Abstract
With the exploding growth in the number of devices and data traffic, cloud networks face challenges such as high speeds and low latency. The traditional edge calculation is to send data that can’t be processed by the local edge server to the remote cloud for processing. This will put great pressure on the remote cloud server, and the data will have relatively large transmission delay through the intermediate device. For this problem, this paper proposes an edge calculation method based on reverse auction algorithm to process the data nearby, and adopts the idea of reverse auction to distribute the overloaded data to the edge server with less load, reduce the transmission delay, improve the user experience, and balance the server load. The final simulation results show that allocating overloaded data to adjacent edge server for processing can make server load balance and significantly reduce transmission delay compared to sending to remote cloud.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, S., Zhou, A., Wei, X., Liu, Y.: Mobile Edge Computing, pp. 53–54. Beijing University of Posts and Telecommunications Press, Beijing (2017)
Yu, R., Ding, J.F., Maharjan, S., et al.: Decentralized and optimal resource cooperation in geo-distributed mobile cloud computing. IEEE Trans. Emerg. Top. Comp. (2016)
Li, J., Zhu, Y.M., Hua, Y.Q., et al.: Crowdsourcing sensing to smartphones: a randomized auction approach. In: 2015 IEEE 23rd International Symposium on Quality of Service, IWQoS, pp. 219–224. IEEE, Washington (2015)
Pu, L.J., Chen, X., Xu, J.D., et al.: Crowd foraging: a QoS-oriented self-organized mobile crowdsourcing framework over opportunistic networks. IEEE J. Sel. Areas Commun. (2017)
Zhang, H.G., Liu B.Y., Susanto, H., et al.: Incentive mechanism for proximity-based mobile crowed service systems. In: The 35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016, pp. 1–9. IEEE, Washington (2016)
Han, Y.Y., Wu, H.Y.: Minimum-cost crowdsourcing with coverage guarantee in mobile opportunistic D2D networks. IEEE Trans. Mob. Comput. (2017)
Cao, C., Lu, Z., Ma, X.: Optimization of equipment energy consumption under cloud-end fusion. China Comput. Soc. Newsl. 12 (2016)
Feng, J., Li, G., Feng, J.: A Review of crowdsourcing technology research. Chin. J. Comput. (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, X., Zhang, Z., Wang, Y., Wang, G. (2018). Resource Allocation Based on Reverse Auction Algorithm in Edge Computing Environment. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11065. Springer, Cham. https://doi.org/10.1007/978-3-030-00012-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-00012-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00011-0
Online ISBN: 978-3-030-00012-7
eBook Packages: Computer ScienceComputer Science (R0)