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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

IEEE 802.16網路以支持向量機配置頻寬 / Bandwidth allocation using support vector machine in IEEE 802.16 networks

李俊毅, Li, Chun-Yi Unknown Date (has links)
近幾年無線寬頻網路崛起,寄望WiMAX可以取代最後一哩,雖然WiMAX有QoS的設計,但是對於Call Admission Control、Bandwidth Allocation、Scheduler並沒有實際定義,給予廠商彈性設計。本篇論文提出以機器學習的方式依據網路狀態動態配置頻寬,以符合實際頻寬需求。 由於BS在配置頻寬的時候並沒有SS佇列的訊息,使得BS無法配置適合的頻寬,達到較好的效能,尤其是有期限的rtPS封包最為明顯。在系統負載較高的環境下,容易導致封包遺失提升,吞吐量降低的情形發生。因此本研究提出了支持向量機的方式,收集大量Training Data,訓練成動態頻寬配置模組;以動態配置適合的頻寬給rtPS,使rtPS在負載高的環境下的封包遺失率降低,且延遲能夠維持一定水準。搭配適應性頻寬配置策略,在低負載的環境下可以保留少許頻寬給Non Real Time Traffic,在高負載環境下,先滿足Real Time Traffic為原則。模擬工具採用NS 2-2.29、長庚大學-資策會的WiMAX模組,以及台大林智仁老師開發的支持向量機函式庫libSVM。 / In recent years, the rise of wireless broadband access networks. Hope that WiMAX can solve the last mile problem. Although WiMAX has QoS design, but for call admission control, bandwidth allocation, scheduler are not defined in standard. In this paper, we proposed a machine learning approach dynamic bandwidth allocation based on network state. BS because of the bandwidth allocation at a time when there is no message of SS’s queue. Enables BS can not configure a more suitable bandwidth to achieve better performance. In particular, there is the deadline of rtPS packets. At the higher loading on the system environment, easily lead to packet loss raise, lower throughput situations happen. In this study, a support vector machine approach to collect a large number of training data. Training modules into a dynamic bandwidth allocation. We can dynamically allocate bandwidth to fit rtPS. Adaptive bandwidth allocation strategy, at the low loading environment can keep some bandwidth for non real time traffic. At a high loading environment must first meet the real time traffic. We use Network Simulator 2-2.29, CGU-III WiMAX module, libSVM library.
2

數位網路上多重目標規劃的數學模式 / Mathematical Models of Pareto Optimal Path Selection on All-IP Networks

王嘉宏, Wang, Chia-Hung Unknown Date (has links)
面對通訊與資訊科技的大幅進步,通訊網路正在進行一個巨大的變革,要將電信網路與數據網路整合成一個單一的All-IP網路以支援所有網路應用服務。欲達到整合型網路的理想,仍有許多困難尚待克服,而服務品質問題是其中最關鍵的問題之一。因為受限於封包交換網路之原有的特性,All-IP網路有影響服務品質的三項因素:過長的延遲時間、抖動以及封包遺失。首先,我們利用了達成度函數(achievement function)來處理單位的轉換,使得能夠同時考量此三項不同單位的因素。接著,本文中提出一套方法來解決All-IP網路上端對端(end-to-end)的資源配置及路徑規劃問題。在分配資源時,我們企圖提供一種成比例的公平性給各個不同等級。此公平性的精神是要使得所有網路使用者的滿足程度相當,而非各個不同等級的使用者分配到相同的資源。我們將以預算方式控制端對端品質管理以追求使用者之整體最大滿意程度。 本論文的規劃概念是將網路規劃分成兩個階段。第一階段是在一筆給定的總預算底下,以成比例的方式去分配資源給各個不同等級,並建置網路上的頻寬,使各等級能依其需求拿到適當的頻寬,確保滿足程度相當。 接下來第二階段則是在第一部份已完成的規劃基礎下,做路徑規劃,指派新進入的使用者到一條較好的路徑,在滿足此使用者的延遲時間要求下,使此系統的壅塞程度越小越好。路徑規劃的概念為如何挑選最佳網路路徑,以規劃具服務品質之端對端路徑,並可達到資源之最有效利用。網路營運者將可運用此套方法來調校自身所營運的網路以追求使用者最高滿意度。 / We present an approach for the fair resource allocation problem and QoS routing in All-IP networks that offer multiple services to users. The objective of the optimization problem is to determine the amount of required bandwidth for each link and each class to maximize the sum of the users' utility. In this work, we focus on approaches that, while allocating bandwidth, attempt to provide a proportionally fair treatment of all the competing classes. First, we will show that an achievement function can map different criteria subject to various utility onto a normalized scale. It may be interpreted as a measure of QoS (Quality of Service) on All-IP networks. Using the bandwidth allocation model, we can find a Pareto optimal allocation of bandwidth on the network under a limited available budget. This allocation can provide the so-called proportional fairness to every class, that is, this allocation can provide the similar satisfaction to each user. Next, we present a routing scheme under consideration of the delay. Such an optimal path provides the end-to-end QoS guarantees to each user. Finally, a numerical example is given to illustrate how to solve the fair resource allocation problem and how to modify the nonlinear parts.

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