SEAMS 2024
Mon 15 - Tue 16 April 2024 Lisbon, Portugal
co-located with ICSE 2024

Cyber-physical systems (CPS) are subject to environmental uncertainties such as adverse operating conditions, malicious attacks, and hardware degradation. These uncertainties may lead to failures that put the system in a sub-optimal (e.g., prolonged response times from a server) or unsafe state (e.g., a self-driving vehicle breaching the lane boundary). Systems that are resilient to such uncertainties rely on two types of operations: (1) \emph{graceful degradation}, for ensuring that the system maintains an acceptable level of safety during unexpected environmental conditions and (2) \emph{recovery}, to facilitate the resumption of normal system functions. Typically, mechanisms for degradation and recovery are developed independently from each other, and later integrated into a system, requiring the designer to develop an additional, ad-hoc logic for activating and coordinating between the two operations.

In this paper, we propose a self-adaptation approach for improving system resiliency through automated triggering and coordination of graceful degradation and recovery. The key idea behind our approach is to treat degradation and recovery as \emph{requirement-driven} adaptation tasks: Degradation can be thought of as temporarily \emph{weakening} an original (i.e., ideal) system requirement to be achieved by the system, and recovery as \emph{strengthening} the weakened requirement when the environment returns within an expected operating boundary. Furthermore, by treating weakening and strengthening as dual operations, we argue that a single requirement-based adaptation method is sufficient to enable coordination between degradation and recovery. Given system requirements specified in \emph{signal temporal logic (STL)}, we propose a run-time adaptation framework that automatically performs degradation and recovery in response to environmental changes. We describe a prototype implementation of our framework and demonstrate the feasibility of the proposed approach using a case study in unmanned underwater vehicles (UUVs).

Tue 16 Apr

Displayed time zone: Lisbon change

11:00 - 12:30
Session 6: Self-Recovery & Evaluation Research Track / Artifact Track at Luis de Freitas Branco
Chair(s): Dalal Alrajeh Imperial College London
Raft Protocol for Fault Tolerance and Self-Recovery in Federated LearningFULL
Research Track
Rustem Dautov SINTEF, Erik Johannes Husom SINTEF Digital
Integrating Graceful Degradation and Recovery through Requirement-driven AdaptationFULL
Research Track
Simon Chu Carnegie Mellon University, Justin Koe The Cooper Union, David Garlan Carnegie Mellon University, Eunsuk Kang Carnegie Mellon University
Learning Recovery Strategies for Dynamic Self-healing in Reactive SystemsFULL
Research Track
Mateo Sanabria Universidad de los Andes, Ivana Dusparic Trinity College Dublin, Ireland, Nicolás Cardozo Universidad de los Andes
SWITCH: An Exemplar for Evaluating Self-Adaptive ML-Enabled SystemsARTIFACT
Artifact Track
Arya Marda IIIT Hyderabad, Shubham Kulkarni IIIT Hyderabad, Karthik Vaidhyanathan IIIT Hyderabad
Pre-print Media Attached