The 27th International Conference on Real-Time Networks and Systems (RTNS) in Toulouse, France is over. Our paper “Response Time Analysis of Multiframe Mixed-Criticality Systems” received not one, but two awards! Before the conference, we were notified that it had received an Outstanding Paper Award, as listed in the conference program. During the conference, we also learned that it received a Best Student Paper Award. I would like to take this opportunity to congratulate Ishfaq Hussain, PhD student at CISTER and first author of the paper. This seems like a good start of a distinguished research career.
The paper “Response Time Analysis of Multiframe Mixed-Criticality Systems” has been accepted at RTNS 2019. This work is the next in our mixed-criticality research line, in collaboration with my former colleagues at CISTER. It continues our work on the multi-frame task model, also considered in our RTCSA paper this year. The multi-frame model describes tasks that have different worst-case execution times for each job, following a known pattern, which can be exploited to reduce the cost of the system. Existing schedulability analyses fail to leverage this characteristic, potentially resulting in pessimism and increased system cost.
In this paper, we present a schedulability analysis for the multi-frame mixed-criticality model. Our work extends both the analysis techniques for Static Mixed-Cricality scheduling (SMC) and Adaptive Mixed-Criticality scheduling (AMC), on one hand, and the schedulability analysis for multi-frame task systems on the other. Our proposed worst-case response time (WCRT) analysis for multi-frame mixed-criticality systems is considerably less pessimistic than applying the SMC, AMC-rtb and AMC-max tests obliviously to the WCET variation patterns. Experimental evaluation with synthetic task sets demonstrates up to 63.8% higher scheduling success ratio compared to the best of the frame-oblivious tests.
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.