negotiation model. J Netw Comput Appl 36(1):543–565

    Article  Google Scholar 

  • Adabi Se, Movaghar A, Rahmani AM, Beigy H (2014) A new fuzzy negotiation protocol for grid resource allocation. J Netw Comput Appl 37:89–126

    Article  Google Scholar 

  • Ali TES, Ammar H (2016) pricing models for cloud computing services, a survey. Int J Comput Appl Technol Res 5(3):126–131

    Google Scholar 

  • Al-Roomi M, Al-Ebrahim S, Buqrais S (2013) Cloud computing pricing models: a survey. Int J Grid Distrib Comput 6(5):93–106

    Article  Google Scholar 

  • Angelov P (1994) A generalized approach to fuzzy optimization. Int J Intell Syst 9(3):261–268

    Article  Google Scholar 

  • Baranwal G, Kumar D, Raza Z, Vidyarthi DP (2018) A negotiation based dynamic pricing heuristic in cloud computing. Int J Grid Util Comput 9(1):83–96

    Article  Google Scholar 

  • Bhojraj S, Libby R (2005) Capital market pressure, disclosure frequency-induced earnings/cash flow conflict, and managerial myopia. Account Rev 80(1):1–20

    Article  Google Scholar 

  • Dastjerdi V, Buyya, R (2012) An autonomous reliability-aware negotiation strategy for cloud computing environments. In: Proceedings of 2012 12th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGRID 2012), IEEE Computer Society, USA, Washington, DC, pp 13–16

  • Calheiros R, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp (SPE) 41(1):23–50

    Article  Google Scholar 

  • Dastjerdi AV, Buyya R (2015) An autonomous time-dependent SLA negotiation strategy for cloud computing. Comput J 58(11):1–15

    Article  Google Scholar 

  • Fatima Sh, Wooldridge M, Jennings N (2009) An analysis feasible solutions for multi-issue negotiation involving nonlinear utility functions. 8th International conference on autonomous agents and multiagent systems, pp 1041–1048

  • Haberland V, Miles S, Luck M (2017) Negotiation strategy for continuous long-term tasks in a grid environment. Auton Agent Multi-Agent Syst 31(1):130–150

    Article  Google Scholar 

  • Javed B, Bloodsworth P, Rasool RU, Munir K, Rana O (2016) Cloud market maker: an automated dynamic pricing marketplace for cloud users. Future Gener Comput Syst 54:52–67

    Article  Google Scholar 

  • Kim TK (2015) T test as a parametric static. Korean J Anesthesiol 68(6):540–546

    Article  Google Scholar 

  • Osterwalder A (2004) The business model ontology—a proposition in a design science approach. PhD Thesis, Business School of University of Lausanne, Lausanne, Switzerland. http://www.hec.unil.ch/aosterwa/PhD/Osterwalder_PhD_BM_Ontology.pdf Accessed 4 Sep 2019

  • Prasad GV, Prasad AS, Rao S (2018) A combinational auction mechanism for multiple resource procurement in cloud computing. IEEE Trans Cloud Comput 6(4):904–914

    Article  Google Scholar 

  • Rong J, Qin T, Abrishami S, Bo A (2019) Competitive cloud pricing for long-term revenue maximization. J Comput Sci Technol 34(3):645–656

    Article  MathSciNet  Google Scholar 

  • Salehan A, Deldari H, Abrishami S (2017) An online valuation-based sealed winner-bid auction game for resource allocation and pricing in clouds. J Supercomput 73(11):4868–4905

    Article  Google Scholar 

  • Samimi P, Teimouri Y, Mukhtar M (2016) A combinatorial double auction resource allocation model in cloud computing. Inf Sci 357:201–221

    Article  Google Scholar 

  • Se Adabi, Movaghar A, Rahmani AM, Beigy H (2013) Negotiation strategies considering market, time and behavior functions for resource allocation in computational grid. J Supercomput 66(3):1350–1389

    Article  Google Scholar 

  • Sim KM (2013a) Towards a unifying multilateral cloud negotiation strategy. Proc Int Multi Conf Eng Comput Sci I:13–15

    Google Scholar 

  • Sim KM (2013b) Complex and concurrent negotiations for multiple interrelated e-markets. IEEE Trans Cybern 43(1):230–245

    Article  Google Scholar 

  • Sim KM, Ng KF (2007) Relaxed-criteria negotiation for grid resource allocation. Int Trans Syst Sci Appl 6(2):37–46

    Google Scholar 

  • Truong-Huu T, Tham CK (2013) A game-theoretic model for dynamic pricing and competition among cloud providers. Proceeding of the 6th IEEE/ACM international conference on utility and cloud computing, pp 235–238

  • Xu J, Cao J (2014) A broker-based self-organizing mechanism for cloud-market. Network and parallel computing, NPC 2014, Lecture Notes in Computer Science, 8707, pp 281–293

  • Xu H, Li BC (2012) Maximizing revenue with dynamic cloud pricing: the infinite horizon case. Proceeding of the 2012 IEEE international conference on communications, pp 2929–2933

  • Xu H, Li BC (2013) Dynamic cloud pricing for revenue maximization. IEEE Trans Cloud Comput 1(2):158–171

    Article  Google Scholar 

  • Zheng X, Martin P, Brohman K (2012) Cloud service negotiation: concession vs. tradeoff approaches. Proceedings of 2012 12th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGRID 2012), IEEE, USA, Washington, DC, pp 515–522

  • Zich C (2017) More quality-higher price?, 9th IBA Bachelor Thesis Conference, July 5th, University of Twente, Enschede, Netherland. https://essay.utwente.nl/72878/1/Zich_BA_BMS.pdf. Accessed: 4 Sep 2019

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Adabi, S., Alayin, F. & Sharifi, A. A new flexible pricing mechanism considering price–quality relation for cloud resource allocation. Evolving Systems 12, 541–565 (2021). https://doi.org/10.1007/s12530-019-09315-3

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