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研究生:曾柏豪
研究生(外文):ZENG, BO-HAO
論文名稱:通過在雲計算網絡中使用多閾值來提高效率的新Sufferage算法
論文名稱(外文):The New Sufferage Algorithm to Enhance Efficiency by Using Multi-threshold in a Cloud Computing Network
指導教授:江茂綸江茂綸引用關係鄭煜輝鄭煜輝引用關係
指導教授(外文):CHIANG, MAO-LUNCHENG, YU-HUEI
口試委員:張庭毅鄭煜輝江茂綸
口試委員(外文):CHANG, TING-YICHENG, YU-HUEICHIANG, MAO-LUN
口試日期:2020-07-14
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊與通訊系
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:2020
畢業學年度:108
語文別:中文
論文頁數:47
中文關鍵詞:雲端計算負載平衡任務調度Sufferage
外文關鍵詞:Cloud ComputingLoad BalancingTask SchedulingSufferage
相關次數:
  • 被引用被引用:0
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  • 下載下載:0
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近年來,雲計算已成為提供不同類型應用程序的理想方式。它可以提供處理,存儲,Web服務和開發平台,以使公司能夠輕鬆地擴展其基礎架構和服務類型。隨著對更高處理能力的需求增加,大量數據將對雲計算環境造成巨大負擔。因此,為了確保雲端的雲服務及資源調度的速度,我們必須修正在雲服務中任務調度的問題。在這種情況下,為解決上述問題,提出了Balanced Multi-threshold Sufferage (BMS)調度算法。 BMS的主要思想是使用Smallest Sufferage Value (SSV),Largest Sufferage Value (LSV)作為調度因素,以提高任務分配的性能。根據實驗結果,提出的BMS演算法比現有算法具有更好的負載均衡和完工時間(makespan)。
In recent years, cloud computing has become the ideal way to provide different types of applications. It can provide processing, storage, web services and development platforms to enable the companies to easily scale their infrastructure and service types. As the demand for higher processing power increases, large amounts of data will cause a huge burden on the cloud computing environment. Therefore, in order to ensure speed of cloud service and resource scheduling, we must revise the problem of task scheduling in cloud services. Under such circumstances, a Balanced Multi-threshold Sufferage (BMS) scheduling algorithm environments has been proposed to improve the above problems in this paper. The main idea of BMS is using Smallest Sufferage Value (SSV) and Largest Sufferage Value (LSV) as the scheduling factor to enhance the performance of task dispatch. According to the experiment results, the proposed BMS algorithm can obtain better load balance and makespan than the existed algorithms.
目錄
摘要 I
ABSTRACT II
致謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章、 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 論文架構 4
第二章、 相關工作 5
2.1 排程演算法基本概念與介紹 5
2.1.1 批次模式排程演算法 5
第三章、 研究方法 12
3.1 Balanced Multi-threshold Sufferage演算法 12
3.2 實例說明 16
第四章、 實驗環境與結果 24
4.1 實驗環境 24
4.2 實驗結果 26
4.2.1 Makespan 26
4.2.2 負載平衡 32
4.2.3 資源利用率 38
第五章、 結論與未來工作 44
參考文獻 45
表目錄
表一、 BMS參數說明 12
表二、預期任務完成時間表格 17
表三、計算 SSV 18
表四、計算STASK 19
表五、計算LSV 20
表六、計算LTASK 21
表七、任務分配階段 22
表八、實驗參數 25
圖目錄
圖一、雲端運算架構 2
圖二、教務系統雲端運算架構圖 2
圖三、Min-Min演算法 6
圖四、Max-Min演算法 7
圖五、Sufferage演算法 8
圖六、MaxSufferae演算法 9
圖七、Advanced MaxSufferae演算法 11
圖八、Balanced Multi-threshold Sufferage演算法 14
圖九、演算法範例的結果 23
圖十、異質性環境下之平均完工時間實驗結果 31
圖十一、異質性環境下之負載平衡實驗結果 37
圖十二、異質性環境下之資源利用率實驗結果 43



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