Abstract
Cloud computing has emerged as the most effective distributed computing paradigm. It has grabbed the attention of many organizations owing to its business prospects and significant features like agility and flexibility. Dynamic scalability of cloud services and availability of number of homogenous cloud service providers in market make it difficult for the provider to fix the prices of cloud services, especially on-demand Infrastructure-as-a-Service cloud service instances. It is quite problematic for provider to map the dynamics of prices with the variation in service requirement and satisfying the users’ quality of service requirement simultaneously. At the same time, it is very essential for the provider to determine the lower bound price of the services beyond which he could not afford the provisioning of the services. This paper presents dynamic demand-based pricing model for on-demand IaaS cloud service instances that will assist the provider to dynamically determine the price of provisioning the cloud services by considering the provider’s and users’ utility concurrently. Genetic algorithm is applied for the optimized evaluation users’ request parameters and provider’s computation capacity that will minimize the cost of execution. Experimental results demonstrate that price evaluation is more efficient and users’ utility increases considerably using the proposed framework in comparison with the existing utility-based pricing model.
Similar content being viewed by others
References
Galante G, De Bona LCE, Mury AR, Schulze B, da Rosa Righi R (2016) An analysis of public clouds elasticity in the execution of scientific applications: a survey. J Grid Comput 14(2):193–216
Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4):50–58
Armbrust M, Fox A, Griffith R, Joseph AD, Katz RH, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2009) Above the clouds: a berkeley view of cloud computing, Vol 4, pp 506–522. Technical Report UCB/EECS-2009-28, EECS Department, University of California, Berkeley
Cloud Harmony (2016). https://cloudharmony.com/status
Pal R, Hui P (2013) Economic models for cloud service markets: pricing and capacity planning. Theor Comput Sci 496:113–124
Amazon EC2 (2016). https://aws.amazon.com/ec2/pricing/
Chun SH, Choi BS (2014) Service models and pricing schemes for cloud computing. Clust Comput 17(2):529–535
Suleiman B, Sakr S, Jeffery R, Liu A (2012) On understanding the economics and elasticity challenges of deploying business applications on public cloud infrastructure. J Internet Serv Appl 3(2):173–193
Kaur S, Singh S, Kaushal S, Sangaiah AK (2016) Comparative analysis of quality metrics for community detection in social networks using genetic algorithm. Neural Netw World 26(6):625
Sangaiah AK, Thangavelu AK, Gao XZ, Anbazhagan N, Durai MS (2015) An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm. Appl Soft Comput 30:628–635
Rathore S, Sangaiah AK, Park JH (2017) A novel framework for internet of knowledge protection in social networking services. J Comput Sci. https://doi.org/10.1016/j.jocs.2017.12.010
Prodan R, Wieczorek M, Fard HM (2011) Double auction-based scheduling of scientific applications in distributed grid and cloud environments. J Grid Comput 9(4):531–548
Yeo CS, Venugopal S, Chu X, Buyya R (2010) Autonomic metered pricing for a utility computing service. Future Gen Comput Syst 26(8):1368–1380
Altmann J, Kashef MM (2014) Cost model based service placement in federated hybrid clouds. Future Gen Comput Syst 41:79–90
Truong HL, Dustdar S (2010) Composable cost estimation and monitoring for computational applications in cloud computing environments. Proc Comput Sci 1(1):2175–2184
Buell K, Collofello J (2011) Transaction level economics of cloud applications. In: 2011 IEEE World Congress on Services (SERVICES), pp 515–518. IEEE
Mills KL, Dabrowski C (2008) Can economics-based resource allocation prove effective in a computation marketplace? J Grid Comput 6(3):291–311
Kansal S, Singh G, Kumar H, Kaushal S (2014) Pricing models in cloud computing. In: Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies, p 33
Macias M, Guitart J (2010) Using resource-level information into non-additive negotiation models for cloud market environments. In: 2010 IEEE Network Operations and Management Symposium-NOMS, pp 325–332
Lu H, Wu X, Zhang W, Liu J (2012) Optimal pricing of multi-model hybrid system for PaaS cloud computing. In: Cloud and Service Computing (CSC), 2012 International Conference, pp 227–231
Son S, Sim KM (2012) A price-and-time-slot-negotiation mechanism for cloud service reservations. IEEE Trans Syst Man Cybern Part B (Cybern) 42(3):713–728
Rohitratana J, Altmann J (2012) Impact of pricing schemes on a market for Software-as-a-Service and perpetual software. Future Gen Comput Syst 28(8):1328–1339
Agmon Ben-Yehuda O, Ben-Yehuda M, Schuster A, Tsafrir D (2013) Deconstructing amazon EC2 spot instance pricing. ACM Trans Econ Comput 1:16
Li CF (2011) Cloud computing system management under flat rate pricing. J Netw Syst Manag 19(3):305–318
Wang H, Jing Q, Chen R, He B, Qian Z, Zhou L (2010) Distributed systems meet economics: pricing in the cloud. HotCloud 10:1–6
Allenotor D, Thulasiram RK (2008) Grid resources pricing: a novel financial option based quality of service-profit quasi-static equilibrium model. In: Proceedings of the 2008 9th IEEE/ACM International Conference on Grid Computing, IEEE Computer Society, pp 75–84
Martens B, Walterbusch M, Teuteberg F (2012) Costing of cloud computing services: a total cost of ownership approach. In: 2012 45th Hawaii International Conference on System Science (HICSS), pp 1563–1572. IEEE
Kaufmann A, Dolan K (2015) Price comparison: Google cloud platform vs. Amazon web services. Enterprise Strategy Groups Lab White Paper
Martens B, Walterbusch M, Teuteberg F (2012) Costing of cloud computing services: a total cost of ownership approach. In: Proceedings of the 2012 45th Hawaii International Conference on System Sciences (HICSS’12). IEEE Computer Society, Washington, DC, USA, pp 1563–1572. http://dx.doi.org/10.1109/HICSS.2012.186
Mitchell M (1998) An introduction to genetic algorithms. MIT Press, Cambridge
https://github.com/WorkflowSim/WorkflowSim-1.0. Accessed 23 May 2017
Yeo CS, Buyya R (2007) Pricing for utility-driven resource management and allocation in clusters. Int High Perform Comput Appl 21(4):405–418
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kansal, S., Kumar, H., Kaushal, S. et al. Genetic algorithm-based cost minimization pricing model for on-demand IaaS cloud service. J Supercomput 76, 1536–1561 (2020). https://doi.org/10.1007/s11227-018-2279-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-018-2279-8