Fast and Effective Multiframe-Task Parameter Assignment Via Concave Approximations of Demand

Task parameters in traditional models, e.g., the generalized multiframe (GMF) model, are fixed after task specification time. When tasks whose parameters can be assigned within a range, such as the frame parameters in self-suspending tasks and end-to-end tasks, the optimal offline assignment towards schedulability of such parameters becomes important. The GMF-PA (GMF with parameter adaptation) model proposed in recent work allows frame parameters to be flexibly chosen (offline) in arbitrary-deadline systems. Based on the GMF-PA model, a mixed-integer linear programming (MILP)-based schedulability test was previously given under EDF scheduling for a given assignment of frame parameters in uniprocessor systems. Due to the NP-hardness of the MILP, we present a pseudo-polynomial linear programming (LP)-based heuristic algorithm guided by a concave approximation algorithm to achieve a feasible parameter assignment at a fraction of the time overhead of the MILP-based approach. The concave programming approximation algorithm closely approximates the MILP algorithm, and we prove its speed-up factor is (1+delta)^2 where delta > 0 can be arbitrarily small, with respect to the exact schedulability test of GMF-PA tasks under EDF. Extensive experiments involving self-suspending tasks (an application of the GMF-PA model) reveal that the schedulability ratio is significantly improved compared to other previously proposed polynomial-time approaches in medium and moderately highly loaded systems

On Self-Triggered Full-Information H-infinity Controllers

Abstract. A self-triggered control task is one in which the task determines its next release time. It has been conjectured that self-triggering can relax the requirements on a real-time scheduler while maintaining application (i.e. control system) performance. This paper presents preliminary results supporting that conjecture for a self-triggered real-time system implementing full-information H ∞ controllers. Release times are selected to enforce upper bounds on the induced L2 gain of a linear feedback control system. These release times are treated as requests by the system scheduler, which then assigns actual release times using Buttazzo’s elastic scheduling algorithm. Preliminary experimental results from a Matlab stateflow simulink model demonstrated a remarkable robustness to scheduling delays induced by real-time schedulers. These results show that self-triggered controllers are indeed able to maintain acceptable levels of application performance during prolonged periods of processor overloading.

Temperature-aware scheduling and assignment for hard real-time applications on MPSoCs

Abstract—Thermal effects in MPSoCs may cause the violation of timing constraints in real-time systems. This paper presents a mixed integer linear programming based solution to this problem. Tasks are assigned and scheduled to an MPSoC to minimize peak temperature, subject to real-time constraints. The proposed approach outperforms existing methods, reducing peak temperature by up to 24.66 ◦ C and by an average of 8.75 ◦ C when compared to minimal-energy solutions. We also present a heuristic for use on large problem instances. Steadystate thermal analysis is used for tasks with long execution times compared to the RC thermal time constants of the cores. Transient analysis is used otherwise. The steady-state analysis based heuristic finds solutions with at most 3.40 ◦ C deviation from optimal peak temperature (0.22 ◦ C on average) while improving upon existing technique by as much as 25.71 ◦ C and 10.86 ◦ C o