The proliferation of mobile devices and the fact that high-bandwidth and continuous connectivity is not available everywhere, has led to the creation of hoarding algorithms that attempt to mitigate the problems related with disconnected operation and with the operation in areas where bandwidth is either scarce or expensive. In this dissertation, a generic hoarding approach for semi-structured information is introduced that has been specialized and evaluated for Web page requests. The approach is based on an infrastructure of infostations that provide comparatively cheap high-speed access to the Web. It relies on clustering of semantically related data items by classifying pages into content and transit pages, where this classification is used to guide the clustering algorithm. It could be shown shown by means of experimental evaluation that the proposed hoarding scheme outperforms existing ones by a factor of more than three in terms of content hit ratio. Moreover, an analysis of energy consumption of wireless interfaces could show that the proposed hoarding scheme also allows for substantial energy savings of mobile devices when accessing data. The performance evaluation of Web applications usually requires the analysis of sequences of user requests for specific Web pages. These sequences can be obtained, for example, by applying empirical methods (recording the real sequence of requests), or by applying a formal model for generating synthetic results. In this dissertation, a novel Web Browsing Model is proposed. By taking into account the hyperlink structure as well as the different user behavior on the Web, the implementation of this model allows us to generate accurate synthetic data that can be used instead of empirically obtained requests. Additionally, it could be shown using empirical data that the probability of choosing some hyperlink from a given page as well as the probability of a user leaving a page without following a hyperlink is best characterized by a power-law. In order to validate the approach, I could show the flexibility and applicability of the Web Browsing Model by performing the required correlations to empirical data.