Uncertainty handling in goal-driven self-optimization - Limiting the negative effect on adaptation
Journal of Systems and Software
| Hi-index | 0.00 |
Socio-technical systems consist of human, hardware and software components that work in tandem to fulfill stakeholder requirements. By their very nature, such systems operate under uncertainty as components fail, humans act in unpredictable ways, and the environment of the system changes. Self-repair refers to the ability of such systems to restore fulfillment of their requirements by relying on monitoring, reasoning, and diagnosing on the current state of individual requirements. Self-repair is complicated by the multi-agent nature of socio-technical systems, which demands that requirements monitoring and self-repair be done in a decentralized fashion. In this paper, we propose a stateful requirements monitoring approach by maintaining an instance of a state machine for each requirement, represented as a goal, with runtime monitoring and compensation capabilities. By managing the interactions between the state machines, our approach supports hierarchical goal reasoning in both upward and downward directions. We have implemented a customizable Java framework that supports experimentation by simulating a socio-technical system. Results from our experiments suggest effective and precise support for a wide range of self-repairing decisions in a socio-technical setting.