WireGuard is a VPN protocol that has gained significant interest recently. Its main advantages are: (i) simple configuration (via pre-shared SSH-like public keys), (ii) mobility support, (iii) reduced codebase to ease auditing, and (iv) Linux kernel implementation that yields high performance. However, WireGuard (intentionally) lacks a control plane. This means that each peer in a WireGuard network has to be manually configured with the other peers’ public key and IP addresses, or by other means. In this paper we present an architecture based on a centralized server to automatically distribute this information. In a nutshell, first we manually establish a WireGuard tunnel to the centralized server, and ask all the peers to store their public keys and IP addresses in it. Then, WireGuard peers use this secure channel to retrieve on-demand the information for the peers they want to communicate to. Our design strives to: (i) offer a key distribution scheme simpler than PKI-based ones, (ii) limit the number of public keys sent to the peers, and (iii) reduce tunnel establishment latency by means of an UDP-based protocol. We argue that such automation can help the deployment in enterprise or ISP scenarios. We also describe in detail our implementation and analyze several performance metrics. Finally, we discuss possible improvements regarding several shortcomings we found during implementation.

Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization. We address this open question through a comparative analysis between the state of the art in ML and the state of the art in TE. To this end, we first present a novel distributed system for TE that leverages the latest advancements in ML. Our system implements a novel architecture that combines Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks (GNN) to minimize network congestion. In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE. Our experimental results show that the proposed MARL+GNN solution achieves equivalent performance to DEFO in a wide variety of network scenarios including three real-world network topologies. At the same time, we show that MARL+GNN can achieve significant reductions in execution time (from the scale of minutes with DEFO to a few seconds with our solution).

Enterprise networks, over the years, have become more and more complex trying to keep up with new requirements that challenge traditional solutions. Just to mention one out of many possible examples, technologies such as Virtual LANs (VLANs) struggle to address the scalability and operational requirements introduced by Internet of Things (IoT) use cases. To keep up with these challenges we have identified four main requirements that are common across modern enterprise networks: (i) scalable mobility, (ii) endpoint segmentation, (iii) simplified administration, and (iv) resource optimization. To address these challenges we designed SDA (Software Defined Access), a solution for modern enterprise networks that leverages Software-Defined Networking (SDN) and other state of the art techniques. In this paper we present the design, implementation and evaluation of SDA. Specifically, SDA: (i) leverages a combination of an overlay approach with an event-driven protocol (LISP) to dynamically adapt to traffic and mobility patterns while preserving resources, and (ii) enforces policies to groups of endpoints for scalable segmentation with low operational burden. We present our experience with deploying SDA in two real-life scenarios: an enterprise campus, and a large warehouse with mobile robots. Our evaluation shows that SDA, when compared with traditional enterprise networks, can (i) reduce overall data plane forwarding state up to 70% thanks to a reactive protocol using a centralized routing server, and (ii) reduce by an order of magnitude the handover delays in scenarios of massive mobility with respect to other approaches. Finally, we discuss lessons learned while deploying and operating SDA, and possible optimizations regarding the use of an event-driven protocol and group-based segmentation.