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Mobile computing research is expanding beyond the traditional approach on voice and data delivery to encompass new classes of rich mobile applications such as location based services, mobile social networks, crowd computing and sensory based applications. These classes of mobile applications have quantitative and qualitative criteria of growing importance like efficiency and performance, scalability, privacy and reliability. The next generation of mobile enterprise systems will monitor and analyze the mobile computing ecosystem and adapt their execution environments and resources accordingly. In this work I focus on orchestrating all components of such a complex system to have an optimal mobile cloud computing enterprise which meets users and providers' concerns.
Due to the highly-varying wireless channels over time, frequency, and space domains, statistical QoS provisioning, instead of deterministic QoS guarantees, has become a recognized feature in the next-generation wireless networks. In this dissertation, we study the adaptive wireless resource allocation problems for statistical QoS provisioning, such as guaranteeing the specified delay-bound violation probability, upper-bounding the average loss-rate, optimizing the average goodput/throughput, etc., in several typical types of mobile wireless networks. In the first part of this dissertation, we study the statistical QoS provisioning for mobile multicast through the adaptive resource allocations, where different multicast receivers attempt to receive the common messages from a single base-station sender over broadcast fading channels. Because of the heterogeneous fading across different multicast receivers, both instantaneously and statistically, how to design the efficient adaptive rate control and resource allocation for wireless multicast is a widely cited open problem. We first study the time-sharing based goodput-optimization problem for non-realtime multicast services. Then, to more comprehensively characterize the QoS provisioning problems for mobile multicast with diverse QoS requirements, we further integrate the statistical delay-QoS control techniques -- effective capacity theory, statistical loss-rate control, and information theory to propose a QoS-driven optimization framework. Applying this framework and solving for the corresponding optimization problem, we identify the optimal tradeoff among statistical delay-QoS requirements, sustainable traffic load, and the average loss rate through the adaptive resource allocations and queue management. Furthermore, we study the adaptive resource allocation problems for multi-layer video multicast to satisfy diverse statistical delay and loss QoS requirements over different video layers. In addition, we derive the efficient adaptive erasure-correction coding scheme for the packet-level multicast, where the erasure-correction code is dynamically constructed based on multicast receivers̕ packet-loss statuses, to achieve high error-control efficiency in mobile multicast networks. In the second part of this dissertation, we design the adaptive resource allocation schemes for QoS provisioning in unicast based wireless networks, with emphasis on statistical delay-QoS guarantees. First, we develop the QoS-driven time-slot and power allocation schemes for multi-user downlink transmissions (with independent messages) in cellular networks to maximize the delay-QoS-constrained sum system throughput. Second, we propose the delay-QoS-aware base-station selection schemes in distributed multiple-input-multiple-output systems. Third, we study the queueaware spectrum sensing in cognitive radio networks for statistical delay-QoS provisioning. Analyses and simulations are presented to show the advantages of our proposed schemes and the impact of delay-QoS requirements on adaptive resource allocations in various environments.
Computing systems has undergone a clear shift towards a larger scale. Highly distributed systems are the standard, people and companies have now a pervasive access to a massive and continuous information flow through a wide variety of devices and systems. While high-level protocols attempt to tame the fast evolution of hardware and software technologies, challenges of reliability, flexibility, openness and 24/7 availability lead to crucial needs of dynamic control and adaptation over all large-scale distributed systems. In this context, monitoring application services becomes more and more a transverse key activity. Beyond traditional system administration and load control, new activities such as autonomic management and decision making systems raise the stakes over monitoring requirements. These systems are now organized around Service Level Agreements referring to some Quality of Service (QoS) criteria. With very different systems consuming monitoring data, requirements on these data also vary in terms of lifespan, precision or granularity. This is referred as Quality of Information (QoI), i.e., an expression of the properties required from the monitored QoS. While monitoring systems with different objectives are proposed to tackle some of the identified issues, the contribution of this PhD thesis is ADAMO, a QoS-aware framework for ADAptive MOnitoring. This framework tackles user provided quality of information (QoI)-aware data queries over dynamic data streams and transforms them into probe configuration settings under resource constraints. In a monitoring system, trade-offs are often needed between QoI, which is required by decision making systems, against system resources when too high QoI impairs system performance. Multiple consumers also tend to share data sources with different on QoI requirements. Thus, the proposed framework relies on a constraint-solving approach in order to provide static and dynamic mechanisms with flexible data access for multiple clients with different QoI needs, as well as generation and configuration of QoS and QoI handling components. Besides, the ADAMO framework factors out the common structure and behavior of monitoring systems in component architecture, so that they can be reusable and extensible. It also provides several extension points which can be altered to support new features. Different parts of the architecture are configurable, or can be partly generated from high-level descriptions of the monitoring requirements. The monitoring framework also dynamically adapts itself to resource constraints. This self-adaptation mechanism is built using all mechanisms of the framework itself, therefore illustrating its own capabilities.