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

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Title:	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
Authors:	王育琪;YU-CHI, WANG
Contributors:	通訊工程學系
Keywords:	多接取邊緣運算;任務劃分;有向無環圖;相依性任務卸載
Date:	2024-07-25
Issue Date:	2024-10-09 16:41:44 (UTC+8)
Publisher:	國立中央大學
Abstract:	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。;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.
Appears in Collections:	[Graduate Institute of Communication Engineering] Electronic Thesis & Dissertation

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