Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Machine Learning-Based Migration Strategy for Virtual Network Function
  Instances   With the growing demand for data connectivity, network service providers are
faced with the task of reducing their capital and operational expenses while
simultaneously improving network performance and addressing the increased
demand. Although Network Function Virtualization (NFV) has been identified as a
promising solution, several challenges must be addressed to ensure its
feasibility. In this paper, we address the Virtual Network Function (VNF)
migration problem by developing the VNF Neural Network for Instance Migration
(VNNIM), a migration strategy for VNF instances. The performance of VNNIM is
further improved through the optimization of the learning rate hyperparameter
through particle swarm optimization. Results show that the VNNIM is very
effective in predicting the post-migration server exhibiting a binary accuracy
of 99.07% and a delay difference distribution that is centered around a mean of
zero when compared to the optimization model. The greatest advantage of VNNIM,
however, is its run-time efficiency highlighted through a run-time analysis.

7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph