Time-Bounded Adaptive Quality of Service Management for Cooperative Embedded Real-Time Systems
Luís Nogueira · 2009
As an increasing number of users runs both real-time and non-real-time applications in an embedded system, the issue of how to provide an efficient resource utilisation in dynamic, open, and heterogeneous environments becomes very important. The need arises from the fact that independently developed services can enter and leave the system at any time, without any previous knowledge about their real execution requirements and tasks’ inter-arrival times but, nevertheless, response to events still has to be provided within timing constraints in order to guarantee a desired level of performance. Within this context, this thesis proposes a cooperative QoS-aware framework which allows resource constrained devices to collectively execute services in cooperation with more powerful neighbours. The proposed framework allows devices to collectively execute services in order to meet non-functional requirements that otherwise would not be met by an individual execution. Devices dynamically group themselves into coalitions, establishing initial service configurations which maximise the satisfaction of each users’ QoS preferences associated with the new services and minimise the impact on the global QoS caused by the new services’ arrival. At coalitions’ runtime, the dynamic QoS arbitration among competing services is done under the users’ control, extending each user’s influence not only to a coalition’s formation phase but also to its operation. The traditional QoS optimisation approach, mainly concentrated on finding single optimal or with a fixed sub-optimality bound solutions, is reformulated as a heuristicbased anytime optimisation that can be interrupted at any time and still able to provide a service solution, even when services exhibit unrestricted local and distributed QoS inter-dependencies among their tasks. The proposed anytime approach is able to quickly find a good initial service solution and effectively optimise the rate at which the quality of the determined solution improves at each iteration of the algorithms. Autonomous individual runtime adaptations are coordinated through an one-step decentralised model based on an effective feedback mechanism, able to reduce the