In the recent years, the Internet of Things (IoT) services has been increasingly applied to promote the quality of the human life and this trend is predicted to stretch for into future. With the recent advancements in IoT technology, fog computing is emerging as a distributed computing model to support IoT functionality. Since the IoT services will experience workload fluctuations over time, it is important to automatically provide the proper number of sufficient fog resources to address the workload changes of IoT services to avoid the over- or under-provisioning problems, meeting the QoS requirements at the same time. In this paper, an efficient resource provisioning approach is presented. This approach is inspired by autonomic computing model using Bayesian learning technique to make decisions about the increase and decrease in the dynamic scaling fog resources to accommodate the workload from IoT services in the fog computing environment. Also, we design an autonomous resource provisioning framework based on the generic fog environment three-tier architecture. Finally, we validate the effectiveness of our solution under three workload traces. The simulation results indicate that the proposed solution reduces the total cost and delay violation, and increases the fog node utilization compared with the other methods.