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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林永松 | |
| dc.contributor.author | Po-Chuan Chien | en |
| dc.contributor.author | 簡伯銓 | zh_TW |
| dc.date.accessioned | 2021-05-13T06:48:48Z | - |
| dc.date.available | 2017-08-31 | |
| dc.date.available | 2021-05-13T06:48:48Z | - |
| dc.date.copyright | 2017-08-31 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-20 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2714 | - |
| dc.description.abstract | 為了應付節節攀升的網路流量，網路系統也必須與時俱進，而第五代移動通信系統（5G）正是為了解決此項問題而提出的目標。在 5G 之中，雲端接取網路因為將基帶處理器與無線寬頻頭端設備分離並集中於中央統一管理，因而可以更有效率的使用計算資源。在本篇論文之中，我們基於拉格朗日鬆弛法，輔以裝箱問題、排程、負載均衡算法，以及在營運時將面臨到的資源限制，提出了一套應用於雲端接取網路的工作分配策略來最大化網路營運商的收益。本篇論文亦模擬了各種情況，並提供實驗模擬數據來說明本方法確能增加網路營運商的利潤。 | zh_TW |
| dc.description.abstract | Due to the rapid increase in the network traffic load, the Internet service system must improve to meet the requirement. Fifth generation (5G) mobile networks aim to deal with this problem, and cloud radio access networks (C-RANs) is a popular approach to this goal. In a C-RAN, baseband processing units are centralized into a pool, which allows us to have a better resource utilization. In this thesis, we use the Lagrangian relaxation method combined with bin packing, scheduling, and traffic shaping to derive a task allocation strategy in a C-RAN that tries to maximize the profit of a network operator who may face multiple kinds of constraints during its operation. After that, we will present the experimental results to show the effectiveness of our proposed method. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-13T06:48:48Z (GMT). No. of bitstreams: 1 ntu-106-R04725015-1.pdf: 828379 bytes, checksum: 7b8d74de8cc5aa3346f716d2ed59909c (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 誌謝 iii
摘要 v Abstract vii 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Related Work 7 2.1 Bin packing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Task scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Load balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3 Mathematical Model 13 3.1 Problem description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3 Task assignment constraints . . . . . . . . . . . . . . . . . . . . . . . . 16 3.4 Capacity constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.5 Server switch constraints . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4 Solution Approach 23 4.1 Lagrangian relaxation method . . . . . . . . . . . . . . . . . . . . . . . 23 4.2 Lagrangian relaxation objective function and constraints . . . . . . . . . 25 4.3 Task assignment subproblem - SP1 . . . . . . . . . . . . . . . . . . . . . 27 4.4 Server power on subproblem - SP2 . . . . . . . . . . . . . . . . . . . . . 28 4.5 Server re-power on related subproblem - SP3 . . . . . . . . . . . . . . . 30 4.6 Block subproblem - SP4 . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.7 Auxiliary subproblem - SP5 . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.8 Getting primal feasible solution . . . . . . . . . . . . . . . . . . . . . . . 33 5 Computational Experiments 39 5.1 Experiment on task quantity . . . . . . . . . . . . . . . . . . . . . . . . 41 5.1.1 Uniform arrival . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.1.2 Bursty arrival . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.2 Experiment on fixed capacity with different number of servers . . . . . . 44 5.2.1 High capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.2.2 Medium capacity . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.2.3 Low capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.3 Experiment on processing time . . . . . . . . . . . . . . . . . . . . . . . 50 5.4 Experiment on tolerance and waiting time . . . . . . . . . . . . . . . . . 53 5.5 Experiment on task block penalty . . . . . . . . . . . . . . . . . . . . . . 54 5.6 Experiment on task revenue rate . . . . . . . . . . . . . . . . . . . . . . 56 5.7 Experiment on server cost rate . . . . . . . . . . . . . . . . . . . . . . . 58 6 Conclusion and Future Work 61 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 6.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 References 65 | |
| dc.language.iso | en | |
| dc.subject | 拉格朗日鬆弛法 | zh_TW |
| dc.subject | 第五代移動通信系統 | zh_TW |
| dc.subject | 無線接取網路 | zh_TW |
| dc.subject | 工作分配策略 | zh_TW |
| dc.subject | 最佳化 | zh_TW |
| dc.subject | cloud radio access network (C-RAN) | en |
| dc.subject | task allocation strategy | en |
| dc.subject | Lagrangian relaxation | en |
| dc.subject | optimization | en |
| dc.subject | 5G | en |
| dc.title | 於雲端接取網路中以最佳化為基礎使收益最大化之工作分配策略 | zh_TW |
| dc.title | An Optimization-based Task Allocation Strategy for Maximization of Revenue in Cloud Radio Access Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 溫演福,莊東穎,林宜隆 | |
| dc.subject.keyword | 第五代移動通信系統,無線接取網路,工作分配策略,最佳化,拉格朗日鬆弛法, | zh_TW |
| dc.subject.keyword | 5G,cloud radio access network (C-RAN),task allocation strategy,optimization,Lagrangian relaxation, | en |
| dc.relation.page | 69 | |
| dc.identifier.doi | 10.6342/NTU201704109 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2017-08-21 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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