Memory Bandwidth Regulation for Multiframe Task Sets

Timing analysis of safety-critical real-time embedded systems should be free of both optimistic and pessimistic aspects. The multiframe model was devised to eliminate the pessimism in the schedulability analysis of systems with tasks whose worst-case execution times vary from job to job, according to known patterns. However, this model is optimistic and unsafe for multicores with shared memory controllers, since it ignores memory contention, and existing approaches to stall analysis based on memory regulation are very pessimistic if straightforwardly applied. This paper remedies this by adapting existing stall analyses for memory-regulated systems of conventional Liu-and-Layland tasks to the multiframe model. Experimental evaluations with synthetic task sets (and different task and memory budget assignment heuristics) show up to 85% higher scheduling success ratio for our analysis, compared to the frameagnostic analysis, enabling higher platform utilisation without compromising safety. We also explore implementation aspects, such as how to speed up the analysis and how to trade off accuracy with tractability.