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DDPS: Dynamic Differential Pricing-based Edge Offloading System with Energy Harvesting Devices (2403.09991v1)

Published 15 Mar 2024 in cs.GT

Abstract: Mobile edge computing (MEC) paves the way to alleviate the burden of energy and computation of mobile users (MUs) by offloading tasks to the network edge. To enhance the MEC server utilization by optimizing its resource allocation, a well-designed pricing strategy is indispensable. In this paper, we consider the edge offloading scenario with energy harvesting devices, and propose a dynamic differential pricing system (DDPS), which determines the price per unit time according to the usage of computing resources to improve the edge server utilization. Firstly, we propose an offloading decision algorithm to decide whether to conduct the offloading operation and how much data to be offloaded if conducted, the algorithm determines offloading operation by balancing the energy harvested with the energy consumed. Secondly, for the offloading case, we formulate the game between the MUs and the server as a Stackelberg game, and propose a differential pricing algorithm to determine the optimal computing resources required by MUs. Furthermore, the proposed algorithm also reallocates computing resources for delay-sensitive devices while server resources are surplus after the initial allocation, aiming to make full use of the server computing resources. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme.

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References (37)
  1. H. Seo, H. Oh, J. K. Choi, and S. Park, “Differential Pricing-Based Task Offloading for Delay-Sensitive IoT Applications in Mobile Edge Computing System,” IEEE Internet Things J., vol. 9, no. 19, pp. 19116-19131, Oct. 2022.
  2. Z. Chang, L. Liu, X. Guo, and Q. Sheng, “Dynamic Resource Allocation and Computation Offloading for IoT Fog Computing System,” IEEE Trans. Ind. Inf., vol. 17, no. 5, pp. 3348-3357, May 2021.
  3. Y. Chen, Z. Chang, G. Min, S. Mao, and T. Hämäläinen, “Joint Optimization of Sensing and Computation for Status Update in Mobile Edge Computing Systems,” IEEE Trans. Wireless Commun., vol. 22, no. 11, pp. 8230-8243, Nov. 2023,
  4. X. Chen, W. Li, S. Lu, Z. Zhou, and X. Fu, “Efficient resource allocation for on-demand mobile-edge cloud computing,” IEEE Trans. Veh. Technol., vol. 67, no. 9, pp. 8769–8780, Sep. 2018.
  5. J. Yan, S. Bi, L. Duan, and Y.-J. A. Zhang, “Pricing-driven service caching and task offloading in mobile edge computing,” IEEE Trans. Wireless Commun., vol. 20, no. 7, pp. 4495–4512, Jul. 2021.
  6. C. Yi, J. Cai, and Z. Su, “A multi-user mobile computation offloading and transmission scheduling mechanism for delay-sensitive applications,” IEEE Trans. Mobile Comput., vol. 19, no. 1, pp. 29–43, Jan. 2020.
  7. M. Tao, K. Ota, M. Dong, and H. Yuan, “Stackelberg Game-Based Pricing and Offloading in Mobile Edge Computing,” IEEE Wireless Commun. Lett., vol. 11, no. 5, pp. 883-887, May 2022.
  8. Y. Chen, Z. Li, B. Yang, K. Nai, and K. Li, “A Stackelberg game approach to multiple resources allocation and pricing in mobile edge computing,” Future Gener Comput Syst, vol. 108, pp. 273-287, 2020.
  9. Y. Li, L. Li, Y. Xia, D. Zhang, and Y. Wang, “Multi-Leader Single-Follower Stackelberg Game Task Offloading and Resource Allocation Based on Selection Optimization in Internet of Vehicles,” IEEE Access, vol. 11, pp. 64430-64441, 2023.
  10. W. Qin, C. Zhang, H. Yao, T. Mai, S. Huang, D. Guo, and R. Gao, “Stackelberg Game-Based Offloading Strategy for Digital Twin in Internet of Vehicles,” in Proc. Int. Wirel. Commun. Mob. Comput. (IWCMC), 2023, pp. 1365-1370.
  11. M. Wang, L. Zhang, P. Gao, X. Yang, K. Wang, and K. Yang, “Stackelberg-Game-Based Intelligent Offloading Incentive Mechanism for a Multi-UAV-Assisted Mobile-Edge Computing System,” IEEE Internet Things J., vol. 10, no. 17, pp. 15679-15689, Sept. 2023.
  12. G. Mitsis, E. E. Tsiropoulou, and S. Papavassiliou, “Price and Risk Awareness for Data Offloading Decision-Making in Edge Computing Systems,” IEEE Syst. J., vol. 16, no. 4, pp. 6546-6557, Dec. 2022.
  13. Q. Li, H. Yao, T. Mai, C. Jiang, and Y. Zhang, “Reinforcement-Learning- and Belief-Learning-Based Double Auction Mechanism for Edge Computing Resource Allocation,” IEEE Internet Things J., vol. 7, no. 7, pp. 5976-5985, Jul. 2020.
  14. T. H. Hai and P. Nguyen, “A Pricing Model for Sharing Cloudlets in Mobile Cloud Computing,” in Proc. Int. Conf. Adv. Comput. Appl. (ACOMP), 2017, pp. 149-153.
  15. Z. Shen, J. Zhang, and H. Tan, “A Truthful FPTAS Auction for the Edge-Cloud Pricing Problem,” in Proc. 6th Int. Conf. Big Data Comput. Commun. (BIGCOM), China, 2020, pp. 140-144.
  16. J. S. Ng, W. Yang Bryan Lim, S. Garg, Z. Xiong, D. Niyato, M. Guizani, and C. Leung, “Collaborative Coded Computation Offloading: An All-pay Auction Approach,” in Proc. IEEE Int Conf Commun, 2021, pp. 1-6.
  17. Q. Wang, S. Guo, J. Liu, C. Pan, and L. Yang, “Profit Maximization Incentive Mechanism for Resource Providers in Mobile Edge Computing,” IEEE Trans. Serv. Comput., vol. 15, no. 1, pp. 138-149, Jan.-Feb. 2022.
  18. W. Sun, J. Liu, Y. Yue, and P. Wang, “Joint Resource Allocation and Incentive Design for Blockchain-Based Mobile Edge Computing,” IEEE Trans. Wireless Commun., vol. 19, no. 9, pp. 6050-6064, Sept. 2020.
  19. B. Wu, X. Chen, Y. Chen, and Y. Lu, “A Truthful Auction Mechanism for Resource Allocation in Mobile Edge Computing,” in Proc. IEEE 22nd Int. Symp. World Wirel., Mob. Multimed. Networks (WoWMoM), 2021, pp. 21-30.
  20. L. Ma, X. Wang, X. Wang, L. Wang, Y. Shi, and M. Huang, “TCDA: Truthful Combinatorial Double Auctions for Mobile Edge Computing in Industrial Internet of Things,” IEEE Trans Mob Comput, vol. 21, no. 11, pp. 4125-4138, Nov. 2022.
  21. R. Wang, C. Zang, P. He, Y. Cui, and D. Wu, “Auction Pricing-Based Task Offloading Strategy for Cooperative Edge Computing,” in Proc. IEEE Glob. Commun. Conf. (GLOBECOM), 2021, pp. 01-06.
  22. Y. Su, W. Fan, Y. Liu, and F. Wu, “A Truthful Combinatorial Auction Mechanism Towards Mobile Edge Computing in Industrial Internet of Things,” IEEE Trans. on Cloud Comput., vol. 11, no. 2, pp. 1678-1691, Apr.-Jun. 2023.
  23. D. Han, W. Chen, and Y. Fang, “A Dynamic Pricing Strategy for Vehicle Assisted Mobile Edge Computing Systems,” IEEE Wireless Commun. Lett., vol. 8, no. 2, pp. 420-423, Apr. 2019.
  24. Z. Chang, C. Wang, and H. Wei, “Flat-Rate Pricing and Truthful Offloading Mechanism in Multi-Layer Edge Computing,” IEEE Trans. Wirel. Commun., vol. 20, no. 9, pp. 6107-6121, Sept. 2021.
  25. M. Liu and Y. Liu, “Price-Based Distributed Offloading for Mobile-Edge Computing With Computation Capacity Constraints,” IEEE Wireless Commun. Lett., vol. 7, no. 3, pp. 420-423, Jun. 2018.
  26. B. Liang, R. Fan, H. Hu, Y. Zhang, N. Zhang, and A. Anpalagan, “Nonlinear Pricing Based Distributed Offloading in Multi-User Mobile Edge Computing,” IEEE Trans. Veh. Technol., vol. 70, no. 1, pp. 1077-1082, Jan. 2021.
  27. S. Kim, S. Park, M. Chen, and C. Youn, “An Optimal Pricing Scheme for the Energy-Efficient Mobile Edge Computation Offloading With OFDMA,” IEEE Commun. Lett., vol. 22, no. 9, pp. 1922-1925, Sept. 2018.
  28. L. Li, M. Siew, Z. Chen, and T. Q. S. Quek, “Optimal Pricing for Job Offloading in the MEC System With Two Priority Classes,” IEEE Trans. Veh. Technol., vol. 70, no. 8, pp. 8080-8091, Aug. 2021.
  29. Q. Yao and L. Tang, “An Approximation Algorithm for Pricing and Request Matching in Mobile ad hoc MEC System,” in Proc. Comput., Commun. and IoT Applications (ComComAp), 2019, pp. 432-437.
  30. R. Wang, C. Zang, P. He, Y. Cui, and D. Wu, “Auction Pricing-Based Task Offloading Strategy for Cooperative Edge Computing,” in Proc. IEEE Glob. Commun. Conf. (GLOBECOM), Dec. 2021, pp. 01-06.
  31. Y. Wang, M. Sheng, X. Wang, L. Wang, and J. Li, “Mobile-edge computing: Partial computation offloading using dynamic voltage scaling,” IEEE Trans. Commun., vol. 64, no. 10, pp. 4268–4282, Oct. 2016.
  32. L. Dong, S. Han, Y. Gao, and Z. Tan, “A Game-Theoretical Approach for Resource Allocation in Mobile Edge Computing,” in Proc. IEEE 20th Int. Conf. Commun. Technol. (ICCT), 2020, pp. 436-440.
  33. W. Zhang, G. Zhang, and S. Mao, “Joint Parallel Offloading and Load Balancing for Cooperative-MEC Systems With Delay Constraints,” IEEE Trans. Veh. Technol., vol. 71, no. 4, pp. 4249-4263, Apr. 2022.
  34. J. Chen, Z. Chang, X. Guo, R. Li, Z. Han, and T. Hämäläinen, “Resource Allocation and Computation Offloading for Multi-Access Edge Computing With Fronthaul and Backhaul Constraints,” IEEE Trans. Veh. Technol., vol. 70, no. 8, pp. 8037-8049, Aug. 2021
  35. M. Guo, W. Wang, X. Huang, Y. Chen, L. Zhang, and L. Chen, “Lyapunov Based Partial Computation Offloading for Multiple Mobile Devices Enabled by Harvested Energy in MEC,” IEEE Internet Things J., vol. 9, no. 11, pp. 9025-9035, Jun. 2022.
  36. M. DeVirgilio, W. D. Pan, L. L. Joiner, and D. Wu, “Internet delay statistics: Measuring Internet feel using a dichotomous Hurst parameter,” in Proc. IEEE SoutheastCon, 2012, pp. 1-6.
  37. N. Powers, A. Alling, K. Osolinsky, T. Soyata, M. Zhu, H. Wang, H. Ba, W. Heinzelman, J. Shi, and M. Kwon, “The Cloudlet Accelerator: Bringing Mobile-Cloud Face Recognition into Real-Time,” in Proc. IEEE Globecom Workshops (GC Wkshps), 2015, pp. 1-7.
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Authors (5)
  1. Hai Xue (3 papers)
  2. Yun Xia (4 papers)
  3. Neal N. Xiong (16 papers)
  4. Di Zhang (232 papers)
  5. Songwen Pei (5 papers)
Citations (1)
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