跳到主要內容

臺灣博碩士論文加值系統

(44.200.140.218) 您好!臺灣時間:2024/07/19 02:52
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:林迺航
研究生(外文):Lin, Nai-Hang
論文名稱:多MEC環境下進行差異化任務卸載之研究
論文名稱(外文):Task Offloading for Differentiated Tasks in aMulti-MEC Environment
指導教授:楊竹星楊竹星引用關係謝錫堃謝錫堃引用關係
指導教授(外文):Yang, Chu-SingShieh, Ce-Kuen
口試委員:楊竹星謝錫堃蘇淑茵許靜芳蔡邦維
口試委員(外文):Yang, Chu-SingShieh, Ce-KuenSou, Sok-IanHsu, Ching-FangTsai, Pang-Wei
口試日期:2023-07-17
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:中文
論文頁數:54
中文關鍵詞:第五代行動通訊多接取邊緣計算計算卸載異質性任務
外文關鍵詞:Fifth generation communicationMulti-access Edge Computingcomputation offloadingheterogeneous tasks
相關次數:
  • 被引用被引用:0
  • 點閱點閱:85
  • 評分評分:
  • 下載下載:16
  • 收藏至我的研究室書目清單書目收藏:0
多接取邊緣運算(Multi-access Edge Computing, MEC)是一種網路服務架構模型,將運算資源靠近使用者和終端設備,提供使用者強大的運算應用服務支援。隨著網路發展,使用者設備急遽增加,其應用如虛擬實境、智慧城市、自駕車等,對於頻寬與延遲有更高的要求。過往需要將這些任務透過骨幹網路傳送至雲端伺服器,當使用者設備急遽增加時,導致骨幹網路壅塞高延遲問題。透過多接取邊緣運算,將運算任務卸載接近使用者端,不必卸載至雲端伺服器,減少高延遲與網路壅塞問題。目前研究多數著重於單一類型的任務需求服務下進行資源分配的研究。在真實的場景中,使用者端之任務往往具有不同的需求。當處理不同任務類型時採用單一類型的處理方式,將會降低MEC的效率。
本論文研究考慮多MEC以及多使用者下的異質性服務任務需求,進行運算任務卸載與資源分配最佳化並建立數學模型,運用模擬退火演算法來解決系統花費最小化之問題。最後實驗結果顯示綜合考量延遲、能耗以及封包錯誤率三種因素對於增強型行動寬頻(Enhanced Mobile Broadband, eMBB)、超可靠低延遲通訊(Ultra Reliable and Low Latency Communications, URLLC)、大規模機器型通訊(Massive Machine Type Communication, mMTC)三類任務應用其整體花費、卸載成功率、公平性與其他卸載策略方法有更好的表現。
Multi-access Edge Computing (MEC) is a network service architecture model that brings computation resources closer to users and terminal devices, providing users with powerful computational application services. With the development of networks, the number of user devices has increased rapidly, and applications such as virtual reality, smart cities, and self-driving cars have higher requirements for bandwidth and latency. In the past, these tasks needed to be transmitted to cloud servers through the backbone network, which led to high latency and congestion in the backbone network when the number of user devices increased rapidly. By offloading computation tasks to the edge of the network through multi-access edge computing, there is no need to offload them to cloud servers, reducing high latency and network congestion issues. Currently, most research focuses on resource allocation for a single type of task request service. In real-world scenarios, user-end tasks often have different requirements. Adopting a single type of processing for different task types would diminish the advantages of MEC.
This paper investigates the optimization of computation task offloading and resource allocation considering heterogeneous service task requests in a multi-access edge computing(MEC) environment with multiple users. A mathematical model is established, and a simulated annealing algorithm is employed to minimize system costs. The experimental results demonstrate that by considering the factors of latency, energy consumption, and packet error rate, the proposed approach outperforms other offloading strategies in terms of overall cost, offloading success rate and fairness under the three types of task applications: Enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency Communications (URLLC), and Massive Machine Type Communication (mMTC).
中文摘要 I
Abstract II
目錄 XVI
表目錄 XVIII
圖目錄 XIX
第一章 緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究貢獻 3
1-4 論文架構 4
第二章 背景知識與相關研究 5
2-1 三大典型5G應用 5
2-1.1 eMBB應用的特點和需求 6
2-1.2 mMTC應用的特點和需求 7
2-1.3 URLLC應用的特點和需求 8
2-2 多邊緣接取運算(Multi-Access Edge Computing, MEC) 8
2-3 公平性指標(Jain's Fairness Index) 10
2-4 相關計算任務卸載論文 11
2-4.1 卸載環境 11
2-4.2 卸載行為 12
2-4.3 卸載目標 13
2-4.4 卸載方法 14
2-5 專有名詞比對表 17
第三章 系統模型和問題建立求解 18
3-1 系統模型 18
3-2 問題建立 28
3-3 問題求解 29
第四章 實驗設計與結果分析 33
4-1 實驗配置 33
4-2 效能分析 37
4-2.1 執行時間 37
4-2.2 系統花費評估 38
4-2.3 卸載任務成功率評估 40
4-2.4 公平性評估 43
第五章 結論與未來展望 48
參考文獻 49
[1] S. Kekki, W. Featherstone, Y. Fang, P. Kuure, A. Li, A. Ranjan, D. Purkayastha,F. Jiangping, D.Frydman, G.Verin et al., "MEC in 5G networks," ETSI white paper, vol.28, pp. 1-28, 2018.
[2] F. Spinelli and V. Mancuso, "Toward enabled industrial verticals in 5G: a survey on MEC-based approaches to provisioning and flexibility," IEEE Communications Surveys and Tutorials, vol. 23, no. 1, pp. 596-630, 2021
[3] R. Goyal, N. Mahajan, T. Goyal, S. Kaushal, N. Gupta, and H. Kumar, "Exploration of 5G technology for cellular communication: a survey," pp. 330-334, 2018.
[4] Q. K. Ud Din Arshad, A. U. Kashif, and I. M. Quershi, "A review on the evolution of cellular technologies," International Bhurban Conference on Applied Sciences and Technology, pp. 989-993, 2019.
[5] B. S. Khan, S. Jangsher, A. Ahmed, and A. Al-Dweik, "URLLC and eMBB in 5G Industrial IoT: A Survey," IEEE Open Journal of the Communications Society, vol. 3,pp. 1134-1163, 2022.
[6] S. R. Pokhrel, J. Ding, J. Park, O. S. Park, and J. Choi,"Towards enabling critical mMTC: A review of URLLC within mMTC," IEEE Access, vol. 8, pp. 131796-131813, 2020.
[7] D. Feng, L. Lai, J. Luo, Y. Zhong, C. Zheng, and K. Ying, "Ultra-reliable and low-latency communications: applications, opportunities and challenges," Science China Infomation Sciences, vol. 64, pp. 1-12,2021
[8] M. Iwabuchi, A. Benjebbour, Y. Kishiyama, G. Ren, C. Tang, T. Tian, L. Gu, T. Takada, and T. Kashima, "5G field experimental trials on URLLC using new frame structure," IEEE Globecom Workshops, pp. 1-6, 2017.
[9] F. Vhora and J. Gandhi, "A comprehensive survey on mobile edge computing: challenges, tools, applications," pp. 49-55, 2020.
[10] D.M. Chiu,"A quantitative measure of fairness and discrimination for resource allocation in shared computer systems," Digital Equipment Corporation, Tech. REP., 1984.
[11] A. Hussain and P. Musilek, "Fairness and utilitarianism in allocating energy to EVs during power contingencies using modified division rules," IEEE Transactions on Sustainable Energy, vol. 13, no. 3, pp. 1444-1456, 2022.
[12] B. K. S. Lima, D. B. da Costa, R. Oliveira, R. Dinis, M. Beko, and U. S. Dias, "Power allocation, relay selection, and user pairing for cooperative NOMA systems with rate fairness" IEEE Vehicular Technology Conference, pp. 1-5, 2021.
[13] Y. L. Lee, J. Loo, and T. C. Chuah, "Modeling and performance evaluation of resource allocation for LTE femtocell networks," Modeling and Simulation of Computer Networks and Systems, pp. 683-716, 2015.
[14] T. Q. Dinh, J. Tang, Q. D. La, and T. Q. S. Quek, "Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling," IEEE Transactions on Communications, vol. 65, no. 8, pp. 3571-3584, 2017.
[15] P. Mach and Z. Becvar, "Mobile Edge Computing: A Survey on Architecture and Computation Offloading" IEEE Communications Surveys & Tutorials, VOL. 19, no. 3, pp. 1628-1656, 2017.
[16] C. H. Duo, P. Dong, Q. Gao, B. Li, "MEC computation offloading-based learning strategy in Ultra-dense networks," Information, vol. 13, p. 271, 2022.
[17] S. Fu, C. Ding, and P. Jiang, "Computational offlaoding of service workflow in mobile edge computing," Information, vol. 13, no. 7, p. 348, 2022.
[18] S. Liu, Y. Yu, X. Lian, Y. Feng, C.She, P. L. Yeoh, L. Guo, B. Vucetic, and Y. Li, "Dependent Task Scheduling and Offloading for Minimizing Deadline Violation Ratio in Mobile Edge Computing Networks," IEEE Journal on Selected Areas in Communications vol. 41, no. 2, pp. 538-554, 2023.
[19] Y.k. Tun, T.N. Dang, K. Kim, M. Alsenwi, W. Saad, and C. S. Hong, "Collaboration in the Sky: A Distributed Framework for Task Offloading and Resource Allocation in Multi-Access Edge Computing," IEEE Internet of Things Journal, vol. 9, pp. 24221-24235, 2021.
[20] X. Deng, J. Yin, P. Guan, N. N. Xiong, L. zHANG, and S. Mumtaz, "Intelligent Delay-Aware Partial Computing Task Offloading for Multiuser Industrial Internet of Things Through Edge Computing," IEEE Internet of Things Journal, vol. 10, no. 4, pp. 2954-2966, 2023.
[21] 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, pp. 4804-4814, 2019.
[22] K. Xiong, G. Huang, J. Liu, and M. Zhang, "Load-Aware Computation Offloading with Latency Limitation in Mobile Edge Computing," International Conference on Neural Networks, Information and Communication Engineering, pp. 694-701, 2023.
[23] Q. Gan, G. Li, W. He, Y. Zhao, Y. Song, and C. Xu, "Delay-minimization offloading scheme in multi-server MEC networks," IEEE Wireless Communications Letters, vol. 12, no. 6, pp. 1071-1075, 2023.
[24] F. Wei, S. Chen, and W. Zou, "A greedy algorithm for task offloading in mobile edge computing system," China Communications, vol. 15, no. 11, pp. 149-157, 2018.
[25] Z. Wang, H. Du, and Q. Ye, "HTR: A joint approach for task offloading and resource allocation in mobile edge computing," IEEE International Conference on Communications, pp. 1-6, 2021.
[26] I Alqerm and J. Pan, "DeepEdge: A New QoE-based resource allocation framework using deep reinforcement learning for future heterogeneous edge-IoT applications," IEEE Transactions on Network and Service Management, vol. 18, pp. 3942-3954, 2021.
[27] P. Zhao, H. Tian, C. Qin, and G. Nie, "Energy-Saving Offloading by Jointly Allocating Radio and Computational Resources for Mobile Edge Computing," IEEE Access, vol. 5, pp. 11255-11268, 2017.
[28] X. Yang, X. Yu, H. Huang, and H. Zhu, "Energy-efficient discovery process for mMTC applications," IFIP Wireless and Mobile Networking Conference, vol. 7, pp. 117054-117063, 2019.
[29] K. Guo, R. Gao, W. Xia, and T. Q. S. Quek, "Online Learning Based Computation Offloading in MEC Systems With Communication and Computation Dynamics," IEEE Transactions on Communications, vol. 69, pp. 1146-1162, 2021.
[30] N. Keshari, T. S. Gupta, and D. Singh, "Particle Swarm Optimization based Task Offloading in Vehicular Edge Computing," IEEE India Council International Conference, pp.1-8, 2021.
[31] G. Zhang, S. Zhang, W. Zhang, Z. Shen, and L. Wang, "Joint Service Caching, Computation Offloading and Resource Allocation in Mobile Edge Computing Systems," IEEE Transactions on Wireless Communications, vol. 2o, pp. 5288-5300, 2021.
[32] K. Zheng, G. Jiang, X. Liu, K. Chi, X. Yao, and J. Liu, "DRL-based offloading for computation delay minimization in wireless-powered Multi-access edge computing," IEEE Transactions on Communications, vol. 71, no. 3, pp. 1755-1770, 2023.
[33] X. Chu, D. Lopez-Perez, Y. Yang, and F. Gunnarsson, "Heterogeneous cellular networks: theory, simulation and deployment," Cambridge University Press, pp.151-169, 2013.
[34] 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, 2019.
[35] W. Zhang, Y. Wen, K. Guan, D. Kilper, H. Luo, and D. O. Wu, "Energy-optimal mobile cloud computing under stochastic wireless channel," IEEE Transactions on Wireless Communications, vol. 12, no. 9, pp. 4569-4581, 2013.
[36] Pushpalatha, Prathyusha, Sindhu, M. J. Khan, I. Singh, and S. Tayal, "BER performance using BPSK Modulation over Rayleigh and Rician fading channel," IEEE International Conference on Communication Systems and Network Technologies, pp. 434-437, 2022.
[37] D. Bertsimas and J. Tsitsiklis, "Simulated annealing," Statistical science, vol. 8, no. 1, pp. 10-15, 1993.
[38] M. K. Sen, A. Datta-Gupta, P. Stoffa, L. Lake, and G. Pope, "Stochastic reservoir modeling using simulated annealing and genetic algorithms," SPE Formation Evaluation, vol. 10, no. 1, pp.49-55, 1995.
[39] Y. Li, "Optimization of task offloading problem based on simulated annealing algorithm in MEC," International Conference on Intelligent Computing and Wireless Optical Communications, pp. 47-52, 2021.
[40] C. Duo, P. Dong, Q. Gao, B. Li, and Y. Li, "MEC computation offloading-based learning strategy in Ultra-dense networks," Information, vol. 13, no. 6, p. 271, 2022.
[41] D.Jung, J. Kim, and J. M. Chung, "Energy minimized computation offloading with popularity-based cooperation in 5G mMTC networks," IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3238-3250, 2022.
[42] J. Yun, Y.Goh, W. Yoo, and J. M. Chung, "5G Multi-RAT URLLC and eMBB Dynamic task offloading with MEC resource allocation using distributed deep reinforcement learning," IEEE Internet of Things Journal, vol. 9, no. 20, pp. 20733-20749, 2022.
[43] C. V. Anamuro, N. Varsier, J. Schwoerer, and X. Lagrange, "Energy-efficient discovery process for mMTC applications," IFIP Wireless and Mobile Networking Conference, pp. 79-86, 2019.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top