The rapid growth of Internet in size and complexity, and frequent emergence of new network applications have made it necessary to develop techniques that can monitor and control the traffic. Efficient and accurate recognition of traffic is the key to the management in real time. This thesis work accomplishes the performance evaluation and optimization of a traffic recognition tool called Traffic Analyzer Module (TAM) which implements a technique that is based on passively observing and identifying signature patterns of the packet payload at the application layer, says signature-based payload recognition. This technique has two highlighted features. Firstly, in contrast to most of previous works which perform classification with offline trace files; this technique applies in online mode which can identify the traffic in real time. Secondly, instead of packet inspection, this technique adopts flow inspection, i.e. identifying traffic in terms of flows each of which consists of the well-known 5-tuple, which canproduce more accurate and reliable results…
Contents
1 Introduction
2 Related Work
3 Background
3.1 Requirement
3.2 Prototype
3.2.1 Mechanism
3.2.2 Architecture
3.2.3 Traffic Analyzer Module (TAM)
3.2.4 Traffic Recognition
4 Throughput Measurement and Optimization
4.1 Experimental Setup
4.1.1 Equipments
4.1.2 Traffic
4.1.3 Metrics
4.1.4 Measurement Point
4.1.5 Configuration
4.2 Results Overview
4.3 Experiments
4.3.1 Experiment 1: Getting Started
4.3.2 Experiment 2: At the Edge of the Network
4.3.3 Experiment 3: Heavy Traffic Load
4.3.4 Experiment 4: Kernel Configuration
4.3.5 Experiment 5: No Profiling
4.3.6 Experiment 6: No Marshalling
4.3.7 Experiment 7: Recognition Optimization
4.3.8 Experiment 8: Gigabit Ethernet
4.3.9 Experiment 9: Bandwidth Tuning
4.3.10 Experiment 10: Traffic Snapshot
5 Conclusion
6 Discussion
6.1 Limitations
6.2 Future Work
7 References
Appendices
A. Definitions
B. Abbreviations
Author: Cao, Hua
Source: Uppsala University Library
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