| DC 欄位 |
值 |
語言 |
| DC.contributor | 資訊工程學系 | zh_TW |
| DC.creator | 張峻瑋 | zh_TW |
| DC.creator | Chun-Wei Chang | en_US |
| dc.date.accessioned | 2023-10-20T07:39:07Z | |
| dc.date.available | 2023-10-20T07:39:07Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=110522098 | |
| dc.contributor.department | 資訊工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 由於機器學習的技術不斷的進步，任務對於雲端群集的需求逐漸增加，這些資源密集型的任務需要更多的計算資源來支持，更多的計算資源和任務就表示對於資源分配要有更好的處理。對於阿里巴巴所提出的模擬器，它是利用貪婪演算法進行任務排程，因為貪婪演算法存在著一些缺點，因此本論文提出以Min-Min演算法和Max-Min演算法以動態任務分配的方式來改善貪婪演算法的缺點。另外，所提出之排程演算法因具有高時間複雜度，這對於大規模分散式系統的任務排程造成了挑戰，所以提出了將任務已更小的單位切割任務資料集後再進行排程。實驗結果表明，Min-Min演算和Max-Min演算法之於阿里巴巴所提出的模擬器結果在任務的 Makespan 有良好的表現，而在切割任務後的結果來看也有良好的表現。 | zh_TW |
| dc.description.abstract | Due to the continuous advancements in machine learning technology, there is a growing demand for tasks in cloud clusters. These resource-intensive tasks require more computational resources to support them. More tasks and computational resources imply a need for improved resource allocation. In the case of the simulator proposed by Alibaba, it utilizes a greedy algorithm for task scheduling. However, since greedy algorithms have their limitations, this paper suggests enhancing the drawbacks of the greedy algorithm by employing the Min-Min and Max-Min algorithms with dynamic task allocation.
Furthermore, the scheduling algorithms introduced here present a challenge due to their high time complexity, especially in the context of task scheduling in large-scale distributed systems. Therefore, it is proposed to break down the task dataset into smaller units for scheduling. Experimental results indicate that both the Min Min and Max-Min algorithms perform well in terms of the Makespan of tasks in Alibaba′s simulator. Moreover, when considering the results after task partitioning, these algorithms still demonstrate excellent performance. | en_US |
| DC.subject | 雲端 | zh_TW |
| DC.subject | 機器學習工作 | zh_TW |
| DC.subject | 總完工時間最佳化 | zh_TW |
| DC.subject | 快速排程策略 | zh_TW |
| DC.subject | Cloud Computing | en_US |
| DC.subject | ML-Task Scheduling | en_US |
| DC.subject | Scheduling Decision Time | en_US |
| DC.subject | Makespan | en_US |
| DC.title | 在有限決策時間下的雲端機器學習計算工 作排程總完工時間最佳化策略研究 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Optimization Strategy for Makespan of ML-Task Scheduling on the Cloud with the Constraint of Scheduling Decision Time | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |