Adaptation framework for wireless thin-client computing

This dissertation presents two novel contributions in the area of wireless thin-client computing. The first contribution is a mathematical performance model for a wireless thin-client system. This model identifies the factors that affect the performance of the system. Also, the model helps to analyze and identify adaptation strategies to maintain a certain quality of service. The second contribution is a proxy-based adaptation framework for wireless thin-client systems. This framework adapts dynamically the performance of a wireless thin-client using dynamically discovered contexts. The framework offers application adaptability by employing a fuzzy rule-based engine that adapts dynamically to wireless bandwidth variability and client processing power variability. The fuzzy rule-based inference engine uses context information to trade off among the different qualities of service parameters offered to end-users. The adaptation framework uses a highly scalable wavelet-based image coding technique to provide scalability of quality of service that degrades gracefully. This adaptation framework shields the user from the ill effects of high variability of the wireless network quality and the mobile device's resources. This adaptation framework improves the performance of active applications in which the display changes rather frequently. Active applications behavior may result in high transmission latency of screen updates. The transmission latency of a large amount of screen update data adversely affects the user perception of quality of service and results in poor interactivity and slow screen updates.