A distributed formal-based model for self-healing behaviors in autonomous systems: from failure detection to self-recovery

The challenges of current software-intensive systems, large-scale information and computing systems environments, which are highly dynamic, heterogeneous, and unpredictable, have motivated the development of techniques that enhance these systems with autonomous behaviors. Even though different concerns about these systems have been deeply studied, their design is still considerably more challenging than traditional ones. Self-healing is one of the main features that characterize autonomic computing systems. Failure detection, recovery strategies, and reliability are of paramount importance to ensure continuous operation and correct functioning even in the presence of a given maximum amount of faulty components. Most existing research and implementations focus on architecture-specific solutions to introduce self-healing behaviors. This implies that users must tailor their software by taking into account architecture-specific fault tolerance features, which requires too much effort from developers and users. This paper proposes a distributed formal model for the specification, verification, and analysis of self-healing behaviors in autonomous systems, from failure-detection to self-recovery. Such a high-level model allows users to specify and apply the desired type of failure detection and recovery without requiring any knowledge about its implementation. Our model allows not only formal verification of different properties but also performance evaluation. We provide the verification of qualitative properties using state-space exploration tools, and quantitative properties are also validated through statistical model-checking. All these properties are preserved in actual implementation by ensuring that the deployed code is consistent with the validated model.