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

Abstract

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, Quality Of Service (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.

Machine-Learning Defined Networking: Towards Intelligent Networks

“…The detailed results can be found in Chap. 7 of the Ph.D. thesis and in the following papers: [19][20][21][22] 2) The second is a field deployment of such solution in a real network located in an Italian city. These testbeds target the monitoring and the traffic engineering features based on RL of an SD-WAN solution and explores different approaches to understand their advantages and limitations.…”

QR-SDN: Towards Reinforcement Learning States, Actions, and Rewards for Direct Flow Routing in Software-Defined Networks

“…To the best of our knowledge, our study is the first to directly represent flow routing in the stateaction space of reinforcement learning. For completeness we note that deep reinforcement learning has been applied to a wide variety of other communication network problems, including distributed routing [58], [59], congestion control [60], data center networks [61], wireless network routing [62]- [71], vehicular ad hoc network routing [72], [73], optical networking [74]- [76], caching [77], and mobile edge computing [78], [79]. We also note that a preprocessing approach for efficiently representing virtual network embeddings for subsequent algorithm processing has been examined in [80].…”

Networking Systems of AI: On the Convergence of Computing and Communications

“…In NSAI, distributed artificial intelligence becomes immersive in all elements of the network, i.e., cloud, edge, terminal devices, which make AI virtually operating as a networking system. On the other hand, by the evolution of SDN/NFV (Software Defined Networks/ Network Function Virtualization) [16], a network is becoming a service-specific system interweaved with AI, i.e., the network operates as an AI system, enabling the real-time smart services. With the developing technology trends of "AI as a network system, and network as an AI system", vice versa, the ecosystem of NSAI can be presenting the nextgeneration waves of both AI systems and telecommunication networks.…”