臺灣博碩士論文加值系統

在Universal Mobile Telecommunications System (UMTS)，GPRS Tunneling (GTP) 協定的延伸稱為GTP’協定，負責把計費資料紀錄從GPRS服務節點傳送到計費閘道。為了確保行動營運商能收到計費資料，GTP’協定的傳輸可靠性 (Reliability 及 Availability) 是很重要的。而GTP’協定的性能評估之重要指標是連線失敗偵測。在本論文，我們研究在第三代通訊規格TS29.060及TS 32.215所提出的GTP’連線錯誤偵測機制。我們希望選取適當的參數值，避免偵測出錯誤的連線失敗(False Failure Detection；例如因暫時性的網路擁塞所引致)。同時，我們希望可以儘快地偵測出真正的連線失敗，當真正的連線失敗被偵測後，GPRS服務節點可立即導向到另一台計費閘道。我們提出一個分析模型去計算錯誤的連線失敗偵測機率以及偵測出真正的連線失敗所需之期望時間。這個分析模型所導出的分析結果已和模擬實驗所得到的數據互相驗證。依據我們的研究結果，行動營運商可針對不同的狀況去調整參數，以降低錯誤的連線失敗偵測和/或偵測真正連線失敗所需的時間。

In Universal Mobile Telecommunications System (UMTS), the extension of GPRS tunneling protocol called GTP’ is utilized to transfer the Charging Data Records (CDRs) from GPRS Support Nodes (GSNs) to Charging Gateways (CGs). To ensure that the mobile operator receives the charging information, availability for the charging system is essential. One of the most important issues on GTP’ availability is connection failure detection. This paper studies the GTP’ connection failure detection mechanism specified in 3GPP TS 29.060 and 3GPP TS 32.215. It is desirable to select appropriate parameter values to avoid false failure detections (e.g., temporary network congestions). It is also important to detect the true failures quickly, and after a true failure is detected, the GSNs can immediately re-direct to another CG. In this paper, we propose an analytic model to compute the false failure detection probability and the expected true failure detection time. The analytic model is validated against simulation experiments. Based on our study, the network operator can select the appropriate parameter values for various traffic conditions to reduce the probability of false failure detection and/or true failure detection time.

中文摘要 i
ABSTRACT ii
Acknowledgment iii
Contents iv
List of Figures v
Chapter 1 Introduction 1
Chapter 2 The GTP’ Protocol 4
2.1 GTP’ Message Format 5
2.2 GTP’ Connection Setup Procedure 8
2.3 GTP’ CDR Transfer Procedure 10
2.4 GTP’ Failure Detection 11
Chapter 3 Probability of False Failure Detection 14
Chapter 4 Expected True Failure Detection Time 18
4.1 Derivation for the NK(tf) distribution 19
4.2 Derivation for E[�輐] 24
Chapter 5 Numerical Examples 28
5.1 Effects of input parameters on �� 29
5.2 Effects of input parameters on E[�輐] 32
Chapter 6 Conclusions 35
Appendix A Derivation for Pr[NK→∞(t0)=0] 36
Appendix B Derivations for fl (tl) and Fl (tl) 38
Appendix C Derivation for E[ |m=0] and Pr[m=0] 39
Appendix D Notation 44
References 46

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