相依性子任務於多邊緣運算伺服器卸載排程與資源分配之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：16

、訪客IP：18.119.122.125

姓名

王育琪(YU-CHI, WANG) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

相依性子任務於多邊緣運算伺服器卸載排程與資源分配之研究
(Study of Dependent Subtask Offloading Scheduling and Resource Allocation in Multi-access Edge Computing)

相關論文

★ 應用MSPP至DWDM都會光纖網路的設計	★ 光網路與WiMAX整合架構研究及其簡化雛型實驗
★ 以Linux系統為基礎之NAT效能優化研究及其實作	★ 光波長劃分多工網路之路徑保護機制研究
★ 標籤交換網路下具有服務品質路由安排之研究	★ 以訊務相關性為基礎的整合性服務可調整QoS排程器之研究
★ 以群體播送支援IPv6環境下移動式網路連結更新之研究	★ 無線區域網路資源動態分配之效能研究
★ 在微觀移動環境下有效資源保留之路徑管理研究	★ 無線網路交握程序之預先認證方法分析與比較
★ 無線區域網路虛擬允入控制之研究	★ IPv6環境下移動網路之連結更新程序及其效能之研究
★ 具有限數量波長轉換節點的分波多工網路之群播波長分配與容量計算研究	★ 階層化行動式IPv6移動錨點選擇機制研究
★ 具高能量移動節點之叢集式感測網路效能研究	★ 預先註冊之快速換手階層化行動式IPv6研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2026-9-1以後開放)

摘要(中)

隨著低延遲的應用，如虛擬實境 (VR)、遠程手術和智慧工廠等不斷蓬勃發展，多接取邊緣運算 (MEC) 成為解決此類應用需求的有效方法。MEC 將運算伺服器放置在更接近裝置端的位置，以縮短數據傳輸時間，從而實現低延遲的任務處理。然而，由於 MEC 伺服器的運算資源相對有限，因此在有限的邊緣運算伺服器資源下最大化利用這些資源，並優化任務卸載策略以確保在有限環境中實現更佳的任務分配和處理成為一個挑戰。

在本篇論文中研究任務劃分策略，一般而言使用者所發出的任務通常包含多種方法或執行程序，因此可以將這些任務劃分為若干具有相依性的子任務，並使用有向無環圖 (DAG) 來模擬這些子任務之間的相依性。通過這種方式，可以進一步將子任務分成多個叢集 (Cluster)，並以此作為卸載的單位，而不是單獨地處理每個子任務，這樣的做法能夠充分利用子任務之間的相依性。並且通過設計叢集的權重，來決定卸載的優先順序，使邊緣運算伺服器能夠更好地進行資源分配。

本篇論文提出了兩種不同的叢集排序卸載策略，基於權重之叢集排序卸載策略以及基於權重和叢集運算量之叢集排序卸載策略。研究了這些策略對任務卸載所帶來的效益，並設計了多台邊緣運算伺服器環境下之資源分配方案，以加速整體任務處理速度，提高任務成功卸載之機率。

模擬結果顯示，在任務延遲容忍度 150ms 到 200ms、伺服器 4 台且使用者裝置 60 台時，將任務劃分並卸載至邊緣運算伺服器進行平行運算，基於權重和叢集運算量之叢集排序卸載策略的阻擋率會比任務無劃分卸載策略約低 16 倍，而基於權重之叢集排序卸載策略的阻擋率會比任務無劃分卸載策略約低 13.7 倍，皆可看出顯著效益。
此外本篇論文提出的策略在特定條件下 (例如在任務延遲約束較嚴格、使用者數量較多或是伺服器採用不同資源分配方案時) 各自表現出不同的優勢。

摘要(英)

Applications like virtual reality (VR), remote surgery, and smart factories are growing rapidly. Multi-access Edge Computing (MEC) effectively meets these applications’ low latency demands by placing computation servers closer to devices. However, MEC’s limited computational resources create challenges in maximizing resource utilization and optimizing task offloading strategies.

This thesis examines task partitioning strategies by breaking down tasks into subtasks and using Directed Acyclic Graphs (DAG) to model their dependencies. Subtasks are grouped into clusters for offloading, leveraging their dependencies. Offloading is prioritized based on cluster weights to improve resource allocation to edge computing servers.

Two cluster ordering strategies are proposed: one based on weight, and the other on weight and cluster cycles. The study investigates the benefits of these strategies, designing a multi-edge server resource allocation scheme to enhance task processing and meet delay constraints.

Simulation results indicate that with a latency tolerance of 150ms to 200ms, four servers, and sixty user equipment, the Weight and Cluster Cycles-Based Cluster Sorting Offloading Strategy reduces the blocking rate by approximately 16 times, and the Weight-Based Cluster Sorting Offloading Strategy by approximately 13.7 times, compared to non-partitioned offloading.

Additionally, these strategies show different advantages under specific conditions, such as stricter latency constraints, more users, or varied resource allocation schemes.

關鍵字(中)

★ 多接取邊緣運算
★ 任務劃分
★ 有向無環圖
★ 相依性任務卸載

關鍵字(英)

論文目次

摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 IX

第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 章節概要 2

第二章相關研究背景 3
2.1 LTE 架構基本介紹 3
2.1.1 LTE 上下行傳輸速率 4
2.2 邊緣運算 (Edge Computing) 6
2.2.1 多接取邊緣運算 (Multi-access Edge Computing) 7
2.2.2 雲運算 (Cloudlet Computing) 8
2.2.3 霧運算 (Fog Computing) 9
2.3 DAG (Directed Acyclic Graph，有向無環圖) 10
2.4 相關文獻 11

第三章研究方法 13
3.1 系統架構 13
3.2 系統流程 17
3.2.1 系統參數 17
3.2.2 任務劃分卸載流程 19
3.2.3 叢集結構和權重設計 21
3.2.4 邊緣運算伺服器卸載流程 23
3.2.5 叢集排序流程 24
3.2.6 叢集排程之流程 28
3.2.7 叢集分割一次結構和卸載之介紹 32

第四章模擬結果與討論 33
4.1 模擬環境 33
4.2 模擬參數 34
4.3 模擬結果比較 34
4.3.1 不同任務延遲約束與 UE 及 ES 數之平均等待時間影響分析 35
4.3.2 不同任務延遲約束與 ES 數之阻擋率影響分析 41
4.3.3 不同任務延遲約束於 ES 不同 CPU 配置之影響分析 46
4.3.4 比較 Non-partition vs Partition 並分析 49
4.3.5 平行度比較及分析 51

第五章結論 54
參考文獻 56

參考文獻

[1] Abbas Bradai, Kamal Singh, Toufik Ahmed, and Tinku Rasheed, "Cellular software defined networking a framework". IEEE Communications Magazine, vol. 53, no.6, June 2015, pp. 36-43.
[2] Houming Qiu, Kun Zhu, Nguyen Cong Luong, Changyan Yi, et al., "Applications of Auction and Mechanism Design in Edge Computing: A Survey". IEEE Transactions on Cognitive Communications and Networking, vol. 8, no.2, 2022, pp. 1034-1058.
[3] Lina A. Haibeh, Mustapha C. E. Yagoub and Abdallah Jarray, "A Survey on Mobile Edge Computing Infrastructure: Design, Resource Management, and Optimization Approaches". IEEE Access, vol. 10, 2022, pp. 27591-27610.
[4] Kai Jiang, Huan Zhou, Xin Chen, and Haijun Zhang, "Mobile Edge Computing for Ultra-Reliable and Low-Latency Communications". IEEE Communications Standards Magazine, vol. 5, no.2, 2021, pp. 68-75.
[5] Mohammad Babar, Muhammad Sohail Khan, Farman Ali, Muhammad Imran, et al., "Cloudlet Computing: Recent Advances, Taxonomy, and Challenges". IEEE Access, vol. 9, 2021, pp. 29609-29622.
[6] Guneet Kaur Walia, Mohit Kumar and Sukhpal Singh Gill, "AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives". IEEE Communications Surveys & Tutorials, vol. 26, no.1, 2024, pp. 619-669.
[7] Taj-Aldeen Naser Abdali, Rosilah Hassan, Azana Hafizah Mohd Aman, and Quang Ngoc Nguyen, "Fog Computing Advancement: Concept, Architecture, Applications, Advantages, and Open Issues". IEEE Access, vol. 9, 2021, pp. 75961-75980.
[8] Jiagang Liu, Ju Ren, Yongmin Zhang, Xuhong Peng, et al., "Efficient Dependent Task Offloading for Multiple Applications in MEC-Cloud System". IEEE Transactions on Mobile Computing, vol. 22, no.4, 2023, pp. 2147-2162.
[9] Mithun Mukherjee, Vikas Kumar, Dipendu Maity, Rakesh Matam, et al., "Delay-sensitive and Priority-aware Task Offloading for Edge Computing- assisted Healthcare Services", in GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020, pp. 1-5.
[10] Lingxiang Li, Marie Siew, Zhi Chen, and Tony Q. S. Quek, "Optimal Pricing for Job Offloading in the MEC System With Two Priority Classes". IEEE Transactions on Vehicular Technology, vol. 70, no.8, 2021, pp. 8080-8091.
[11] Jianhui Liu and Qi Zhang, "Offloading Schemes in Mobile Edge Computing for Ultra-Reliable Low Latency Communications". IEEE Access, vol. 6, 2018, pp. 12825-12837.
[12] Jianhui Liu and Qi Zhang, "Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC", in 2020 IEEE Wireless Communications and Networking Conference (WCNC). May 2020, pp. 1-6
[13] Xingxia Dai, Zhu Xiao, Hongbo Jiang, Ming Lei, et al., "Offloading Dependent Tasks in Edge Computing With Unknown System-Side Information". IEEE Transactions on Services Computing, vol. 16, no.6, 2023, pp. 4345-4359.
[14] Ying Shang, Jinglei Li and Xiguang Wu, "DAG-based Task Scheduling in Mobile Edge Computing", in 2020 7th International Conference on Information Science and Control Engineering (ICISCE), 2020, pp. 426-431.
[15] Xiaoyan Lv, Hongwei Du and Qiang Ye, "TBTOA: A DAG-Based Task Offloading Scheme for Mobile Edge Computing", in ICC 2022 - IEEE International Conference on Communications, 2022, pp. 4607-4612.
[16] Jianhui Liu and Qi Zhang, "Reliability and Latency Aware Code-Partitioning Offloading in Mobile Edge Computing", in 2019 IEEE Wireless Communications and Networking Conference (WCNC), April 2019, pp. 1-7.

指導教授

陳彥文

審核日期

2024-7-25

推文