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

An Optimization-based Task Allocation Strategy for Maximization of Revenue in Cloud Radio Access Networks

簡伯銓(Po-Chuan Chien)

指導教授 ： 林永松

國立臺灣大學/管理學院/資訊管理學研究所/碩士(2017年)

https://doi.org/10.6342/NTU201704109

摘要

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。

關鍵字

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

並列摘要

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.

並列關鍵字

5G ； cloud radio access network (C-RAN) ； task allocation strategy ； optimization ； Lagrangian relaxation

參考文獻

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