Browse > Article
http://dx.doi.org/10.7840/kics.2015.40.9.1802

Optimal Bidding Strategy for VM Spot Instances for Cloud Computing  

Choi, Yeongho (University of Suwon, Department of Computer Science)
Lim, Yujin (University of Suwon, Department of Information Media)
Park, Jaesung (University of Suwon, Department of Information Security)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.40, no.9, 2015 , pp. 1802-1807 More about this Journal
Abstract
The cloud computing service provides physical IT resources to VM instances to users using virtual technique and the users pay cost of VM instances to service provider. The auction model based on cloud computing provides available resources of service provider to users through auction mechanism. The users bid spot instances to process their a job until its deadline time. If the bidding price of users is higher than the spot price, the user will be provided the spot instances by service provider. In this paper, we propose a new bidding strategy to minimize the total cost for job completion. Typically, the users propose bidding price as high as possible to get the spot instances and the spot price get high. we lower the spot price using proposed strategy and minimize the total cost for job completion. To evaluate the performance of our strategy, we compare the spot price and the total cost for job completion with real workload data.
Keywords
cloud computing; auction; QoS; spot instance; bidding strategy;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 H. Kim and H. Kim, "Control algorithm for virtual machine-level fairness in virtualized cloud data center," J. KICS, vol. 38C, no. 6, pp. 512-520, Jun. 2013.   DOI
2 M. Kim and M. Park, "Energy-aware virtual machine deployment method for cloud computing," J. KICS, vol. 40, no. 1, pp. 61-69, Jan. 2015.   DOI
3 Y. Choi, Y. Lim, and J. Park, "Reinforcement learning approach for resource allocation in cloud computing," J. KICS, vol. 40, no. 4, pp. 654-658, Apr. 2015.
4 S. Lee, T. Kim, and J. Lee, "Resource availability-based multi auction model for cloud service reservation and resource brokering system," JKSS, vol. 23, no. 1, pp. 1-10, Mar. 2014.
5 S. Zaman and D. Grosu, "A combinatorial auction-based mechanism for dynamic VM provisioning and allocation in clouds," IEEE Trans. Cloud Comput., vol. 1, no. 2, pp. 129-141, Oct. 2013.   DOI   ScienceOn
6 H. Zhang, B. Li, H. Jiang, F. Liu, A. V. Vasilakos, and J. Liu, "A framework for truthful online auctions in cloud computing with heterogeneous user demands," in Proc. IEEE INFOCOM 2013, pp. 14-19, Turin, Italy, April 2013.
7 P. Athanasions and S. U. Pillai, Probability, Random Variables, and Stochastic Processes, Prentice Hall, 2002.
8 D. Feitelson, Parallel workloads archive: Logs, Retrieved Aug. 15, 2015, from http://www.cs.huji.ac.il/labs/parallel/workload.
9 M. Abundo, V. D. Valerio, V. Cardellini, and F. L. Presti, "QoS-aware bidding strategies for VM spot instances: a reinforcement learning approach applied to periodic long running jobs," in Proc. IFIP/IEEE Int. Symp. Integrated Netw. Management (IM), pp. 53-61, Ottawa, Canada, May 2015.