Parallel tasks in the cloud computing environment are vulnerable to resource failure and hence cannot be completed, and dynamically providing cloud resources has low reliability. Aiming at this issue, firstly, we introduce a failure recovery mechanism. Because the failure regularity of resources changes dynamically under the condition of failure recoverability, the twoparameter Weibull distribution is used to describe the local characteristics of resource nodes and the failure regularity of communication links in different time periods. Then, based on the analysis of various interactions between parallel tasks, we propose a resource reliability evaluation model based on variableparameter failure regularity. Finally, the model is incorporated into the particle swarm optimization algorithm to obtain the reliabilityaware and adaptive inertia weight PSO resource scheduling algorithm (RPSO), so that the reliability of the alternative resources is fully considered when calculating the fitness. Simulation results show that when appropriate failure recovery parameters are selected, the proposed RPSO algorithm can increase the reliability of cloud services and only add a small amount of additional failure recovery overhead.