Rapid Recovery for Systems with Scarce Faults

TL;DR

This paper introduces the concept of k-resilience for safety-critical systems, enabling rapid recovery from a limited number of faults, and demonstrates that constructing optimal control strategies is computationally feasible.

Contribution

It proposes a new control objective called k-resilience, balancing complexity and precision, with low computational complexity for optimal control synthesis.

Findings

k-resilience effectively models fault recovery in safety-critical systems.

Constructing optimal control strategies for k-resilience is computationally efficient.

Experimental results demonstrate the feasibility of the approach.

Abstract

Our goal is to achieve a high degree of fault tolerance through the control of a safety critical systems. This reduces to solving a game between a malicious environment that injects failures and a controller who tries to establish a correct behavior. We suggest a new control objective for such systems that offers a better balance between complexity and precision: we seek systems that are k-resilient. In order to be k-resilient, a system needs to be able to rapidly recover from a small number, up to k, of local faults infinitely many times, provided that blocks of up to k faults are separated by short recovery periods in which no fault occurs. k-resilience is a simple but powerful abstraction from the precise distribution of local faults, but much more refined than the traditional objective to maximize the number of local faults. We argue why we believe this to be the right level of abstraction for safety critical systems when local faults are few and far between. We show that the computational complexity of constructing optimal control with respect to resilience is low and demonstrate the feasibility through an implementation and experimental results.