Mixed-criticality Scheduling with Dynamic Memory Bandwidth Regulation

Mixed-criticality multicore system design must often provide both safety guarantees and high performance. Memory bandwidth regulation among different cores can be a useful tool for providing safety guarantees as it mitigates the interference when accessing main memory. The use of mode changes and system models such as those of Vestal can help provide both safety, for critical functions, and scheduling performance, by efficiently utilising the platform. In this work, we therefore combine per-core memory access regulation with the well established Vestal model and improve on the state-of-the-art in two respects. 1) we allow the memory access budgets of the cores to be dynamically adjusted, when the system undergoes a mode change, reflecting the different needs in each mode, for better schedulability. 2) we devise a memory-regulation-aware and stall-aware schedulability analysis for such systems, based on the well-known AMC-max technique. By comparison, the state-of-the-art did not offer the option of dynamic adjustment of core budgets, and only offered regulation-aware schedulability analysis based on AMC-rtb, which is inherently more pessimistic. As an additional contribution, 3) we consider different task assignment and bandwidth allocation heuristics, in experiments with synthetic task sets, to assess the improvement from using dynamic memory budgets and the new analysis. In our results, we have observed an improvement in schedulability ratio up to 9.1% over the state-of-the-art algorithm.info:eu-repo/semantics/publishedVersio

Decoupling Criticality and Importance in Mixed-Criticality Scheduling

Research on mixed-criticality scheduling has flourished since Vestal’s seminal 2007 paper, but more efforts are needed in order to make these results more suitable for industrial adoption and robust and versatile enough to influence the evolution of future certification standards in keeping up with the times. With this in mind, we introduce a more refined task model, in line with the fundamental principles of Vestal’s mode-based adaptive mixed-criticality model, which allows a task’s criticality and its importance to be specified independently from each other. A task’s importance is the criterion that determines its presence in different system modes. Meanwhile, the task’s criticality (reflected in its Safety Integrity Level (SIL) and defining the rules for its software development process), prescribes the degree of conservativeness for the task’s estimated WCET during schedulability testing. We indicate how such a task model can help resolve some of the perceived weaknesses of the Vestal model, in terms of how it is interpreted, and demonstrate how the existing scheduling tests for the classic variant’s of Vestal’s model can be mapped to the new task model essentially without changes.info:eu-repo/semantics/publishedVersio

Response time analysis of memory-bandwidth- regulated multiframe mixed-criticality systems

The multiframe mixed-criticality task model eliminates the pessimism in many systems where the worst-case execution times (WCETs) of successive jobs vary greatly by design, in a known pattern. Existing feasibility analysis techniques for multiframe mixed-criticality tasks are shared-resource-oblivious, hence un-safe for commercial-o -the-shelf (COTS) multicore platforms with a memory controller shared among all cores. Conversely, the feasibility analyses that account for the interference on shared resource(s) in COTS platforms do not leverage theWCET variation in multiframe tasks. This paper extends the state-of-the-art by presenting analysis that incorporates the memory access stall in memory-bandwidth-regulated multiframe mixed-criticality multicore systems. An exhaustive enumeration approach is proposed for this analysis to further enhance the schedulability success ratio. The running time of the exhaustive analysis is improved by proposing a pruning mechanism that eliminates the combinations of interfering job sequences that subsume others. Experimental evaluation, using synthetic task sets, demonstrates up to 72% improvement in terms of schedulability success ratio, compared to frame-agnostic analysis.This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDP/UIDB/04234/2020); by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by national funds through the FCT, within project PREFECT (POCI01-0145-FEDER-029119); by FCT through the European Social Fund (ESF) and the Regional Operational Programme (ROP) Norte 2020, under grant 2020.08045.BD.info:eu-repo/semantics/publishedVersio

Designing Mixed Criticality Applications on Modern Heterogeneous MPSoC Platforms

Multiprocessor Systems-on-Chip (MPSoC) integrating hard processing cores with programmable logic (PL) are becoming increasingly common. While these platforms have been originally designed for high performance computing applications, their rich feature set can be exploited to efficiently implement mixed criticality domains serving both critical hard real-time tasks, as well as soft real-time tasks. In this paper, we take a deep look at commercially available heterogeneous MPSoCs that incorporate PL and a multicore processor. We show how one can tailor these processors to support a mixed criticality system, where cores are strictly isolated to avoid contention on shared resources such as Last-Level Cache (LLC) and main memory. In order to avoid conflicts in last-level cache, we propose the use of cache coloring, implemented in the Jailhouse hypervisor. In addition, we employ ScratchPad Memory (SPM) inside the PL to support a multi-phase execution model for real-time tasks that avoids conflicts in shared memory. We provide a full-stack, working implementation on a latest-generation MPSoC platform, and show results based on both a set of data intensive tasks, as well as a case study based on an image processing benchmark application