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Partly Proportionate fair (Partly-Pfair) scheduling, which allows task migration at runtime and assigns each task processing time with regard to its weight, makes it possible to build highly efficient embedded multi-core systems. Due to its non-work-conserving behavior, which might leave the CPU idle even when tasks are ready to execute, tasks finish only shortly before their deadlines are reached. Benefits are lower task jitter, but additional workload, e.g. through interrupts, can lead to deadline violations. In this paper we present a work-conserving extension of Partly-Pfair scheduling, called PERfair scheduling and the algorithm P-ERfair-PD2 which applies Pfair modifications used for Partly-Pfair on the concept of ERfairness and PD2 policies. With a simulation based schedulability examination we show for multiple time base (MTB) task sets that P-ERfair- PD2 has the same performance as Partly-Pfair-PD2. Additionally, we show that P-ERfair- PD2 has a much higher robustness against perturbations, and therefore it is well suited for embedded domains, especially for the Automotive domain.
Eingebettete Systeme unterliegen neben den funktionalen Anforderungen besonders nichtfunktionalen Qualitätsanforderungen wie Effizienz, Zuverlässigkeit und Echtzeitfähigkeit. Mit steigendem Bedarf an Rechenkapazität können bisherige Konzepte zur Leistungssteigerung von Singlecore-Systemen jedoch nicht mehr eingesetzt werden - der Umstieg auf Multicore-Systeme wird erforderlich. Im zweiten Teil dieser Arbeit wird ein simulationsbasierter Ansatz zum Vergleich von Multicore-Scheduling-Algorithmen vorgestellt, mit dem Algorithmen für Multicore-Systeme mit voller Migration und dynamischer Task-Priorität untersucht werden. Wir erweitern diesen Ansatz um ein Verfahren zur Untersuchung einer Tasksetmenge mit stochastisch beschriebenen Eigenschaften und vergleichen ihn mit den im Teil 1 beschriebenen Algorithmen BinPacking-EDF und P-ERfair-PD² für eine Gruppe von Automotive Powertrain Systemen.
Proportionate fair (Pfair) scheduling, which allows task migration at runtime and assigns each task processing time with regard to its weight, is one of the most efficient group of SMP multiprocessor scheduling algorithms known up to now. Drawbacks are tight requirements to the task system, namely the restriction to periodic task systems with synchronized task activation, quantized task execution time, and implicit task deadline. Most likely, a typical embedded real-time system does not fulfill these requirements. In this paper we address violations of these requirements. For heterogeneous task systems, we define the multiple time base (MTB) task system, which is a less pessimistic model than sporadic task systems and is used for automotive systems. We apply the concept of Pfair scheduling to MTB task systems, called partly proportionate fair (Partly-Pfair) scheduling. The restrictions on MTB task systems required for Partly-Pfair ness are weaker than restrictions on periodic task systems required for Pfair ness. In a simulation based study we examined the performance of Partly-Pfair-PD and found it capable to schedule feasible MTB task sets causing a load of up to 100% of the system capacity.
Partitionierungs-Scheduling von Automotive Restricted Tasksystemen auf Multiprozessorplattformen
(2009)
Eingebettete Systeme unterliegen neben den funktionalen Anforderungen besonders nicht funktionalen Qualitätsanforderungen wie Effizienz, Zuverlässigkeit und Echtzeitfähigkeit. Mit steigendem Bedarf an Rechenkapazität können bisherige Konzepte zur Leistungssteigerung von Singlecore-Systemen jedoch nicht mehr eingesetzt werden – der Umstieg auf Multicore-Systeme wird erforderlich. Im ersten Teil dieser Arbeit werden eine mögliche Prozessorarchitektur für zukünftige Automotive Multicore-Systeme und die Abstraktion der Software für diese Systeme vorgestellt. Nach einer Klassifkation von Multicore-Scheduling-Algorithmen präsentieren wir exemplarisch einen Algorithmus mit statischer Taskallokation und einen Algorithmus mit dynamischer Taskallokation. Bei beiden Algorithmen handelt es sich um eine Überführung theoretisch behandelter Algorithmen auf Automotive Systeme.
Towards the Derivation of Guidelines for the Deployment of Real-Time Tasks on a Multicore Processor
(2014)
The deployment of automotive software on a multicore processor includes the task of mapping executables to cores. Given the number of possible solutions, integrators have to solve a complex problem. Considering multiple, often conflicting goals like minimizing task response times and memory consumption, complexity further increased with the advent of multicore processors. We present a model-based approach for deriving design rules supporting integrators with statically mapping tasks to a multicore ECU. First, an evolutionary algorithm is used to sample the design space. For each sample, a model-based analysis is performed, resulting in the required fitness values according to the system metric objectives. Finally, subsets of the sample population are used to derive deployment guidelines by evaluating similarities between highly ranked solutions. This reduces the number of solutions to be considered by the integrators by orders of magnitude. In a case-study, we demonstrate the developed approach on an artificial automotive engine management system.