The NorNet testbed (https://www.nntb.no) is an Internet testbed platform for research on multi-homed systems. The particular property of multi-homed systems is to be connected to multiple Internet Service Providers (ISP) simultaneously. Its initial purpose is of course to still provide connectivity in case of ISP/network failures. But does it really work that well, also with current protocols and applications? And redundancy does not come for free. A user connected to multiple ISPs will also receive multiple Internet bills each month. So, is there a possibility to make further use of multi-homing in the usual case where nothing goes wrong? Obviously, there are a lot of interesting research questions, which need to be examined in realistic Internet setups! Therefore, we are building up the NorNet open Internet testbed platform as a Linux- and Open-Source-software-based infrastructure, which currently spreads over multiple sites in different countries.

NorNet makes extensive use of advanced Linux features like Kernel-based Virtualisation (KVM), Linux Containers (LXC), BTRFS file system features, IP routing rules, Stream Control Transmission Protocol (SCTP), Multi-Path TCP (MPTCP), and many more. The goal of this talk is therefore to present an overview of the testbed, its underlying Linux features, and how they are combined to provide the multi-homing features to the various testbed users. This particularly includes an overview of how to make use of multi-path transport with MPTCP – based on the Linux MPTCP implementation – in multi-homed environments. The idea is to provide guidelines for also utilising multi-homing features in own projects.

An effective way of jointly analyzing data from multiple sources is through coupled matrix and tensor factorizations (CMTF). Different characteristics of datasets from multiple sources require to employ various regularizations, constraints, loss functions and different types of coupling structures between datasets. While existing algorithmic approaches for CMTF can incorporate constraints, linear couplings and different loss functions, none of them has been shown to achieve the flexibility to incorporate all. We propose a flexible algorithmic framework for coupled matrix and tensor factorizations, which utilizes Alternating Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM). The framework facilitates the use of a variety of constraints, loss functions and couplings with linear transformations. Numerical experiments on simulated datasets and real data from chemometrics and hyperspectral super-resolution demonstrate that the proposed approach is accurate, flexible and computationally efficient with comparable or better performance than available CMTF algorithms.

While we focus on CANDECOMP/PARAFAC (CP) –based CMTF models, we will also briefly discuss the use of an AO-ADMM based algorithmic approach for fitting a PARAFAC2 model.  We demonstrate that the proposed algorithmic approach enables imposing constraints in all modes, which has been a challenge using the traditional alternating least squares-based algorithm used for PARAFAC2.