叢集小基地台網路上預測式輔助熱門影片快取方法研究

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研究生：	蔡伯元 Tsai, Po-Yuan
論文名稱：	叢集小基地台網路上預測式輔助熱門影片快取方法研究 Prediction-Based Caching of Popular Videos in Cluster-Centric Small Cell Networks
指導教授：	王家祥 Wang, Jia-Shung
口試委員:	蕭旭峰 Hsiao, Hsu-Feng 陳弘軒 Chen, Hung-Hsuan
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊系統與應用研究所 Institute of Information Systems and Applications
論文出版年：	2017
畢業學年度：	105
語文別：	英文
論文頁數：	44
中文關鍵詞：	快取、分群、預測、小基地台、行動裝置、盧比變化碼、叢集小基地台網路
外文關鍵詞：	Caching, Clustering, Prediction, Small Cells, Mobile Devices, LT codes, Cluster-Centric Small Cell Networks
相關次數：	點閱：3 下載：0
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　　近年來，行動網路的流量成長非常快速，其中又以影片的流量為大宗，為了因應這樣的狀況，叢集小基地台網路被利用來服務使用者的影片觀看需求。由於回程網路的流量限制，影片快取機制被用來降低網路流量。預測式輔助熱門影片快取方法的目標是預測出某時段內的熱門影片，並將其先行下載、快取，以供該時段的使用者觀看。
　　在訓練階段，對熱門影片有相似喜好的使用者會被分在同一個群內，而後每個群會分配給一個小基地台進行服務。藉由盧比變化碼將影片編碼的方式，多個小基地台可以一起合作，成為小基地台的群，群內的小基地台各自儲存影片的一部份，在使用者要求的時候合作解碼。在服務階段，會依照小基地台的群內的使用者的喜好，預測未來一段時間的熱門影片，預先將其下載、快取。對於新加入的使用者，會先對其進行觀察，再分配到最相似的群內。使用相似度進行使用者分群，不只可以減少快取被取代的可能性，也可以提昇預測的效果。
　　本篇論文所提出的方法在其中一組模擬實驗中，相較小基地台獨自服務使用者的快取方法，在兩百部、五十部、三十部、十部影片的情況下，分別可以降低 34.2%、29.9%、28.6%、23.6% 的下載率；相較小基地台合作，但不預測熱門影片的快取方法，在兩百部、五十部、三十部、十部影片的情況下，分別可以降低 7.4%、7.1%、5.8%、2.6% 的下載率。

Mobile traffic has grown very fast in recent years, particularly for videos watched on mobile devices. Cluster-centric small cell networks are used to serve the huge demand of watching videos. To avoid bottleneck in the limited capacity of backhaul link to the core network, caching in the network edge is such a way that the cached video could be delivered with less network traffic. The motivation of the prediction-based caching plan is to predict the most popular videos in a period, and cache them in advance for further requests.
In the training phase, users who have similar preference to popular videos will be assigned to the same cluster, and the cluster will be assigned to a small cell for serving the clustered users. Some small cells could cooperate together as a cluster of small cells, and share their cache space with the help of distributed LT codes. During the serving phase, the most popular videos will be predicted, and they could be cached to serve requests in the future. New users will be assigned to small cells based on their preference. Clustering users with similarity could not only decrease the possibility of caches being replaced, but also has the benefit of predicting videos ranking more accurately.
As the simulation result of one test case, the proposed methods decreased the global download rate by 34.2% with top 200 videos, 29.9% with top 50 videos, 28.6% with top 30 videos, 23.6% with top 10 videos compared to non-cooperative caching plan, and decreased by 7.4% with top 200 videos, 7.1% with top 50 videos, 5.8% with top 30 videos, and 2.6% with top 10 videos compared to cooperative but not predictive caching plan.

摘要    1
ABSTRACT    2
誌謝    3
CONTENTS    4
LIST OF FIGURES    6
LIST OF TABLES    7
Chapter 1. Introduction    8
Chapter 2. Related Works    11
1 LT Codes with Belief Propagation    11
2 Video Services in Wireless Network    12
2.1 Content Delivery by Cooperative Caching    12
2.2 Caching Policy based on Content Popularity    12
3 Community Detection in Social Networks    13
Chapter 3. Proposed Methods    14
1 Rank-Based Clustering Method    14
1.1 User Clustering    16
1.2 Cooperation Strategy for SCs    18
1.3 Assignment of New Users to SCs    20
2 Prediction-Based Caching Plan    21
2.1 Popular Videos Prediction    22
2.2 Videos Cached Adjustment    23
Chapter 4. Simulation and Experimental Results    25
1 Simulation of the Proposed Method    25
1.1 Environment    25
1.2 Test Cases    25
1.3 Number of Clusters    27
1.4 Cooperation Combinations    28
1.5 Quantity of Videos    28
1.6 Training Data    29
1.7 Cache Space of Each Small Cell    29
1.8 Proportion of Unreplaceable Cache Space    30
1.9 Cache Replacement Method    30
2 Results and Discussions    31
2.1 Comparison of Test Cases    31
2.2 Comparison of Cooperation Combinations    32
2.3 Comparison of Quantity of Videos    34
2.4 Comparison of Cache Space of Each Small Cell    35
2.5 Comparison of Proportion of Unreplaceable Cache Space for Predicted Popular Videos    36
2.6 Overall Performance    37
Chapter 5. Conclusion    41
REFERENCES    42
                                

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