When degradation occurs in a dispersed computing network, mitigating the persistent effects of failures and improving the disposal efficiency is an open problem. However, the occurrence of failed nodes in a dispersed computing network is a random and low-probability incident. Further, the resilient resources for emergency allocation are from devices with complex spatial locations in practice. These factors pose challenges to the resource allocation problem using resilience mechanisms. In this article, we explore and analyze a resilience mechanism for dispersed computing networks as an optimization model. We investigate a resilience-aware dynamic resource allocation model to cope with a degraded dispersed computing network and obtain better emergency response at a lower cost. The uncertainties of node failures are uniquely explored to capture failure nodes more precisely and initiate the resilience mechanism for such nodes. In addition, we propose a novel approach to deal with the dynamic and complex coupling characteristics of decision variables in the model. This approach incorporates the induced artificial fish swarm algorithm with dynamic system simulation to generate and improve the scheme for the allocation of resilient resources. Finally, numerical simulation results verify the improved performance of our model and the effectiveness of the algorithm.