臺灣博碩士論文加值系統

一般而言，封包失序傳遞（Packet Reordering）都會被視作對網路效能產生負面影響的因素。但是，在本論文中，針對早期隨機偵測（Random Early Detection）丟棄封包所會產生的封包失序問題，我們提出一個想法，藉由利用這種封包失序傳遞的現象來增進網路的效能。在現今網路中預先防止佇列滿額的主動式佇列管理（Active Queue Management）最著名就是早期隨機偵測（RED），而當早期隨機偵測為避免網路壅塞的發生而隨著機率來丟棄封包時，所引發封包失序傳遞的現象，借由我們修改過RED後的結構進而轉成幫助提昇網路的效能。簡言之，我們的想法是要去利用而不是降低封包失序傳遞的現象。作法是將早期隨機偵測（RED）中的佇列（Queue）分隔為兩個部份，佇列一跟佇列二。佇列一的功用與原先RED的佇列相同，佇列二被設計來保留被RED依機率所丟棄的封包，隨後藉由特別的排程演算法傳送出去來節省封包恢復重傳的時間，這種修改RED並且保留丟棄封包等待傳送的架構，我們稱之為Random Early Detection with Recovery Queue （R2Q）。在本論文中設計有兩種排程演算法可供利用，一種為優先權排程（Priority Scheduling），另一種為加權輪替式排程（Weighted Round-Robin Scheduling）。我們使用J-Sim模擬器來模擬網路環境及架構，驗證我們的想法。

Packet reordering is generally considered to have negative impact on network performance. In this thesis, the packet reordering is used to assist TCP to recover faster in RED-enabled packet switched networks. The RED queue management prevents networks from congestion by dropping packets with a probability earlier than the time when congestion would actually occur. After a RED router drops a packet, packer reordering is introduced during TCP’s recovery process. A new, simple buffer mechanism, called RED with Recovery Queue or R2Q, is proposed to create this type of packet reordering on behalf of TCP with the objective of accelerating TCP’s recovery and thus improving the overall network performance. In R2Q, the original RED queue is segmented into two sub-queues. The first sub-queue remains the function of the original RED while the second picks up the packets discarded by the first. Then, scheduling of the second-chance transmission of the packets in the secondary sub-queue is the key in achieving our objective. In this thesis, we considered two scheduling schemes: priority and weighted round robin. To evaluate the performance of R2Q with these two scheduling schemes, we implemented and evaluated them in the J-Sim network simulation environment. The well-known dumbbell network topology was adopted and we varied different parameters, such as round-trip time, bottleneck bandwidth, buffer size, WRR weight and so on, in order to understand how R2Q performs under different network configurations. We found that R2Q is more effective in the networks of sufficient buffer and larger product of RTT and bandwidth. With WRR, we may achieve as much as 2% improvement over the original RED. The improvement may be more in networks of even higher speed.

致 謝 iv
摘 要 v
Abstract vi
List of Tables ix
List of Figures x
1. Introduction - 1 -
2. Background - 4 -
2.1 Quality of Service (QoS) - 4 -
2.1.1 Integrated Service (IntServ) Architecture - 4 -
2.1.2 Differentiated Service (DiffServ) Architecture - 6 -
2.2 Queue Management - 9 -
2.2.1 Passive Queue Management (PQM) - 9 -
2.2.2 Active Queue Management (AQM) - 10 -
2.3 Random Early Detection (RED) - 11 -
2.4 Packet Reordering - 12 -
3. Random Early Detection with Recovery Queue (R2Q) - 15 -
3.1 R2Q Structure - 15 -
3.2 Principle - 16 -
3.3 Transmission Scheduling - 17 -
3.3.1 Pre-matured Packet Reordering - 18 -
3.3.2 Priority Scheduling - 18 -
3.3.3 Weighted Round-Robin Scheduling - 19 -
4. Simulation Results - 22 -
4.1 Simulation Setup - 22 -
4.2 Choice of RED Configuration - 24 -
4.2.1 RED Simulation: RED( , , 1/100, 0.00048 ) - 24 -
4.3 R2Q Simulation - 25 -
4.3.1 R2Q Simulation 1: Priority Scheduling - 26 -
4.3.2 R2Q Simulation 2: Weighted Round-Robin Scheduling - 26 -
4.3.3 Conclusion: RED V.S. R2Q - 28 -
4.3.4 R2Q Simulation 3: Round-Trip Time and Bottleneck Bandwidth - 28 -
4.3.5 R2Q Simulation 5: Total Buffer Size - 29 -
5. Conclusion - 31 -

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