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\section*{Abstract}

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The focus of this dissertation is the real-time identification of bulk data transfers by Grid applications. Since Grid projects are increasingly significant users of the network, such identification can support better network management through online traffic re-engineering and consequently improve network performance for all users. Rather than collecting aggregate traffic statistics over five minute or more tiome intervals---as is commonly done---we classify and closely investigate per-flow properties.

Firstly, we perform Gigabit Ethernet line-rate monitoring of full packet payloads to undertake protocol reconstruction for applications under study. This gives precise flow classification independent of well-known port number techniques and also identifies related flows}\DIFaddend{5} (\DIFaddbegin{6}\DIFadd{e.g., parallel bulk connections alongside control connections}\DIFaddend{6})\DIFaddbegin{7}\DIFadd{. Secondly, we do line-rate heuristic flow analysis to classify encrypted or payload-unavailable flows. Beyond classification, we also give probabilistic notification of application-level phase changes, which enable the tagging of associated flows as related bulk data.

To tackle the first of these techniques, we developed a real-time monitoring system based on the Endace DAG card. Using application-level and network-level traces in conjunction with an understanding of protocol behaviour, informed by documentation and source code, we constructed plain-text protocol analysers. We evaluated the efficacy of this system at a University Computing Science department Internet access point and found ...}{}\DIFadd{. For the heuristic analysis, we again used traces and knowledge of protocol behaviour to construct recognisers using X, Y and Z. These can be evaluated in real-time to generate events for a network management system }\nts{indicate effectiveness/efficiency}\DIFadd{.

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