@InProceedings{Chu:SEAMS:2024,
AUTHOR = {Chu, Simon and Koe, Justin and Garlan, David and Kang, Eunsuk},
TITLE = {Integrating Graceful Degradation and Recovery through Requirement-driven Adaptation},
YEAR = {2024},
MONTH = {15-16 April},
BOOKTITLE = {Proc. the International Conference on Software Engineering for Adaptive and Self-managing Systems (SEAMS)},
ABSTRACT = {Cyber-physical systems (CPS) are subject to environmental uncer- tainties such as adverse operating conditions, malicious attacks, and hardware degradation. These uncertainties may lead to failures that put the system in a sub-optimal or unsafe state. Systems that are resilient to such uncertainties rely on two types of operations: (1) graceful degradation, for ensuring that the system maintains an acceptable level of safety during unexpected environmental condi- tions and (2) 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 improv- ing system resiliency through automated triggering and coordina- tion of graceful degradation and recovery. The key idea behind our approach is to treat degradation and recovery as requirement-driven adaptation tasks: Degradation can be thought of as temporarily weakening original (i.e., ideal) system requirements to be achieved by the system, and recovery as strengthening the weakened require- ments when the environment returns within an expected operating boundary. Furthermore, by treating weakening and strengthen- ing 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 signal temporal logic (STL), we propose a run-time adaptation framework that 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.},
KEYWORDS = {Self-adaptation}
}
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