Mobile devices must deal with limited and dynamically varying resources, in particular, the network quality of service (QoS). In addition, wireless devices have other constraints such as limited memory, battery power, and physical dimensions. Applications that execute in such environments need to adapt to the dynamic operating conditions in order to preserve an acceptable level of service as close to 100% of the time as possible. Viewing and downloading digital spatial data on mobile devices has become more popular, especially with the availability of <i>location-aware</i> applications that exploit GPS (Global Positioning System) receivers integrated in many of today's mobile devices. Map client applications face many challenges in accessing data across a wireless network. Vector spatial data files tend to be large, and file sizes tend to increase unpredictably depending on the complexity of feature geometry. Due to the limited size of the mobile device display, viewing all the details of the map could cause unreasonable clutter and render the map useless. Even if it is feasible to transmit all the details from a QoS standpoint, this could pose a problem from a usability standpoint. This research effort aims to tackle the issues of QoS and usability on mobile devices through a client-proxy-server model where clients are on mobile devices. The proxy performs two functions. First, it supplies the client with vital data about the status of the system that allows the client to take adaptive decisions aimed at maintaining the QoS. Second, it performs the adaptive actions requested by the client. There are two types of adaptive actions performed by the proxy, activating and deactivating filters. When filters are activated, the amount of data transmitted from the server to the client is reduced. The client may decide to activate one or more filters either to maintain QoS or to limit clutter on the screen and enhance usability. The map client-server application and the proxy were developed in Java (tm), and a number of experiments and real-life scenarios were designed to determine the effectiveness and feasibility of the proposed adaptation model and to evaluate the performance of the proxy.

Mobile devices must deal with limited and dynamically varying resources, in particular, the network quality of service (QoS). In addition, wireless devices have other constraints such as limited memory, battery power, and physical dimensions. Applications that execute in such environments need to adapt to the dynamic operating conditions in order to preserve an acceptable level of service as close to 100% of the time as possible. Viewing and downloading digital spatial data on mobile devices has become more popular, especially with the availability of <i>location-aware</i> applications that exploit GPS (Global Positioning System) receivers integrated in many of today's mobile devices. Map client applications face many challenges in accessing data across a wireless network. Vector spatial data files tend to be large, and file sizes tend to increase unpredictably depending on the complexity of feature geometry. Due to the limited size of the mobile device display, viewing all the details of the map could cause unreasonable clutter and render the map useless. Even if it is feasible to transmit all the details from a QoS standpoint, this could pose a problem from a usability standpoint. This research effort aims to tackle the issues of QoS and usability on mobile devices through a client-proxy-server model where clients are on mobile devices. The proxy performs two functions. First, it supplies the client with vital data about the status of the system that allows the client to take adaptive decisions aimed at maintaining the QoS. Second, it performs the adaptive actions requested by the client. There are two types of adaptive actions performed by the proxy, activating and deactivating filters. When filters are activated, the amount of data transmitted from the server to the client is reduced. The client may decide to activate one or more filters either to maintain QoS or to limit clutter on the screen and enhance usability. The map client-server application and the proxy were developed in Java (tm), and a number of experiments and real-life scenarios were designed to determine the effectiveness and feasibility of the proposed adaptation model and to evaluate the performance of the proxy.

Adaptive parallel applications that can change resources during execution, promise increased application performance and better system utilization. Furthermore, they open the opportunity for developing a new class of parallel applications driven by unpredictable data and events. The research issues in an adaptive parallel system are complex and interrelated. The nature and complexities of the relationships among these issues are not well researched and understood. Before developing adaptive applications or an infrastructure support for adaptive applications, these issues need to be investigated and studied in detail. One way of understanding and investigating these issues is by modeling and simulation. A model for adaptive parallel systems has been developed to enable the investigation of the impact of malleable workloads on its performance. The model can be used to determine how different model parameters impact the performance of the system and to determine the relationships among them Subsequently, a discrete event simulator has been developed to numerically simulate the model. Using the simulator, the impact of the variation in the number of malleable jobs in the workload, the flexibility, the negotiation cost, and the adaptation cost on system performance have been studied. The results and conclusions of these simulation experiments are presented in this dissertation. In general, the simulation results reveal that the performance improves with an increase in the number of malleable jobs in a workload, and that the performance saturates at a certain percentage of rigid to malleable jobs mix. A high percentage of malleable jobs is not necessary to achieve significant improvement in performance. The performance in general improves as the flexibility increases up to a certain point; then, it saturates. The negotiation cost impacts the performance, but not significantly. The number of negotiations for a given workload increases as number of malleable jobs increases up to a certain point, and then it decreases as number of malleable jobs increases further. The performance degrades as the application adaptation cost increases. The impact of the application adaptation cost on performance is much more significant compared to that of the negotiation cost.