Abstract
This paper models repairable computing systems performing a mission that is successful if the system can accomplish a specified amount of work within the allowed mission time or deadline. During the mission the system is subject to a sequence of full and incremental data backup procedures to facilitate an effective system recovery and avoid repeating the entire mission work from the very beginning when a system failure happens. The repair time is fixed while the system time-to-failure can follow any arbitrary type of distributions. This paper makes novel contributions by first developing a new numerical algorithm to evaluate mission success probability and expected completion time of the considered repairable real-time computing systems subject to mixed full and incremental backups. Correctness of the proposed evaluation algorithm is verified using Monte Carlo simulations. We make another new contribution by formulating and solving the backup schedule optimization problem that finds the full and incremental backup frequencies maximizing the mission success probability. Through illustrative examples, effects of different parameters (including the system time-to-failure distribution parameter, maximum allowed mission time, data backup and retrieval times, storage availability, repair time and efficiency) on the mission success probability and expected completion time as well as on the optimal backup schedule solution are investigated.