  Migration and replication of virtual network functions (VNFs) are well-known
mechanisms to face dynamic resource requests in Internet Service Provider (ISP)
edge networks. They are not only used to reallocate resources in carrier
networks, but in case of excessive traffic churns also to offloading VNFs to
third party cloud providers. We propose to study how traffic forecasting can
help to reduce the number of required migrations and replications when the
traffic dynamically changes in the network. We analyze and compare three
scenarios for the VNF migrations and replications based on: (i) the current
observed traffic demands only, (ii) specific maximum traffic demand value
observed in the past, or (iii) predictive traffic values. For the prediction of
traffic demand values, we use an LSTM model which is proven to be one of the
most accurate methods in time series forecasting problems. Based the traffic
prediction model, we then use a Mixed-Integer Linear Programming (MILP) model
as well as a greedy algorithm to solve this optimization problem that considers
migrations and replications of VNFs. The results show that LSTM-based traffic
prediction can reduce the number of migrations up to 45\% when there is enough
available resources to allocate replicas, while less cloud-based offloading is
required compared to overprovisioning.
