This work approaches the little studied topic of compiler optimisations directed to network applications. It starts by investigating if there exist any fundamental differences between application domains that justify the development and tuning of domain-specific compiler optimisations. It shows an automated approach that is capable of identifying domain-specific workload characterisations and presenting them in a readily interpretable format based on decision trees. The generated workload profiles summarise key resource utilisation issues and enable compiler engineers to address the highlighted bottlenecks. By applying this methodology to data intensive network infrastructure application it shows that data organisation is the key obstacle to overcome in order to achieve high performance. It therefore proposes and evaluates three specialised data transformations (structure splitting, array regrouping, and software caching) against the industrial EEMBC networking benchmarks and real-world data sets. It also demonstrates on one hand that speedups of up to 2.62 can be achieved, but on the other that no single solution performs equally well across different network traffic scenarios. Hence, to address this issue, an adaptive software caching scheme for high frequency route lookup operations is introduced and its effectiveness evaluated one more time against EEMBC networking benchmarks and real-world data sets achieving speedups of up to 3.30 and 2.27. The results clearly demonstrate that adaptive data organisation schemes are necessary to ensure optimal performance under varying network loads. Finally this research addresses another issue introduced by data transformations such as array regrouping and software caching, i.e. the need for static analysis to allow efficient resource allocation. This thesis proposes a static code analyser that allows the automatic resource analysis of source code containing lists and tree structures. The tool applies a combination of amortised analysis and separation logic methodology to real code and is able to evaluate type and resource usage of existing data structures, which can be used to compute global resource consumption values for full data intensive network applications.