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The architecture of ARINC-653 partitioned scheduling has been widely applied to avionics systems owing to its robust temporal isolation among applications. However, this partitioning mechanism causes the problem of how to optimize the partition scheduling of a complex system while guaranteeing its schedulability. In this paper, a model-based optimization approach is proposed. We formulate the problem as a parameter sweep application, which searches for the optimal partition scheduling parameters with respect to minimum processor occupancy via an evolutionary algorithm. An ARINC-653 partitioned scheduling system is modeled as a set of timed automata in the model checker UPPAAL. The optimizer tentatively assigns parameter settings to the models and subsequently invokes UPPAAL to verify schedulability as well as evaluate promising solutions. The parameter space is explored with an evolutionary algorithm that combines refined genetic operators and the self-adaptation of evolution strategies. The experimental results show the applicability of our optimization method. The architecture of ARINC-653 partitioned scheduling has been widely applied to avionics systems owing to its robust temporal isolation among applications. However, this partitioning mechanism causes the problem of how to optimize the partition scheduling of a complex system while guaranteeing its schedulability. In this paper, a model-based optimization approach is proposed. We formulate the problem as a parameter sweep application, which searches for the optimal partition scheduling parameters with respect to minimum processor occupancy via an evolutionary algorithm. An ARINC-653 partitioned scheduling system is modeled as a set of timed automata in the model checker UPPAAL. The optimizer tentatively assigns parameter settings to the models and subsequently invokes UPPAAL to verify schedulability as well as evaluate promising solutions. The parameter space is explored with an evolutionary algorithm that combines refined genetic operators and the self-adaptation of evolution strategies. The experimental results show the applicability of our optimization method.

A parameter generation method based on model checking is proposed to tackle the parameter selection of hierarchical scheduling systems in Integrated Modular Avionics (IMA) by combining the classical symbolic model checking and the Statistical Model Checking (SMC). It builds a generic timed automata network to describe the temporal behavior of hierarchical systems. A distributed genetic algorithm is adopted to search the optimum partition parameters with respect to processor utilization while guaranteeing the schedulability of the system, which is formulated as safety properties of symbolic model checking and hypothesis testing of SMC. Comparing with the widely-used response time analysis, the formal model of this method is more expressive to cover complex features. The application of SMC alleviates the "state space explosion" of classical model checking. Finally, the parameter generation experiments show that the present method is able to find the global optimum solutions in the parameter space.

The architecture of Integrated Modular Avionics (IMA) provides airborne software with a robust temporal partitioning mechanism, which achieves the reliable fault containment between avionics applications. However, the partition scheduling of an IMA system is a complex nonlinear non-convex optimization problem, making it difficult to solve the optimal temporal allocation for partitions using traditional analytical methods. This paper presents a model-based approach to optimizing the partition scheduling of IMA systems, whose temporal behavior is modeled as a network of timed automata. Given a system model, the optimizer employs a parallel genetic algorithm to search for the optimal partition resource parameters with respect to minimum processor occupancy. For each promising parameter combination, the schedulability constraints and processor occupancy of the system are precisely evaluated by Classical and Statistical Model Checking (i.e., CMC and SMC), respectively. We also apply SMC hypothesis testing to the fast falsification of non-schedulable solutions, thereby speeding up the schedulability verification based on CMC. Two case studies demonstrate that our proposed approach outperforms classical analytical methods on the processor occupancy of typical IMA systems.

This paper presents a modeling framework for schedulability analysis of distributed integrated modular avionics (DIMA) systems that consist of spatially distributed ARINC-653 modules connected by a unified AFDX network. We model a DIMA system as a set of stopwatch automata (SWA) in UPPAAL to analyze its schedulability by classical model checking (MC) and statistical model checking (SMC). The framework has been designed to enable three types of analysis: global SMC, global MC, and compositional MC. This allows an effective methodology including (1) quick schedulability falsification using global SMC analysis, (2) direct schedulability proofs using global MC analysis in simple cases, and (3) strict schedulability proofs using compositional MC analysis for larger state space. The framework is applied to the analysis of a concrete DIMA system., Comment: In Proceedings MARS/VPT 2018, arXiv:1803.08668

This paper presents an approach for schedulability analysis of Distributed Integrated Modular Avionics (DIMA) systems that consist of spatially distributed ARINC-653 multicore modules connected by a unified Avionics Full-Duplex Switched Ethernet (AFDX) network. A multicore DIMA system is modeled as a set of stopwatch automata in uppaal to verify its schedulability by model checking. However, direct verification is infeasible due to the large state space. Therefore, global analysis based on statistical model checking (SMC) and compositional analysis based on classical model checking are combined, thereby mitigating the state space explosion problem. Even though the nature of SMC testing cannot prove schedulability, the model of a DIMA system first undergoes quick schedulability falsification using global SMC analysis. Thereafter, a compositional approach is used to check each partition, including its communication environment individually. By using assume-guarantee reasoning, it is ensured that each real-time task meets the deadline and that communication constraints are also fulfilled globally. The approach is finally applied to the schedulability analysis of a concrete multicore DIMA system.