Service function chaining (SFC) enables network operators to flexibly provide diverse services such as Internet-of-Things (IoT) and mobile applications. SFC technologies are required to offer stable and guaranteed quality-of-service (QoS) even in the circumstances of dynamically-changing resource demands and traffic volumes. To meet QoS requirements against time-varying network environment, infrastructure providers need to dynamically adjust the amount of computational resources such as CPU assigned to virtual network functions (VNFs) in each service function chain. If the increased resource demand of a VNF cannot be satisfied from the available resources in the current node, the VNF should be migrated to another node. Related work has been tackling SFC embedding and scheduling issues by applying various techniques such as game theory, integer linear programming and heuristic methods. However, they have limitations that they cannot provide an agile operation of VNF migration as they require a large number of iterations. In order to overcome the limitations, we propose to utilize an encoder-decoder recurrent neural network and train it to solve the VNF migration scheduling problem. Through the simulation, we verify that the proposed method can agilely determine VNF redeployment locations by keeping the frequency of server overload and VNF migration minimum.