Abstract
Recently, Vehicular Edge Computing (VEC) is evolving as a solution for offloading computationally intensive tasks in in-vehicle environments. However, when the number of vehicles and users is large, pure edge resources may be insufficient and limited, most existing work focuses on minimizing system latency by designing some offloading strategies. Therefore, hybrid multilayer edge structures are in dire require of mission deployment strategies that can synthesize cost and mission latency. In this paper, we argue that each application can be decomposed into multiple interdependent subtasks, and that the different subtasks can be deployed separately into different edge layers in a hybrid three-tier edge computing infrastructure for execution. We develop an improved DQN task deployment algorithm based on Lyapunov optimization to jointly optimize the average workflow latency and cost under a long-term cost constraint, and simulation results clearly show that, comparing with the traditional approach, our proposed method effectively reduces the cost consumption by 92.8% while sacrificing only some latency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kumar, S., Bhagat, L., Jin, J.: Multi-neural network based tiled 360\(^{\circ }\) video caching with mobile edge computing. J. Netw. Comput. Appl. 201, 103342 (2022). https://doi.org/10.1016/j.jnca.2022.103342
Hu, X., Wang, J., Zhong, C.: Statistical CSI based design for intelligent reflecting surface assisted MISO systems. Sci. China Inf. Sci. 63(12) (2020). https://doi.org/10.1007/s11432-020-3033-3
Lai, X., Fan, L., Lei, X., Deng, Y., Karagiannidis, G.K., Nallanathan, A.: Secure mobile edge computing networks in the presence of multiple eavesdroppers. IEEE Trans. Commun. 70(1), 500–513 (2022). https://doi.org/10.1109/TCOMM.2021.3119075
Na, Z., et al.: UAV-based wide-area internet of things: an integrated deployment architecture. IEEE Network 35(5), 122–128 (2021). https://doi.org/10.1109/MNET.001.2100128
Quan, W., Cheng, N., Qin, M., Zhang, H., Chan, H.A., Shen, X.: Adaptive transmission control for software defined vehicular networks. IEEE Wirel. Commun. Lett. 8(3), 653–656 (2019). https://doi.org/10.1109/LWC.2018.2879514
Lee, E., Lee, E.K., Gerla, M., Oh, S.Y.: Vehicular cloud networking: architecture and design principles. IEEE Commun. Mag. 52(2), 148–155 (2014). https://doi.org/10.1109/MCOM.2014.6736756
Li, T., Gao, C., Jiang, L., Pedrycz, W., Shen, J.: Publicly verifiable privacy-preserving aggregation and its application in IoT. J. Netw. Comput. Appl. 126, 39–44 (2019). https://doi.org/10.1016/j.jnca.2018.09.018
Liu, Y., et al.: Dependency-aware task scheduling in vehicular edge computing. IEEE Internet Things J. 7(6), 4961–4971 (2020). https://doi.org/10.1109/JIOT.2020.2972041
Lin, W., et al.: A hardware-aware CPU power measurement based on the power-exponent function model for cloud servers. Inf. Sci. 547, 1045–1065 (2021). https://doi.org/10.1016/j.ins.2020.09.033
Hu, L., Yan, H., Li, L., Pan, Z., Liu, X., Zhang, Z.: MHAT: an efficient model-heterogenous aggregation training scheme for federated learning. Inf. Sci. 560, 493–503 (2021). https://doi.org/10.1016/j.ins.2021.01.046
Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv. Tutorials 19(4), 2322–2358 (2017). https://doi.org/10.1109/COMST.2017.2745201
Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., Chen, S.: Vehicular fog computing: a viewpoint of vehicles as the infrastructures. IEEE Trans. Veh. Technol. 65(6), 3860–3873 (2016). https://doi.org/10.1109/TVT.2016.2532863
Zhou, Z., Liu, P., Chang, Z., Xu, C., Zhang, Y.: Energy-efficient workload offloading and power control in vehicular edge computing, pp. 191–196 (2018). https://doi.org/10.1109/WCNCW.2018.8368975
He, Q., et al.: A game-theoretical approach for mitigating edge DDoS attack. IEEE Trans. Dependable Secur. Comput. 19(4), 2333–2348 (2022). https://doi.org/10.1109/TDSC.2021.3055559
Caiazza, C., Giordano, S., Luconi, V., Vecchio, A.: Edge computing vs centralized cloud: impact of communication latency on the energy consumption of LTE terminal nodes. Comput. Commun. 194, 213–225 (2022). https://doi.org/10.1016/j.comcom.2022.07.026
Zhou, Y., et al.: A novel approach to applications deployment with multiple interdenpendent tasks in a hybrid three-layer vehicular computing environment, pp. 251–256 (2021). https://doi.org/10.1109/SMC52423.2021.9659035
Zhao, Z., Liu, S., Zhou, M., Guo, X., Xue, J.: Iterated greedy algorithm for solving a new single machine scheduling problem, pp. 430–435 (2019). https://doi.org/10.1109/ICNSC.2019.8743328
Shahidani, F., Ghasemi, A., Haghighat, A.: Task scheduling in edge-fog-cloud architecture: a multi-objective load balancing approach using reinforcement learning algorithm, pp. 1337–1359 (2023). https://doi.org/10.1007/s00607-022-01147-5
Zhang, K., Mao, Y., Leng, S., He, Y., Zhang, Y.: Mobile-edge computing for vehicular networks: a promising network paradigm with predictive off-loading. IEEE Veh. Technol. Mag. 12(2), 36–44 (2017). https://doi.org/10.1109/MVT.2017.2668838
Wu, Y., Gao, C.: Intelligent task offloading for vehicular edge computing with imperfect CSI: a deep reinforcement approach 55, 9 (2022). https://doi.org/10.1016/j.phycom.2022.101867
Zhang, L., Xia, J., Gao, C., Zhu, F., Fan, C., Ou, J.: DQN-based mobile edge computing for smart internet of vehicle, 45 (2022). https://doi.org/10.1186/s13634-022-00876-1
Acknowledgement
This work was supported in part by the Key Research and Development Project of Henan Province under Grant No. 231111211900, in part by the Henan Province Science and Technology Project under Grant No. 232102210024.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Tu, J. et al. (2024). DQN-Based Applications Offloading with Multiple Interdependent Tasks in Mobile Edge Computing. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 561. Springer, Cham. https://doi.org/10.1007/978-3-031-54521-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-54521-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-54520-7
Online ISBN: 978-3-031-54521-4
eBook Packages: Computer ScienceComputer Science (R0)