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Cost Optimization of Partial Computation Offloading and Pricing in Vehicular Networks

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Abstract

For vehicles with limited computation resources offloading their computational tasks to a mobile edge computing (MEC) server has been studied in the past as an effective means for improving their computational capabilities. However, most of these studies do not consider, in a meaningful way, the economic aspects related to both the computation offloading of the vehicles and the MEC service providers. In order to fill this gap, in this paper, a new cost based optimization methodology which jointly considers the cost of partial offloading vs. the pricing of the MEC server is proposed and its performance is analyzed. In particular, we first formally establish the cost model for vehicles and then, by setting a service price, the revenue model for MEC server. Secondly, optimal vehicle offloading strategies are identified and through a cost minimization partial computation offloading algorithm vehicles can configure, in an optimal way, the local CPU frequency and task partition based on the service price. Thirdly, by considering its computation resource limitations, the resource allocation and pricing mechanism for the MEC server is presented. It is shown that, through the development of an appropriate pricing algorithm, the MEC server can obtain the service price which maximizes its revenue while at the same time satisfying the server’s resource constraints. Numerical results have verified that the proposed scheme is indeed more cost effective as compared to local execution with dynamic voltage scaling (DVS) technique, full computation offloading and other partial computation offloading schemes. Furthermore, various performance evaluation results obtained by means of computer simulations have shown that the proposed pricing scheme achieves higher revenue as compared to other previously known fixed and random pricing schemes.

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References

  1. Wang, C, Li, Y, Jin, D, & Chen, S. (2016). On the serviceability of mobile vehicular cloudlets in a large-scale urban environment. IEEE Transactions on Intelligent Transportation Systems, 17(10), 2960–2970. https://doi.org/10.1109/TITS.2016.2561293.

    Article  Google Scholar 

  2. Mao, Y, You, C, Zhang, J, Huang, K, & Letaief, KB. (2017). A survey on mobile edge computing: the communication perspective. IEEE Communication Surveys & Tutorials, 19(4), 2322–2358. https://doi.org/10.1109/COMST.2017.2745201.

    Article  Google Scholar 

  3. He, Y, Chen, M, Ge, B, & Guizani, M. (2016). On wifi offloading in heterogeneous networks: Various incentives and trade-off strategies. IEEE Communication Surveys & Tutorials, 18(4), 2345–2385. https://doi.org/10.1109/COMST.2016.2558191.

    Article  Google Scholar 

  4. Mach, P, & Becvar, Z. (2017). Mobile edge computing: a survey on architecture and computation offloading. IEEE Communication Surveys & Tutorials, 19(3), 1628–1656. https://doi.org/10.1109/COMST.2017.2682318.

    Article  Google Scholar 

  5. Yuan, Q, Zhou, H, Li, J, Liu, Z, Yang, F, & Shen, XS. (2018). Toward efficient content delivery for automated driving services: an edge computing solution. IEEE Network, 32(1), 80–86. https://doi.org/10.1109/MNET.2018.1700105.

    Article  Google Scholar 

  6. Ren, J, Guo, H, Xu, C, & Zhang, Y. (2017). Serving at the edge: a scalable iot architecture based on transparent computing. IEEE Network, 31(5), 96–105. https://doi.org/10.1109/MNET.2017.1700030.

    Article  Google Scholar 

  7. Dinh, TQ, Tang, J, La, QD, & Quek, TQS. (2017). Offloading in mobile edge computing: Task allocation and computational frequency scaling. IEEE Transactions on Communications, 65(8), 3571–3584. https://doi.org/10.1109/TCOMM.2017.2699660.

    Article  Google Scholar 

  8. Wang, Y, Sheng, M, Wang, X, Wang, L, Han, W, Zhang, Y, & Shi, Y . (2015). Energy-optimal partial computation offloading using dynamic voltage scaling. In Proceedings IEEE ICC Workshop, pp 2695–2700 https://doi.org/10.1109/ICCW.2015.7247586.

  9. Wang, Y, Sheng, M, Wang, X, Wang, L, & Li, J. (2016). Mobile-edge computing: Partial computation offloading using dynamic voltage scaling. IEEE Transaction on Wireless Communication, 64(10), 4268–4282. https://doi.org/10.1109/TCOMM.2016.2599530.

    Article  Google Scholar 

  10. Chen, X, Jiao, L, Li, W, & Fu, X. (2016). Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Transaction Network, 24(5), 2795–2808. https://doi.org/10.1109/TNET.2015.2487344.

    Article  Google Scholar 

  11. Mao, Y, Zhang, J, & Letaief, KB. (2016). Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE Journal on Selected Areas in Communication, 34(12), 3590–3605. https://doi.org/10.1109/JSAC.2016.2611964.

    Article  Google Scholar 

  12. Kim, Y, Kwak, J, & Chong, S. (2018). Dual-side optimization for cost-delay tradeoff in mobile edge computing. IEEE Transactions on Vehicular Technology, 67(2), 1765–1781. https://doi.org/10.1109/TVT.2017.2762423.

    Article  Google Scholar 

  13. Liu, M, & Liu, Y. (2018). Price-based distributed offloading for mobile-edge computing with computation capacity constraints. IEEE Wireless Communication Letters, 7(3), 420–423. https://doi.org/10.1109/LWC.2017.2780128.

    Article  Google Scholar 

  14. Fang, W, Yao, X, Zhao, X, Yin, J, & Xiong, N. (2018). A stochastic control approach to maximize profit on service provisioning for mobile cloudlet platforms. IEEE Transactions on Systtem, Man, and Cybernetics A, System Humans, 48(4), 522–534. https://doi.org/10.1109/TSMC.2016.2606400.

    Article  Google Scholar 

  15. Kuang, Z, Li, L, Gao, J, Zhao, L, & Liu, A. (2019). Partial offloading scheduling and power allocation for mobile edge computing systems. IEEE Internet Things Journal, 6(4), 6774–6785. https://doi.org/10.1109/JIOT.2019.2911455.

    Article  Google Scholar 

  16. Cao, X, Wang, F, Xu, J, Zhang, R, & Cui, S. (2019). Joint computation and communication cooperation for energy-efficient mobile edge computing. IEEE Internet Things Journal, 6(3), 4188–4200. https://doi.org/10.1109/JIOT.2018.2875246.

    Article  Google Scholar 

  17. Zheng, K, Liu, F, Zheng, Q, Xiang, W, & Wang, W. (2013). A graph-based cooperative scheduling scheme for vehicular networks. IEEE Transactions on Vehicular Technology, 62(4), 1450–1458. https://doi.org/10.1109/TVT.2013.2244929.

    Article  Google Scholar 

  18. Zhang, K, Mao, Y, Leng, S, He, Y, & ZHANG, Y. (2017). Mobile-edge computing for vehicular networks: a promising network paradigm with predictive off-loading. IEEE Vehicular Technology Magazine, 12 (2), 36–44. https://doi.org/10.1109/MVT.2017.2668838.

    Article  Google Scholar 

  19. Qi, Q, Wang, J, Ma, Z, Sun, H, Cao, Y, Zhang, L, & Liao, J. (2019). Knowledge-driven service offloading decision for vehicular edge computing: a deep reinforcement learning approach. IEEE Transactions on Vehicular Technology, 68(5), 4192–4203. https://doi.org/10.1109/TVT.2019.2894437.

    Article  Google Scholar 

  20. Yu, R, Ding, J, Huang, X, Zhou, M, Gjessing, S, & Zhang, Y. (2016). Optimal resource sharing in 5G-Enabled vehicular networks: A matrix game approach. IEEE Transactions on Vehicular Technology, 65(10), 7844–7856. https://doi.org/10.1109/TVT.2016.2536441.

    Article  Google Scholar 

  21. He, Y, Zhao, N, & Yin, H. (2018). Integrated networking, caching, and computing for connected vehicles: a deep reinforcement learning approach. IEEE Transactions on Vehicular Technology, 67(1), 44–55. https://doi.org/10.1109/TVT.2017.2760281.

    Article  Google Scholar 

  22. Du, J, Yu, FR, Chu, X, Feng, J, & Lu, G. (2019). Computation offloading and resource allocation in vehicular networks based on dual-side cost minimization. IEEE Vehicular Technology Magazine, 68(2), 1079–1092. https://doi.org/10.1109/TVT.2018.2883156.

    Article  Google Scholar 

  23. Zhang, K, Mao, Y, Leng, S, Maharjan, S, & Zhang, Y. (2017). Optimal delay constrained offloading for vehicular edge computing networks. In Proceedings IEEE ICC, pp 1–6, DOI https://doi.org/10.1109/ICC.2017.7997360, (to appear in print).

  24. Belanovic, P, Valerio, D, Paier, A, Zemen, T, Ricciato, F, & Mecklenbrauker, CF. (2010). On wireless links for vehicle-to-infrastructure communications. IEEE Transactions on Vehicular Technology, 59(1), 269–282. https://doi.org/10.1109/TVT.2009.2029119.

    Article  Google Scholar 

  25. Yu, S, Langar, R, Fu, X, Wang, L, & Han, Z. (2018). Computation offloading with data caching enhancement for mobile edge computing. IEEE Transactions on Vehicular Technology, 67(11), 11098–11112. https://doi.org/10.1109/TVT.2018.2869144.

    Article  Google Scholar 

  26. Nee, RV, & House, A. (2000). OFDM for Wireless Multimedia Communications.

  27. Muquet, B, Wang, Zhengdao, Giannakis, GB, de Courville, M, & Duhamel, P. (2002). Cyclic prefixing or zero padding for wireless multicarrier transmissions?. IEEE Transactions on Communications, 50 (12), 2136–2148. https://doi.org/10.1109/TCOMM.2002.806518.

    Article  Google Scholar 

  28. Mostofi, Y, & Cox, DC. (2005). Ici mitigation for pilot-aided ofdm mobile systems. IEEE Transactions Wireless Communications, 4(2), 765–774. 10.1109/TWC.2004.840235.

    Article  Google Scholar 

  29. Morelli, M, & Mengali, U. (1999). An improved frequency offset estimator for ofdm applications. IEEE Communications Letters, 3(3), 75–77. https://doi.org/10.1109/4234.752907.

    Article  Google Scholar 

  30. Du, J, Liu, X, & Rao, L. (2018). Proactive doppler shift compensation in vehicular cyber-physical systems. IEEE/ACM Transactions Network, 26(2), 807–818. https://doi.org/10.1109/TNET.2018.2797107.

    Article  Google Scholar 

  31. Alieiev, R, Hehn, T, Kwoczek, A, & KIzrner, T. (2018). Predictive communication and its application to vehicular environments: Doppler-shift compensation. IEEE Transactions on Vehicular Technology, 67 (8), 7380–7393. https://doi.org/10.1109/TVT.2018.2835662.

    Article  Google Scholar 

  32. Chelli, K, & Herfet, T. (2016). Doppler shift compensation in vehicular communication systems. In 2016 2nd IEEE International Conference on Computer and Communications (ICCC), pp 2188–2192 https://doi.org/10.1109/CompComm.2016.7925088.

  33. Yue, J, Zhao, D, & Todd, TD. (2014). Cloud server job selection and scheduling in mobile computation offloading. In 2014 IEEE GLOBECOM., pp 4990–4995.

  34. Qu, G. (2001). What is the limit of energy saving by dynamic voltage scaling?. In IEEE/ACM International Conference on Computer Aided Design., pp 560–563, DOI https://doi.org/10.1109/ICCAD.2001.968707, (to appear in print).

  35. Zhang, W, Wen, Y, Guan, K, Kilper, D, Luo, H, & Wu, DO. (2013). Energy-optimal mobile cloud computing under stochastic wireless channel. IEEE Transactions Wireless Communications, 12 (9), 4569–4581. https://doi.org/10.1109/TWC.2013.072513.121842.

    Article  Google Scholar 

  36. Zhang, K, Leng, S, Peng, X, Pan, L, Maharjan, S, & Zhang, Y. (2019). Artificial intelligence inspired transmission scheduling in cognitive vehicular communications and networks. IEEE Internet Things Journal, 6(2), 1987–1997. https://doi.org/10.1109/JIOT.2018.2872013.

    Article  Google Scholar 

  37. Chen, M, & Hao, Y. (2018). Task offloading for mobile edge computing in software defined ultra-dense network. IEEE Journal Selected Areas on Communications, 36(3), 587–597. https://doi.org/10.1109/JSAC.2018.2815360.

    Article  Google Scholar 

  38. Ko, S, Han, K, & Huang, K. (2018). Wireless networks for mobile edge computing: Spatial modeling and latency analysis. IEEE Transactions on Wireless Communications, 17(8), 5225–5240. https://doi.org/10.1109/TWC.2018.2840120.

    Article  Google Scholar 

  39. Lange, S, Gebert, S, Zinner, T, Tran-Gia, P, Hock, D, Jarschel, M, & Hoffmann, M. (2015). Heuristic approaches to the controller placement problem in large scale sdn networks. IEEE Transactions on Network and Service Management, 12(1), 4–17. https://doi.org/10.1109/TNSM.2015.2402432.

    Article  Google Scholar 

  40. Miettinen, AP, & Nurminen JK. (2010). Energy efficiency of mobile clients in cloud computing. In Proceedings USENIX HotCloud.

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Acknowledgements

The financial support of the National Natural Science Foundation of China (NSFC) (Grant No. 61671072) and the Beijing Natural Science Foundation (No. L192025) is gratefully acknowledged. We also would like to thank both reviewers who provided us with comments which helped us to significantly improve the presentation of our paper.

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Correspondence to Tiejun Lv or P. Takis Mathiopoulos.

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Li, L., Lv, T., Huang, P. et al. Cost Optimization of Partial Computation Offloading and Pricing in Vehicular Networks. J Sign Process Syst 92, 1421–1435 (2020). https://doi.org/10.1007/s11265-020-01572-9

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