The continuous growing and the heterogeneity of the Internet of Things devices is an increasing concern in Fog Computing, where the nodes tend to stay overloaded, what compromises the responses times. In response to this challenge, we propose an Architecture Model for Fog Computing and a new Priority Load Balancer that aims to increasing the fog nodes efficiency. Our research combines tasks information and dynamics computational load in order to reduce the response time of the Fog Computing. Results show that the proposed solution have the best response time when compared to scenario with direct and round-robin strategies. Experiments with three Fog Nodes types using three different Task sizes and three different quantities of requests for sensors show that our proposed load balancer was able to reduce the response time of high priority tasks by more than 56% compared to others balancers.