Abstract
The reverse auction method is widely applied to resource allocation for cloud workflow systems, and it dynamically allocates resources depending on the supply and demand of market. However, during the auction the price of cloud resource is usually fixed, and the resource allocation mechanism cannot adapt to the changeful market. This results in low efficiency of resource utility. In this paper, we first propose a dynamic pricing strategy in reverse auction, and then present an efficient resource allocation mechanism based on dynamic pricing reverse auction. During the auction, providers change resource price according to the trade situation so that the novel mechanism can increase chances of making a deal and improve efficiency of resource utility. In addition, providers improve their competitiveness by lowering price. Simultaneously users are timelier to get resources with the increasing of trade rate. Therefore, users can obtain cheaper resources in shorter time, which decrease monetary cost and completion time for workflow execution. The experiments show that our mechanism can achieve better results in resource utility, monetary cost and completion time compared to BOSS algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Foster, I., Yong, Z., Raicu, I.: Cloud computing and grid computing 360-degree compared. In: Grid Computing Environments Workshop, 2008. GCE 2008, pp. 1–10 (2008)
Juve, G., Deelman, E.: Scientific workflows and clouds. J. Crossroads 16, 14–18 (2010)
Cui, L., Shiyong, L.: Scheduling scientific workflows elastically for cloud computing. In: 2011 IEEE International Conference on Cloud Computing (CLOUD), pp. 746–747 (2011)
Tram Truong, H.T. Chen-Khong, T.: An auction-based resource allocation model for green cloud computing. In: 2013 IEEE International Conference on Cloud Engineering (IC2E), pp. 269–278 (2013)
Vinu Prasad, G., Rao, S., Prasad, A.: A combinatorial auction mechanism for multiple resource procurement in cloud computing. In: 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 337–344 (2012)
Rahmanl, M.A., Rahman, R.M.: CAPMAuction: reputation indexed auction model for resource allocation in grid computing. In: 2012 7th International Conference on Electrical and Computer Engineering (2012)
Qu, H., Ryzhov, I.O., Fu, M.C.: Learning logistic demand curves in business-to-business pricing. In: Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World, pp. 29–40. IEEE Press (2013)
Prasad, A.S., Rao, S.: A mechanism design approach to resource procurement in cloud computing. J. IEEE Trans. Compt. 63, 17–30 (2014)
Fard, H.M., Prodan, R., Fahringer, T.: A truthful dynamic workflow scheduling mechanism for commercial multicloud environments. J. IEEE Trans. Parallel Distrib. Syst. 24, 1203–1212 (2013)
Wood, T., Shenoy, P.J., Venkataramani, A.: Black-box and gray-box strategies for virtual machine migration. In: NSDI, p. 17 (2007)
Gorlach, K.. Leymann, F.: Dynamic service provisioning for the cloud. In: 2012 IEEE Ninth International Conference on Services Computing (SCC), pp. 555–561 (2012)
Shi, X., Zhao, Y.: Dynamic resource scheduling and workflow management in cloud computing. In: Web Information Systems Engineering - Wise 2010 Workshops, pp. 440–448 (2011)
Ludwig, S.A.: Particle swarm optimization approach with parameter-wise hill-climbing heuristic for task allocation of workflow applications on the cloud. In: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 201–206 (2013)
Sharma, B., Thulasiram, R.K., Thulasiraman, P.: Pricing cloud compute commodities: a novel financial economic model. In: 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 451–457 (2012)
Niu, D., Feng, C., Li, B.: Pricing cloud bandwidth reservations under demand uncertainty. In: ACM SIGMETRICS Performance Evaluation Review, pp. 151–162. ACM (2012)
Xu, H., Li, B.: Maximizing revenue with dynamic cloud pricing: the infinite horizon case. In: 2012 IEEE International Conference on Communications (ICC), pp. 2929–2933. IEEE (2012)
Teng, F., Magoules, F.: Resource pricing and equilibrium allocation policy in cloud computing. In: 2010 IEEE 10th International Conference on Computer and Information Technology (CIT), pp. 195–202. IEEE (2010)
Mihailescu, M., Teo, Y.M.: On economic and computational-efficient resource pricing in large distributed systems. In: 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), pp. 838–843. IEEE (2010)
Acknowledgement
This work is partially supported by Australian Research Council Linkage Projects LP0990393 and LP130100324, and Chinese National Natural Science Foundation Project NO 61300169 and the key teacher training project of Anhui University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, X., Liu, X., Zhu, E. (2015). An Efficient Resource Allocation Mechanism Based on Dynamic Pricing Reverse Auction for Cloud Workflow Systems. In: Bae, J., Suriadi, S., Wen, L. (eds) Asia Pacific Business Process Management. AP-BPM 2015. Lecture Notes in Business Information Processing, vol 219. Springer, Cham. https://doi.org/10.1007/978-3-319-19509-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-19509-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19508-7
Online ISBN: 978-3-319-19509-4
eBook Packages: Computer ScienceComputer Science (R0)