Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards autonomic orchestration of machine learning pipelines in future
  networks   Machine learning (ML) techniques are being increasingly used in mobile
networks for network planning, operation, management, optimisation and much
more. These techniques are realised using a set of logical nodes known as ML
pipeline. A single network operator might have thousands of such ML pipelines
distributed across its network. These pipelines need to be managed and
orchestrated across network domains. Thus it is essential to have autonomic
multi-domain orchestration of ML pipelines in mobile networks. International
Telecommunications Union (ITU) has provided an architectural framework for
management and orchestration of ML pipelines in future networks. We extend this
framework to enable autonomic orchestration of ML pipelines across multiple
network domains. We present our system architecture and describe its
application using a smart factory use case. Our work allows autonomic
orchestration of multi-domain ML pipelines in a standardised, technology
agnostic, privacy preserving fashion.

29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con