Recent studies have shown that reusing standby elements during the mission may improve the mission success probability significantly. However, such a benefit cannot be effectively achieved without a careful design of the replacement and maintenance schedule (RMS), which determines work period durations of operating elements and types of maintenance performed for idle elements. This paper makes contributions by modeling and optimizing the RMS for a heterogeneous dual-unit warm standby system with the aim to minimize the total expected mission cost, covering operation, standby and maintenance costs as well as mission failure penalty cost. The two system elements are dissimilar, characterized by different performance, failure time distribution and cost parameters. For a successful mission, a specified amount of work must be accomplished before both elements become failed or unavailable. We propose a new probabilistic model-based methodology for assessing the mission success probability and expected mission cost (EMC) of the considered system. An optimization problem is further formulated and solved to find the optimal RMS minimizing the EMC. A case study of a two-pump oil transfer system is conducted to demonstrate the proposed model and effects of different cost parameters on the optimal RMS solution and corresponding mission success probability and EMC.

As one of the enabling technologies for cyber-physical systems and Internet of Things systems, the cloud computing provides cost-effective resources in an on-demand manner. This merit lends the cloud to running critical services that need redundancy to achieve high reliability. This paper models a cloud service using the N -version programming (NVP) redundancy technique that creates and runs multiple task solver versions (TSVs) in parallel to perform a requested service and decides the output using the threshold voting. A malicious attacker may get an unauthorized access to a user's data when the user's and attacker's virtual machines co-reside in the same cloud server. To reduce the chance of the co-residence attack success and users’ expense, an individual TSV cancellation policy is implemented, which removes a TSV's virtual machine from its host server immediately once this TSV completes the task execution. A probabilistic method is proposed to evaluate the task reliability and data security under the considered cloud service model. Constrained optimization problems are further formulated and solved, which find the optimal number of TSVs maximizing the task reliability subject to providing a desired level of data security. Examples are presented to demonstrate interactions and impacts of different parameters on the task reliability and data security, as well as on the optimization solutions.

This paper models a dual-unit standby system with non-identical, reusable units that perform the mission task alternatively according to a pre-specified replacement and maintenance schedule (RMS). When one unit is online operating, the other unit undergoes maintenance of a particular type determined by the RMS. In the case of one unit failing and the amount of work completed before the unit failure not exceeding a threshold value, the system aborts the primary mission and immediately activates a rescue procedure to avert or alleviate the risk of system failure and thus any associated damages. Both the RMS and the mission abort threshold parameter adopted can affect the mission success probability (MSP) significantly. This paper makes contributions by formulating and solving a constrained optimization problem that determines the joint RMS and mission abort policy, maximizing the MSP while meeting a constraint on the damage avoidance probability (DAP). A probabilistic method is suggested to evaluate the MSP and DAP of the considered system. Based on the string solution representation designed in this work, the genetic algorithm is then implemented to solve the optimization problem. A case study on a dual-pump liquid transfer system used in the chemical reactor is conducted to demonstrate the proposed model, and optimization solutions balancing MSP and DAP.