行動邊緣運算為移動用戶提供了強大的計算資源，以提升用戶的體驗。在本文中，我們考慮了一個在超高密度網路下多用戶多細的行動邊緣運算系統，部分卸載是一個有用的解決方案，將計算任務遷移到行動網路邊緣或雲端的伺服器，而不受限行動裝置的處理能力。我們提出了聯合卸載策略和資源分配的方法，通過將密集任務部分卸載到小型基地台以減少任務執行的延遲。由於配有運算能力的小型基地台計算資源有限，我們進一步利用小型基地台與大型基地台合作，以降低整體的延遲。我們推導出系統中最優的卸載率和最優的任務拆分的閉式解，如此一來，最小化延遲問題可簡化為資源分配的問題，並提出了一種基於有限內存BFGS的資源分配方法來解決我們的問題。最後，模擬結果顯示，在完全卸載和部分卸載的模型中，與沒有大型基地台的情況相比，有大型基地台對於減輕小型基地台的工作負載有很大的幫助，並且我們提出的方法能有效降低整體的延遲。

Mobile Edge Computing (MEC) provides powerful computing resources for mobile users to enhance the user experience. In this thesis, we consider a multi-user multi-cell MEC system in ultra-dense network (UDN). Partial offloading is a useful solution where computation tasks are migrated to the MEC server or cloud server in order to avoid undesired latency due to limited computation capabilities of mobile devices. We proposed a joint offloading strategy and resource allocation method to reduce the task execution latency by offloading the intensive task partially to the micro-cell base station (Micro-BS). Considering the limited computing resources at the Micro-BS equipped with MEC server, Micro-BS will collaborate with macro-cell base station (Macro-BS) for minimizing the overall latency in our work. We derive closed-form solutions of optimal offloading ratio and optimal task splitting strategy in our system. Then, the latency minimization problem can be reformulated as a piecewise non-convex optimization problem for resource allocation. A Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) based resource allocation method was proposed to solve our problem. Finally, the simulation results show that the Macro-BS will help Micro-BS to release the workload and our proposed method reduces the latency significantly compared to the case without Macro-BS in the full offloading and partial offloading models.

[1] H. T. Dinh, C. Lee, D. Niyato, and P. Wang, “A survey of mobile cloud computing: architecture, applications, and approaches,” Wireless communications and mobile computing, vol. 13, no. 18, pp. 1587–1611, 2013.
[2] U. Cisco, “Cisco annual internet report (2018–2023) white paper,” [Online (accessed March 26, 2021) https://www. cisco. com/c/en/us/solutions/collateral/ executive-perspectives/annual-internet report/whitepaper-c11-741490. html, 2020.
[3] L. Yang, J. Cao, H. Cheng, and Y. Ji, “Multi-user computation partitioning for latency sensitive mobile cloud applications,” IEEE Transactions on Computers, vol. 64, no. 8, pp. 2253–2266, 2014.
[4] M. Kamel, W. Hamouda, and A. Youssef, “Ultra-dense networks: A survey,” IEEE Communications Surveys & Tutorials, vol. 18, no. 4, pp. 2522–2545, 2016.
[5] H. C. B.-H. K. Changsheng You, Kaibin Huang, “Energy-efficient resource allocation for mobile-edge computation offloading,” IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1397–1411, 2017.
[6] J. Ren, G. Yu, Y. He, and G. Y. Li, “Collaborative cloud and edge computing for latency minimization,” IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 5031–5044, 2019.
[7] F. Fang, Y. Xu, Z. Ding, C. Shen, M. Peng, and G. K. Karagiannidis, “Optimal task partition and power allocation for mobile edge computing with noma,” in 2019 IEEE Global Communications Conference (GLOBECOM), 2019.
[8] S. Pan, Z. Zhang, Z. Zhang, and D. Zeng, “Dependency-aware computation offloading in mobile edge computing: A reinforcement learning approach,” IEEE Access, vol. 7, pp. 134 742–134 753, 2019.
[9] T.-Y. Kan, Y. Chiang, and H.-Y. Wei, “Task offloading and resource allocation in mobile-edge computing system,” in 2018 27th wireless and optical communication conference (WOCC). IEEE, 2018, pp. 1–4.
[10] U. Saleem, Y. Liu, S. Jangsher, X. Tao, and Y. Li, “Latency minimization for d2denabled partial computation offloading in mobile edge computing,” IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4472–4486, 2020.
[11] D. Zhang, J. Tang, W. Du, J. Ren, and G. Yu, “Joint optimization of computation offloading and ul/dl resource allocation in mec systems,” in 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2018, pp. 1–6.
[12] K. Cheng, Y. Teng, W. Sun, A. Liu, and X. Wang, “Energy-efficient joint offloading and wireless resource allocation strategy in multi-mec server systems,” CoRR, vol. abs/1803.07243, 2018. [Online]. Available: http://arxiv.org/abs/1803.07243
[13] Z. Ning, P. Dong, X. Kong, and F. Xia, “A cooperative partial computation offloading scheme for mobile edge computing enabled internet of things,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4804–4814, 2018.
[14] P. Wang, Z. Zheng, B. Di, and L. Song, “Hetmec: Latency-optimal task assignment and resource allocation for heterogeneous multi-layer mobile edge computing,” IEEE Transactions on Wireless Communications, vol. 18, no. 10, pp. 4942–4956, 2019.
[15] Y.-H. C. Jian-Jyun Hung, Wanjiun Liao, “Resource allocation for multi-access edge computing with coordinated multi-point reception,” 2020 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6, 2020.
[16] M. K. Awad and A. A. M. R. Behiry, “A quasi-newton-based approach to load balancing in coordinated multipoint (comp) green hetnets,” 2019 Seventh Internationl Conference on Digital Information Processing and Communication (ICDIPC), pp. 72–77, 2019.
[17] S. Hu and G. Li, “Dynamic request scheduling optimization in mobile edge computing for iot applications,” IEEE Internet of Things Journal, vol. 7, no. 2, pp. 1426–1437, 2019.
[18] T. X. Tran and D. Pompili, “Joint task offloading and resource allocation for multi-server mobile-edge computing networks,” IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 856–868, 2018.
[19] Y. Mao, J. Zhang, S. Song, and K. B. Letaief, “Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems,” IEEE Transactions on Wireless Communications, vol. 16, no. 9, pp. 5994–6009, 2017.
[20] M.-H. Chen, M. Dong, and B. Liang, “Joint offloading decision and resource allocation for mobile cloud with computing access point,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 3516–3520.
[21] X. Yang, G. You, C. Zhao, M. Dou, and X. Guo, “An improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy,” arXiv preprint arXiv:1901.00577, 2019.
[22] J. Xu, L. Chen, and S. Ren, “Online learning for offloading and autoscaling in energy harvesting mobile edge computing,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 3, pp. 361–373, 2017.
[23] Q. Fan and N. Ansari, “Workload allocation in hierarchical cloudlet networks,” IEEE Communications Letters, vol. 22, no. 4, pp. 820–823, 2018.
[24] F. Fang, Y. Xu, Z. Ding, C. Shen, M. Peng, and G. K. Karagiannidis, “Optimal resource allocation for delay minimization in noma-mec networks,” IEEE Transactions on Communications, vol. 68, no. 12, pp. 7867–7881, 2020.
[25] Z. Song, Y. Liu, and X. Sun, “Joint task offloading and resource allocation for noma-enabled multi-access mobile edge computing,” IEEE Transactions on Communications, vol. 69, no. 3, pp. 1548–1564, 2020.
[26] A. Mokhtari and A. Ribeiro, “A dual stochastic dfp algorithm for optimal resource allocation in wireless systems,” in 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2013, pp. 21–25.
[27] U. Cisco, “Ieee 802.11ax: The sixth generation of wi-fi white paper,” [Online] https://www.cisco.com/c/en/us/products/collateral/wireless/white-paperc11- 740788.html, 2020.
[28] J. Ren, G. Yu, Y. Cai, and Y. He, “Latency optimization for resource allocation in mobile-edge computation offloading,” IEEE Transactions on Wireless Communications, vol. 17, no. 8, pp. 5506–5519, 2018.
[29] M. M. Najafabadi, T. M. Khoshgoftaar, F. Villanustre, and J. Holt, “Large-scale distributed l-bfgs,” Journal of Big Data, vol. 4, no. 1, pp. 1–17, 2017.
[30] G. Yuan, Z. Wei, and S. Lu, “Limited memory bfgs method with backtracking for symmetric nonlinear equations,” Mathematical and Computer Modelling, vol. 54, no. 1-2, pp. 367–377, 2011.
[31] M. K. Awad and A. A. M. R. Behiry, “A quasi-newton-based approach to load balancing in coordinated multipoint (comp) green hetnets,” in 2019 Seventh International Conference on Digital Information Processing and Communications (ICDIPC), 2019, pp. 72–77.
[1] H. T. Dinh, C. Lee, D. Niyato, and P. Wang, “A survey of mobile cloud computing: architecture, applications, and approaches,” Wireless communications and mobile computing, vol. 13, no. 18, pp. 1587–1611, 2013.
[2] U. Cisco, “Cisco annual internet report (2018–2023) white paper,” [Online (accessed March 26, 2021) https://www. cisco. com/c/en/us/solutions/collateral/ executive-perspectives/annual-internet report/whitepaper-c11-741490. html, 2020.
[3] L. Yang, J. Cao, H. Cheng, and Y. Ji, “Multi-user computation partitioning for latency sensitive mobile cloud applications,” IEEE Transactions on Computers, vol. 64, no. 8, pp. 2253–2266, 2014.
[4] M. Kamel, W. Hamouda, and A. Youssef, “Ultra-dense networks: A survey,” IEEE Communications Surveys & Tutorials, vol. 18, no. 4, pp. 2522–2545, 2016.
[5] H. C. B.-H. K. Changsheng You, Kaibin Huang, “Energy-efficient resource allocation for mobile-edge computation offloading,” IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1397–1411, 2017.
[6] J. Ren, G. Yu, Y. He, and G. Y. Li, “Collaborative cloud and edge computing for latency minimization,” IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 5031–5044, 2019.
[7] F. Fang, Y. Xu, Z. Ding, C. Shen, M. Peng, and G. K. Karagiannidis, “Optimal task partition and power allocation for mobile edge computing with noma,” in 2019 IEEE Global Communications Conference (GLOBECOM), 2019.
[8] S. Pan, Z. Zhang, Z. Zhang, and D. Zeng, “Dependency-aware computation offloading in mobile edge computing: A reinforcement learning approach,” IEEE Access, vol. 7, pp. 134 742–134 753, 2019.
[9] T.-Y. Kan, Y. Chiang, and H.-Y. Wei, “Task offloading and resource allocation in mobile-edge computing system,” in 2018 27th wireless and optical communication conference (WOCC). IEEE, 2018, pp. 1–4.
[10] U. Saleem, Y. Liu, S. Jangsher, X. Tao, and Y. Li, “Latency minimization for d2denabled partial computation offloading in mobile edge computing,” IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4472–4486, 2020.
[11] D. Zhang, J. Tang, W. Du, J. Ren, and G. Yu, “Joint optimization of computation offloading and ul/dl resource allocation in mec systems,” in 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2018, pp. 1–6.
[12] K. Cheng, Y. Teng, W. Sun, A. Liu, and X. Wang, “Energy-efficient joint offloading and wireless resource allocation strategy in multi-mec server systems,” CoRR, vol. abs/1803.07243, 2018. [Online]. Available: http://arxiv.org/abs/1803.07243
[13] Z. Ning, P. Dong, X. Kong, and F. Xia, “A cooperative partial computation offloading scheme for mobile edge computing enabled internet of things,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4804–4814, 2018.
[14] P. Wang, Z. Zheng, B. Di, and L. Song, “Hetmec: Latency-optimal task assignment and resource allocation for heterogeneous multi-layer mobile edge computing,” IEEE Transactions on Wireless Communications, vol. 18, no. 10, pp. 4942–4956, 2019.
[15] Y.-H. C. Jian-Jyun Hung, Wanjiun Liao, “Resource allocation for multi-access edge computing with coordinated multi-point reception,” 2020 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6, 2020.
[16] M. K. Awad and A. A. M. R. Behiry, “A quasi-newton-based approach to load balancing in coordinated multipoint (comp) green hetnets,” 2019 Seventh Internationl Conference on Digital Information Processing and Communication (ICDIPC), pp. 72–77, 2019.
[17] S. Hu and G. Li, “Dynamic request scheduling optimization in mobile edge computing for iot applications,” IEEE Internet of Things Journal, vol. 7, no. 2, pp. 1426–1437, 2019.
[18] T. X. Tran and D. Pompili, “Joint task offloading and resource allocation for multi-server mobile-edge computing networks,” IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 856–868, 2018.
[19] Y. Mao, J. Zhang, S. Song, and K. B. Letaief, “Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems,” IEEE Transactions on Wireless Communications, vol. 16, no. 9, pp. 5994–6009, 2017.
[20] M.-H. Chen, M. Dong, and B. Liang, “Joint offloading decision and resource allocation for mobile cloud with computing access point,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 3516–3520.
[21] X. Yang, G. You, C. Zhao, M. Dou, and X. Guo, “An improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy,” arXiv preprint arXiv:1901.00577, 2019.
[22] J. Xu, L. Chen, and S. Ren, “Online learning for offloading and autoscaling in energy harvesting mobile edge computing,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 3, pp. 361–373, 2017.
[23] Q. Fan and N. Ansari, “Workload allocation in hierarchical cloudlet networks,” IEEE Communications Letters, vol. 22, no. 4, pp. 820–823, 2018.
[24] F. Fang, Y. Xu, Z. Ding, C. Shen, M. Peng, and G. K. Karagiannidis, “Optimal resource allocation for delay minimization in noma-mec networks,” IEEE Transactions on Communications, vol. 68, no. 12, pp. 7867–7881, 2020.
[25] Z. Song, Y. Liu, and X. Sun, “Joint task offloading and resource allocation for noma-enabled multi-access mobile edge computing,” IEEE Transactions on Communications, vol. 69, no. 3, pp. 1548–1564, 2020.
[26] A. Mokhtari and A. Ribeiro, “A dual stochastic dfp algorithm for optimal resource allocation in wireless systems,” in 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2013, pp. 21–25.
[27] U. Cisco, “Ieee 802.11ax: The sixth generation of wi-fi white paper,” [Online] https://www.cisco.com/c/en/us/products/collateral/wireless/white-paperc11- 740788.html, 2020.
[28] J. Ren, G. Yu, Y. Cai, and Y. He, “Latency optimization for resource allocation in mobile-edge computation offloading,” IEEE Transactions on Wireless Communications, vol. 17, no. 8, pp. 5506–5519, 2018.
[29] M. M. Najafabadi, T. M. Khoshgoftaar, F. Villanustre, and J. Holt, “Large-scale distributed l-bfgs,” Journal of Big Data, vol. 4, no. 1, pp. 1–17, 2017.
[30] G. Yuan, Z. Wei, and S. Lu, “Limited memory bfgs method with backtracking for symmetric nonlinear equations,” Mathematical and Computer Modelling, vol. 54, no. 1-2, pp. 367–377, 2011.
[31] M. K. Awad and A. A. M. R. Behiry, “A quasi-newton-based approach to load balancing in coordinated multipoint (comp) green hetnets,” in 2019 Seventh International Conference on Digital Information Processing and Communications (ICDIPC), 2019, pp. 72–77.