Abstract
Infrastructure as a Service (IaaS) is a cloud computing service provided over the internet to facilitate the provisioning of various services such as storage, processes, etc. The provider in the IaaS market may offer some purchasing plans including: reservation, on-demand, and spot plans for its resources. As in real scenarios, demand volume for each plan is assumed to be a random variable with a given probability distribution. The provider maximizes its average revenue in the long run by optimal allocation of its resources among the plans. We formulate an Integer Linear Programming (ILP) model with a stochastic constraint, to determine the number of resources to be allocated for each plan in every time slot in the planning horizon. First, fixed prices are considered for each plan, then two mechanisms of Continuous Double Auction and Second Price Sealed Bid Auction are considered for reservations and spot plans, respectively, to obtain market-driven prices of the services. The Seasonal Weighted Moving Average method is used to predict the amount of demand in every slot. Finally, the proposed mechanisms are evaluated through simulations and the results confirm the effectiveness of the methods in maximizing the revenue and overall utilization of the available IaaS capacity.
Similar content being viewed by others
Data availability
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
References
Li Z., Li M.: A hierarchical cloud pricing system. IEEE Ninth World Congress on Serv. 403-411 (2013).
Mell, P., Grance, T.,: The NIST definition of cloud computing.” Natl Inst Standards Technol. 15, 2009.
Fard M.V., Sahafi A., Rahmani A.M., Mashhadi P.S.,: Resource allocation mechanisms in cloud computing: a systematic literature review. IET Software, 2020
Toosi, A.N., Vanmechelen, K., Ramamohanarao, K., Buyya, R.: Revenue maximization with optimal capacity control in infrastructure as a service cloud markets. IEEE Trans. Cloud. Comput. 3(3), 261–274 (2015)
Xu, H., Li, B.: Dynamic cloud pricing for revenue maximization. IEEE Transact. Cloud Comput. 1(2), 158–171 (2013)
Wang W., Li B., Liang B.,: Towards optimal capacity segmentation with hybrid cloud pricing. IEEE 32nd Int. .Conf. Distr. Comput. Syst. 425-434 (2012).
Wang W., Niu D., Li B., Liang B.,: Dynamic cloud resource reservation via cloud brokerage. IEEE 33rd Int. Conf. Distr. Comput. Sys. 400-409 (2013)
Zhang Q., Zhu Q., Boutaba R.,: Dynamic resource allocation for spot markets in cloud computing environments. Fourth IEEE Int. Conf. Uti. Cloud Comput. 178-185 (2011).
Osterwalder A.,: The business model ontology: a proposition in a design science approach. Unpublished dissertation, University of Lausanne, (2004)
Al-Roomi, M., Al-Ebrahim, S., Buqrais, S., Ahmad, I.: Cloud computing pricing models: a survey. Int. J. Grid Distr. Comput. 6(5), 93–106 (2013)
Zaman, S., Grosu, D.: Combinatorial auction-based allocation of virtual machine instances in clouds. J. Parallel Distr. Comput. 73(4), 495–508 (2013)
Wang, R.: Auctions versus posted-price selling. Am. Econ. Rev. 83(4), 838–851 (1993)
Kayalvili, S., Selvam, M.: Hybrid SFLA-GA algorithm for an optimal resource allocation in cloud. Clust. Comput. 22, 3165–3173 (2018)
Belgacem A., Beghdad-Bey K., Nacer H., Bouznad S.,: Efficient dynamic resource allocation method for cloud computing environment. Cluster Comp, (2020).
Jyoti, A., Shrimali, M.: Dynamic provisioning of resources based on load balancing and service broker policy in cloud computing. Clust. Comput. 23(1), 377–395 (2020)
Muthulakshmi, B., Somasundaram, K.,: A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment. Clust. Comput. (2017).
Teylo L., Arantes L., Sens P., Drummond L.M.,: A dynamic task scheduler tolerant to multiple hibernations in cloud environments. Clust. Comput. 1–23, (2020)
Tafsiri, A., Yousefi, S.: Combinatorial double auction-based resource allocation mechanism in cloud computing market. J. Syst. Softw. 137, 322–334 (2018)
Van Den Bossche R., Vanmechelen K., Broeckhove J.,: Optimizing IaaS reserved contract procurement using load prediction. IEEE 7th International Conference on Cloud Computing, pp. 88-95, (2014)
Liu K., Peng J., Liu W, Yao P., Huang Z.,: Dynamic resource reservation via broker federation in cloud service: a fine-grained heuristic-based approach. IEEE Global Communications Conference, pp. 2338-2343, (2014)
Dubois, D.J., Casale, G.: Optispot: minimizing application deployment cost using spot cloud resources. Clust. Comput. 19(2), 893–909 (2016)
Dipu Kabir H.M., Sabyasachi A.S., Khosravi A., Hosen M.A., Nahavandi S., Buyya R.,: A cloud bidding framework for deadline constrained jobs. 2019 IEEE International Conference on Industrial Technology (ICIT), pp. 765–772, (2019)
Lin, W., Liang, C., Wang, J.Z., Buyya, R.: Bandwidth-aware divisible task scheduling for cloud computer. Software: Pract. Exp. 44(2), 163–174 (2014)
Win, T.R., Yee, T.T., Htoon, E.C.: Optimized resource allocation model in cloud computing system. ICAIT 2019, 49–54 (2019)
Taha H.A.,: Operations research: an introduction. Pearson Education Limited, (2014)
ILOG Optimization Academic Initiative, “IBM.” http:// www-01.ibm.com/software/integration/optimization/cplex
Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: A review of auto-scaling techniques for elastic applications in cloud environments. J. Grid Comput. 12(4), 559–592 (2014)
Samimi, P., Teimouri, Y., Mukhtar, M.: A combinatorial double auction resource allocation model in cloud computing. Inf. Sci. 357, 201–216 (2016)
Krishna V.,: Auction theory. Academic press, (2009)
Santhiya, H., Karthikeyan, P.: Price adjustment for double auction based scheduling in grid environment. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 2(3), 1166–1169 (2013)
Zafer M., Song Y., Lee K.-W.,: Optimal bids for spot vms in a cloud for deadline constrained jobs. IEEE Fifth International Conference on Cloud Computing, pp. 75-82, (2012).
Wang, H., Tianfield, H., Mair, Q.: Auction based resource allocation in cloud computing. Multiagent Grid Syst. 10(1), 51–66 (2014)
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shokri Habashi, F., Yousefi, S. & Ghalebsaz Jeddi, B. Resource allocation mechanisms for maximizing provider’s revenue in infrastructure as a service (IaaS) cloud. Cluster Comput 24, 2407–2423 (2021). https://doi.org/10.1007/s10586-021-03262-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-021-03262-y