Optimizing software rejuvenation policy for tasks with periodic inspections and time limitation

Abstract Software aging has been observed in diverse types of software systems, causing gradual performance degradation with time and/or load and eventually system failures. To mitigate the aging effects and prevent serious losses caused by the system failure, software rejuvenations can be proactively performed to restore the system performance. This paper models and optimizes a state-based rejuvenation policy for software systems performing real-time computing tasks and undergoing periodic inspections. During each scheduled inspection, the system state is evaluated and the decision about the rejuvenation is made based on the evaluated system state and a rejuvenation decision function. The time of each rejuvenation procedure (corresponding to the system downtime) depends on the system state as well as on the amount of task operations accomplished before deciding to perform the rejuvenation. As the rejuvenation policy determines the time and number of rejuvenations performed during the task processing, it can affect the probability that the system can accomplish the real-time task by a certain deadline significantly. In this work, we optimize the state-based rejuvenation policy to maximize the probability of task completion (PTC) of periodically inspected software systems. The methodology encompasses an event transition-based iterative method proposed for quantifying the PTC and application of the Genetic Algorithm for deriving the optimal rejuvenation policy. Examples are presented to demonstrate the proposed methodology and influences of several parameters (e.g., inspection interval, rejuvenation time) on the optimization results.