A paper “Memory Bandwidth Regulation for Multiframe Task Sets” has been accepted at RTCSA 2018. This paper aims to reduce cost of real-time systems where the worst-case execution times of tasks vary from job to job, according to known patterns. This kind of execution behavior can be captured by the multi-frame task model. However, this model is optimistic and unsafe for multi-cores with shared memory controllers, since it ignores memory contention, and existing approaches to stall analysis based on memory regulation are very pessimistic if straight-forwardly applied.
This paper remedies this by adapting existing stall analyses for memory-regulated systems to the multi-frame model. Experimental evaluations with synthetic task sets show up to 85% higher scheduling success ratio for our analysis, compared to the frame-agnostic analysis, enabling higher platform utilization without compromising safety. We also explore implementation aspects, such as how to speed up the analysis and how to trade off accuracy with tractability.