This work deals with Network Slicing-based 5G Networks to support the
varying demands of customers and for efficient resource utilization. A
network slice can be defined as a set of network and virtual network
function (VNF) resources deployed across multiple administrative
domains. Here, multi-domain refers to multiple infrastructure providers
spread across different geographic regions. Slice demands and QoS
requirements may vary dynamically, which can be satisfied by scaling the
allotted VNF resources. The VNF scaling problem can be posed as a
time-series forecasting problem that predicts future VNF resources based
on the slice traffic demand. 5G deployments with multiple domains pose
a serious challenge in terms of data privacy since one domain may need
access to the data of another domain for efficient resource allocation
using the conventional forecasting approaches that require data
aggregation.

In this work, we use the federated learning approach in which the
training data remains within the respective domains but learns a shared
model by aggregating locally-computed updates. We evaluate the
applicability of federated settings in VNF scaling using two
state-of-the-art deep learning models, Long Short-Term Memory (LSTM) and
Gated Recurrent Units (GRUs). We present a comparison of the
performance of the proposed federated system against the centralized
system. Additionally, synthetic data in each domain has been generated
using Generative Adversarial Networks (GANs) to improve the forecasting
results.

Next, we wrote a discrete event simulator using a Python-based discrete
simulator SimPy to study the performance of our auto-scaling
system. Using the simulator, we conducted experiments to compare the
performance of scaling and non-scaling systems across various
workloads. We also compare our proactive approach's effectiveness
against the reactive VNF scaling.