Abstract
In the Iaas service mode for cloud computing, cloud providers allocate resources in the form of Virtual Machines (VM) to cloud users via auction mechanism. The existing auction mechanism lacks self-adapting adjustment to market changes. An improved online auction mechanism by taking into account the changes in demand during peak and trough period in the allocation scheme has been proposed, so that the auctioneer can make decisions reasonably, improve resource utilization rate, and bring higher profits. Firstly, we present an auction framework for VM allocation based on multi-time period, then prove the mechanism satisfies individual rationality and incentive compatibility. Finally, we try to use the real workload file to perform simulation experiments to verify the effectiveness of the improved online mechanism.
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
Bassamboo, A., Gupta, M., Juneja, S.: Efficient winner determination techniques for internet multi-unit auctions. In: Ifip Conference on Towards the E-Society: E-Commerce, E-Business, E-Government (2002)
Sandholm, T., et al.: CABOB: a fast optimal algorithm for winner determination in combinatorial auctions. Manag. Sci. 51(3), 374–390 (2005)
Zheng, G., Lin, Z.C.: A winner determination algorithm for combinatorial auctions based on hybrid artificial fish swarm algorithm. Phys. Proc. 25(22), 1666–1670 (2012)
Sandholm, T.: Algorithm for optimal winner determination in combinatorial auctions. Artif. Intell. 135(1–2), 1–54 (2002)
Zurel, E., Nisan, N.: An efficient approximate allocation algorithm for combinatorial auctions. In: Acm Conference on Electronic Commerce (2001)
Hoos, H.H., Boutilier, C.: Solving combinatorial auctions using stochastic local search. In: Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence (2000)
Cavallo, R.: Optimal decision-making with minimal waste: strategyproof redistribution of VCG payments. In: International Joint Conference on Autonomous Agents and Multiagent Systems (2006)
Lahaie, S., Parkes, D.C.: On the communication requirements of verifying the VCG outcome. In: ACM Conference on Electronic Commerce (2008)
Dobzinski, S., Nisan, N.: Limitations of VCG-based mechanisms. In: ACM Symposium on Theory of Computing, San Diego, California, Usa, June (2007)
Nisan, N., Ronen, A.: Computationally feasible VCG mechanisms. In: ACM Conference on Electronic Commerce (2011)
Mashayekhy, L., et al.: Incentive-compatible online mechanisms for resource provisioning and allocation in clouds. In: IEEE International Conference on Cloud Computing (2014)
Mashayekhy, L., et al.: An online mechanism for resource allocation and pricing in clouds. IEEE Trans. Comput. 65(4), 1172–1184 (2016)
Huimin, L., Li, Y., Zhang, Y., Chen, M., Serikawa, S., Kim, H.: Underwater optical image processing: a comprehensive review. Mob. Netw. Appl., 1–12 (2017)
Lu, H., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. Mob. Netw. Appl. (2017)
Lu, H., Li, Y., Mu, S., Wang, D., Kim, H., Serikawa, S.: Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet of Things J., 1–8 (2017)
Acknowledgements
Project supported by the National Nature Science Foundation of China (Grant No.61170201, No.61070133, No.61472344); Six-talent peaks project in Jiangsu Province (Grant No.2011-DZXX-032). Innovation Foundation for graduate students of Jiangsu Province (Grant No.CXLX12 0916), Jiangsu Science and Technology Project No. BY2015061-06BY2015061-08, Yangzhou Science and Technology Project No. SXT20140048, SXT20150014, SXT201510013, Natural Science Foundation of the Jiangsu Higher Education Institutions (Grant No.14KJB520041), Innovation Program for graduate students of Jiangsu Province (Grant No.SJZZ16_0261).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Teng, L., Geng, H., Yang, Z., Zhu, J. (2018). A Demand-Based Allocation Mechanism for Virtual Machine. In: Lu, H., Xu, X. (eds) Artificial Intelligence and Robotics. Studies in Computational Intelligence, vol 752. Springer, Cham. https://doi.org/10.1007/978-3-319-69877-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-69877-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69876-2
Online ISBN: 978-3-319-69877-9
eBook Packages: EngineeringEngineering (R0)