對於網路服務提供者而言，操作人員可透過預測網路連線需求資訊來管控伺服器運作需求。本文提出一個傳輸控制協定短期連線需求預測模型，藉由自回歸模型結合斜率調整機制來預測傳輸控制協定的連線需求。實驗結果顯示，利用此模型進行連線需求預測，可以讓準確率從95.04%改善至96.13%。

Foreseeing possible TCP connect requests enables system administrators to consolidate resources onto their network servers. This thesis proposes a modified Autoregressive (AR) forecasting model, with a tuning process, slope tuning, that enhances the prediction results. The result of the conducted experiment shows an improvement in forecasting accuracy, which is rising from 95.04% to 96.13%.

致謝I
ABSTRACTII
摘要III
目錄IV
圖目錄VII
表目錄X
第一章1
1.1研究動機與背景1
1.2研究目的3
1.3章節組織3
第二章4
2.1文獻探討4
2.2 觀察網路流量變化8
2.3 線性預測模型10
第三章14
3.1 WIMAX網路環境模擬與流量記錄14
3.1.1 Network Simulator – Version 2簡介14
3.1.2 模擬WiMAX網路環境15
3.1.3 封包記錄格式17
3.2封包分析19
3.2.1 TCP傳輸協定分析20
3.2.2 封包流量統計25
3.3 自回歸預測模型29
3.3.1 AR模型運作流程31
3.3.2 柏格演算法(Burg algorithm)32
3.3.3 誤差調整機制與斜率調整機制36
第四章40
4.1 評估誤差調整機制與斜率調整機制40
4.1.1 評估誤差調整機制41
4.1.2 評估斜率調整機制42
4.1.3 評估兩種判斷機制與AR模型的預測誤差44
4.2 評估AR模型的階層(ORDER)45
4.3 評估歷史資料取樣時間間隔47
第五章51
5.1 結論51
5.2 未來展望52
參考文獻53
附錄A58
附錄B61
附錄C69
附錄D70
附錄E72

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