Abstract
Service function chaining (SFC) provides the plat-form for flexible resource management by dynamically allocating resources to virtual and/or container network functions (VNFs/CNFs). To meet the quality of service (QoS) requirements while facing increasing resource demands, the sys-tem will require the migration of the VNFs/CNFs from the current server to the others that offer sufficient resources. In this study, we formulate an integer linear programming (ILP) based optimization model to solve the function migration scheduling problem so that it meets QoS requirements of each service function (SF) chain. The remarkable points of this work are the following two points. The one is that we consider latency between VNFs/CNFs belonging to an SF chain, avoiding overhead due to their unnecessary migration and resource shortage. And the other is that we consider the case in which each VNF/CNF must be to be deployed strictly to a designated virtual machine (or container). To reduce complexity, we apply an encoder-decoder recurrent neural network (ED-RNN) as a machine learning model to the function migration scheduling problem. Performance evaluations show that the ED-RNN based approach achieves a similar performance as the ILP, while adding the benefits of very low complexity.
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