University of Houston

(m,k)-firm constraints have been used to schedule tasks in soft/firm real-time systems under overloaded conditions. In general, they are provided by application designers to guarantee the minimum levels of quality of service (QoS). Many problems concentrating in task schedulability under these constraints were investigated in the last ten years. However, little work has been done in combining the optimization of the QoS and task schedulability subject to these (m, k)-firm constraints. In this thesis, the problem of maximizing the guaranteed performance while maintaining a schedulable task set in periodic firm real-time systems is considered. To quantify the performance, a granularity-related metric called Granularity of Quality of Service - Reward (GQoS-reward) is proposed. We then show that maximizing the total GQoS-reward is an NP-Hard problem and a heuristic method to solve the problem is studied. In addition to the improvement to the GQoS, positive effects on other main performance metrics for soft/firm real time systems, such as effective processor utilization (EPU), total accumulated reward and instability, are also supported by the simulation results using our optimization strategy.