Nowadays, current and future developments of highly automated vehicles face the challenge of designing failoperational systems without violating economic-driven cost restrictions. Thereby self-adapting failure management processes at runtime are considered to keep reasonable costs while achieving a sufficient level of reliability, availability, and safety. The aim is to regain the system's safe and available status as fast as possible after a failure occurs by implementing detection, isolation, switching, and reconfiguration measures. These self-organized measures introduce additional fallback levels by optimizing the usage of the system's resources. Thus, it significantly reduces the vehicle's costs compared to, e.g., highly redundant systems. However, implementing and executing this process is tedious, especially regarding reconfiguring the vehicle's application to the system resources, e.g., computing nodes. Depending on the vehicle's current status, the solution space may contain many possible system configurations to regain the availability and safety of the vehicle. As a result, various characteristics must be considered to determine the best-suited system configuration. Furthermore, a valid solution must be found under a given time constraint. Therefore, this paper defines a system model and suggests a mapping problem for the reconfiguration procedure to maintain the availability and safety of the vehicle. Furthermore, a process to determine the optimal solution within a given timeframe is suggested to determine a Pareto front of the most suitable system configurations, which maintain the system's safe operation.