研究成果--90級資管所博碩士班論文簡介
無線網路下具服務品質需求的多媒體傳輸資源配置之研究-羅哲峰(Master)
題目(中) 無線網路下具服務品質需求的多媒體傳輸資源配置之研究
題目(英) Resource Allocation for Multimedia with QoS Requirement in Wireless Network
研究生 羅哲峰(碩士學位)
指導教授 陳嘉玫
摘要(中) 隨著網路科技的快速發展,人類的日常生活已經和網際網路緊密的結合在一起。加上無線網路技術的日趨成熟,未來利用行動通訊設備來傳輸多媒體(如影像)資訊必將成為下一波網際網路新趨勢。加上愈來愈多的影像、聲音等即時資料或大量資訊需要在網路上傳送,對即時性的要求也愈來愈高,網路頻寬早已不敷所需。因此,為了解決網路壅塞的問題,也許不只在硬體上的增加頻寬,而且在如何有效管理與分配這種有限且寶貴的資源的工作上更是不可少的。
本研究提出一個動態資源配置的方法,以reward-penalty(報酬─賠償)的觀念,找出在有限的資源下做最有效的分配的解決方案。使得真正需要使用資源的使用者可以確實地獲得所要求的資源,並得到服務品質保證。系統資源管理者或服務提供業者在有限資源的限制下,透過允入控制與資源配置兩個重要程序,將資源作最有效的管理與分配,並同時讓系統獲得最大的收益。而系統內的使用者為配合系統的資源分配,本身的資源調整也以平滑式(smooth)的調整為原則。因此本研究所提出之動態資源配置演算法在資源分配上除了對新提出要求的資源做最有效的配置外,對已存在的使用者提供使用者要求的資源和保證服務品質,同時系統更能獲得本身收益的最大化。
在模擬實驗的結果中,我們可以得知本研究所提出的reward-penalty(報酬─賠償)的觀念所建構出來之系統,顯著的優於所謂的考慮單一reward (報酬)的觀念所建構出來的系統,兩者模擬比較後可以發現,CB method在做允入控制時不僅考慮了要求者的報酬率(reward rate),同時也對其賠償率(penalty rate)列為預測的重要參數之一,有效掌握此兩者資訊,便能表現出較佳的效能。
摘要(英)

With the rapid development of Web-based technologies, our daily life has become intensely involved with Internet. Combined with the maturity of wireless network technologies, the transmission of multimedia data using mobile communication equipments will surely become the next step of Internet usage. More and more real-time data and massive amount of information are being transmitted on the Internet, making the bandwidth a scarce resource. To resolve the congestion of Internet, therefore, the efficient management and distribution of limited and valuable resource is more important than the enhancement of it.

Our research posed a dynamic resource allocation method, which exploited the reward-penalty concept in order to find the most efficient allocation solution under the constraint of limited resources. The method enabled the users who need to use resource to achieve the necessary resources and their guarantee of quality. The system resource managers or service providers could make the best arrangement of their constrained resources and gain the highest reward through two essential procedures: Admission Control and Resource Allocation. Users themselves, on the other hand, “smoothly” adjust the resource they had to match the resources they gained. Our algorithm provided existent users with what they requested while at the same time maximized the benefit of the system and made the most efficient arrangement of resources in regards to new requests.

The consequences of simulation experiments showed that our system, which was based on reward-penalty model, is apparently superior to the so-called one based on reward model. The results also showed that CB method took users’ reward rate as well as their penalty rate into account while maintaining admission control.

論文下載 etd-0720101-143053.pdf
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