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研究生:郭泰麟
研究生(外文):Tai-Lin Kuo
論文名稱:基於邊緣運算之資料流導向任務的負載平衡解決方案
論文名稱(外文):The Load Balancing Solution for Dataflow-Oriented Jobs in Edge Computing
指導教授:王尉任王尉任引用關係
指導教授(外文):Wei-Jen Wang
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:50
中文關鍵詞:邊緣運算智慧物聯網資料流導向任務負載平衡
外文關鍵詞:Edge ComputingAIoTDataflow-oriented JobsLoad Balancing
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近年來隨著物聯網 (IoT) 與人工智慧 (AI) 技術的蓬勃發展,智慧物聯網 (AIoT) 的應用服務也越來越熱門。由於服務對於低網路延遲、高資料隱私等需求的增加,使得邊緣運算相對於雲端運算擁有較為顯著的優勢。
然而,在邊緣運算的環境中,除了運算資源的限制外,運算設備也容易因為能源限制或過熱等環境因素影響而導致運行效率突然降低。而在AIoT的應用服務當中,所執行的運算任務通常需要處理連續的資料流,且任務可能會有著前後執行的關聯,因此當運算設備遇到突發的運算效能下降而使部分任務無法符合服務品質 (QoS) 的限制時,則可能會造成後續任務的延誤或停滯。
為了解決上述問題,本研究將在邊緣智慧物聯網的應用架構下,針對資料流導向任務的工作流程提出自動化的負載平衡解決方案。在任務因運算設備的效能變動而導致過載時,可以自動化的進行偵測與判斷,並將過載的任務轉移至合適的節點,恢復整體工作流程的運行效率。
In recent years, due to the vigorous development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies, the applications of Artificial Intelligence of Things (AIoT) has become more and more popular. Because of the increasing demand for such as the low latency and high data privacy of services, edge computing has more prominent advantages than cloud computing.
However, in the edge computing environment, except for the computing resources constraint, the computing efficiency of computing devices will be affected by energy constraints or physical factors such as temperature. Moreover, the computing jobs of AIoT usually deal with continuous dataflows, and maybe context-dependent. As a result, the decrease of computing efficiency may lead to the quality of service (QoS) of jobs unaffordable, moreover, causing the workflow stagnant.
To solve the problem, in this paper, we propose the automatic load balancing solution for dataflow-oriented jobs in edge computing. Automatically detect the overloaded jobs and transfer them to appropriate edge servers to resume the calculations and make the workflow back to the normal state.
摘要 i
Abstract ii
目錄 iii
表目錄 vi
圖目錄 v
第一章 緒論 1
1-1 研究背景 1
1-2 研究動機與目標 2
1-3 論文貢獻 3
1-4 論文架構 4
第二章 相關研究 5
2-1 資料流導向任務的相關研究 5
2-1-1 Towards Workload Balancing in Fog Computing Empowered IoT 5
2-1-2 Cooperative Load Balancing Scheme for Edge Computing Resources 5
2-2 相關研究的分析與討論 6
第三章 演算法 9
3-1 過載偵測階段 (Overload Detection Phase) 9
3-1-1 任務執行效率與QoS評估 9
3-1-2 實行任務轉移的標準 11
3-1-3 演算法流程 12
3-2 任務轉移階段 (Jobs Transfer Phase) 15
3-2-1 任務過載原因的判斷與分析 15
3-2-3 任務與節點的運算資源評估 16
3-2-3 目標節點的選擇策略 18
3-2-4 演算法流程 18
第四章 實驗 22
4-1 實驗環境與系統架構 22
4-1-1系統架構 22
4-1-2 Load Balancer實作與演算法參數 24
4-1-3 過載狀態的模擬 25
4-2 實驗設計與結果分析 25
4-2-1 實驗一:單執行緒任務的工作流程 26
4-2-2 實驗二:多執行緒任務的工作流程 30
4-2-3 實驗三:斷線導致無法取得資料 34
第五章 結論與未來研究方向 37
參考文獻 38
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