A reliable Wide Area Network (WAN) has become a necessity for businesses enterprises to transmit critical data between multiple branches and to increase their revenues. Software-Defined Wide Area Networking (SD-WAN) is an emerging paradigm that introduces the advantages of Software Defined Networking (SDN) into Enterprise Networking (EN). SD-WAN can support differentiated services over public WAN by dynamically changing the flow forwarding rules over an overlay network based on monitoring data and service requirements. This paper proposes an early implementation of SD-WAN based on open source components, such as OpenDaylight as SDN controller, OpenvSwitch (OvS) and a set of services for network monitoring and policy-based path selection. We present a demo-test in a simple emulated but realistic network environment, showing new features and advantages for the enterprise in terms of resource optimization.

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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.

A reliable Wide Area Network (WAN) has become an imperative need for enterprises with Cloud-hosted applications and distributed branch offices. Software-Defined Wide Area Network (SD-WAN) has been regarded as the most promising technological solution for next generation enterprise networks capable of increasing network agility and reducing costs. In this paper, we present an experimental SD-WAN solution capable of running and optimizing delay-sensitive services, such as VoIP and video streaming, while minimizing downtime caused by network failures. We validate our solution thanks to two SD-WAN testbeds: the first one is deployed in a municipal network of an Italian city, while the other is emulated in our laboratory. The goal is to show the capability of SD-WAN of guaranteeing fast recovery and resilience in case of network failures, exploiting an innovative eBPF-based monitoring technique.

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