Machine Learning-assisted Planning and Provisioning for SDN/NFV-enabled Metropolitan Networks

After more than ten years of research and development, Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are finally going mainstream. The fifth generation telecommunication standard (5G) will make use of novel technologies to create increasingly intelligent and autonomous networks. The METRO-HAUL project proposes an advanced SDN/NFV metro-area infrastructure based on an optical backbone interconnecting edge-computing nodes, to support 5G and advanced services. In this work, we present the METRO-HAUL planning tool subsystem that aims to optimize network resources from two different perspectives: off-line network design and on-line resource allocation. Off-line network design algorithms are mainly devoted to capacity planning. Once network infrastructure is in production stages and operational, on-line resource allocation takes into account flows generated by end-user-oriented services that have different requirements in terms of bandwidth, delay, QoS and set of VNFs to be traversed. Through the paper, we describe the components inside the planning tool, which compose a framework that enables intelligent optimization algorithms based on Machine Learning (ML) to assist the control plane in taking strategic decisions. The proposed framework aims to guarantee a fair behavior towards past, current and future requests as network resource allocation
decisions are assisted with ML approaches. Additionally, interaction schemes are proposed between the open-source
JAVA-based Net2Plan tool, ML libraries and algorithms in Python easing algorithm development and prototyping for rapid
interaction with SDN/NFV control and orchestration modules.