Abstract
With the increasingly diverse and complex demands of the Internet of Things (IoT) devices, terminal equipments have been unable to effectively meet their quality of service (QoS). To resolve this issue, the resource allocation strategy for edge-cloud collaborative computing has been seen as a promising scheme by offloading computation-intensive tasks from IoT devices to edge servers or cloud data center. In this paper, we study the resource collaborative scheduling problem and formulate a truthful online auction mechanism in the mobile edge computing (MEC) system. We propose the objective problem of maximizing the long-term average revenue, subjecting to the task queue stability constraint. Furthermore, we apply Lyapunov optimization techniques to deal with this objective problem, which can be solved without prior information. So as to derive subproblems optimal solutions and obtain effective resource allocation strategy, a revenue maximization online auction (RMOA) algorithm is designed. Theoretical analysis shows that the RMOA algorithm can achieve optimal system revenue approximately while ensuring the stability of the MEC system. In addition, simulation results indicate the effectiveness of the RMOA algorithm and verify the influence of various parameters.
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References
Chen, X., Zhang, Y., Chen, Y.: Cost-efficient request scheduling and resource provisioning in multiclouds for internet of things. IEEE IoT J. 7(3), 1594–1602 (2020)
Zhang, J., Chen, B., Zhao, Y., Cheng, X., Hu, F.: Data security and privacy-preserving in edge computing paradigm: survey and open issues. IEEE Access 6, 18209–18237 (2018)
Gao, Y., Liu, L., Hu, B., Lei, T., Ma, H.: Federated region-learning for environment sensing in edge computing system. IEEE Trans. Netw. Sci. Eng. 7(4), 2192–2204 (2020)
Wu, B., Chen, X., Chen, Y., Lu, Y.: A truthful auction mechanism for resource allocation in mobile edge computing. In: IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 21–30 (2021)
Lei, Y., Zheng, W., Ma, Y., Xia, Y., Xia, Q.: A novel probabilistic-performance-aware and evolutionary game-theoretic approach to task offloading in the hybrid cloud-edge environment. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds.) CollaborateCom 2020, Part I. LNICST, vol. 349, pp. 255–270. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67537-0_16
Kai, C., Zhou, H., Yi, Y., Huang, H.: Collaborative cloud-edge-end task offloading in mobile-edge computing networks with limited communication capability. IEEE Trans. Cogn. Commun. Netw. 7(2), 624–634 (2021)
Zhang, M., Huang, J.: Mechanism design for network utility maximization with private constraint information. In: IEEE Conference on Computer Communications, INFOCOM, Paris, France, pp. 919–927 (2019)
Jiang, W., Li, M., Zhou, X., Qu, W., Qiu, T.: Multi-user cooperative computation offloading in mobile edge computing. In: Yu, D., Dressler, F., Yu, J. (eds.) WASA 2020, Part I. LNCS, vol. 12384, pp. 182–193. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59016-1_16
Mao, Y., Zhang, J., Song, S., Letaief, K.: Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems. IEEE Trans. Wirel. Commun. 16(9), 5994–6009 (2017)
Wu, Y., He, Y., Qian, L., Huang, J., Shen, X.: Optimal resource allocations for mobile data offloading via dual-connectivity. IEEE Trans. Mob. Comput. 17(10), 2349–2365 (2018)
Jiang, F., Wang, K., Dong, L., Pan, C., Yang, K.: Stacked autoencoder-based deep reinforcement learning for online resource scheduling in large-scale MEC networks. IEEE IoT J. 7(10), 9278–9290 (2020)
Zhang, H., Jiang, H., Li, B., Liu, F., Vasilakos, A., Liu, J.: A framework for truthful online auctions in cloud computing with heterogeneous user demands. IEEE Trans. Comput. 65(3), 805–818 (2016)
Zheng, B., Pan, L., Liu, S., Wang, L.: An online mechanism for purchasing Iaas instances and scheduling pleasingly parallel jobs in cloud computing environments. In: IEEE 39th International Conference on Distributed Computing Systems, ICDCS, Dallas, TX, USA, pp. 35–45 (2019)
Chen, Y., Zhang, N., Zhang, Y., Chen, X., Wu, W., Shen, X.: Energy efficient dynamic offloading in mobile edge computing for internet of things. IEEE Trans. Cloud Comput. (2019)
Zhang, D., et al.: Near-optimal and truthful online auction for computation offloading in green edge-computing systems. IEEE Trans. Mob. Comput. 19(4), 880–893 (2020)
Wang, X., et al.: Dynamic resource scheduling in mobile edge cloud with cloud radio access network. IEEE Trans. Parallel Distrib. Syst. 29(11), 2429–2445 (2018)
Zhang, F., Zhou, X., Sun, X.: Constrained VCG auction with multi-level channel valuations for spatial spectrum reuse in non-symmetric networks. IEEE Trans. Commun. 67(2), 1182–1196 (2019)
Michael, N.: Stochastic Network Optimization with Application to Communication and Queueing Systems. Stochastic Network Optimization with Application to Communication and Queueing Systems. Morgan & Claypool, San Rafael (2010)
Acknowledgments
This work is partly supported by the National Natural Science Foundation of China (Nos. 61872044, 61902029), the Excellent Talents Projects of Beijing (No. 9111923401) and Beijing High-level Innovative and Entrepreneurial Talents Project Famous Teacher Program.
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Wu, B., Chen, X., Jiao, L. (2021). Collaborative Computing Based on Truthful Online Auction Mechanism in Internet of Things. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-030-92638-0_9
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