Our dependency on the telecommunication infrastructure is continuously increasing, as different infrastructures – such as energy and telecommunication – now have mutual dependencies. This calls for increased monitoring of the fibre network, which is a highly critical part of the infrastructure. State of Polarisation (SoP) of light propagating through fibre transmission systems is impacted by any vibrations and mechanical impacts on the fibre. By continuously monitoring the SoP, any unexpected movements of a fibre along a fibre-path may be traced. Movements may be caused by e.g. work in node-rooms impacting patch-cords, trawlers or other types of sub-sea equipment touching or hooking into sub-sea fibre cables, digging close to a fibre-cable, or geophysical phenomena like earthquakes. In this paper, we describe a low-cost, scalable system for SoP monitoring and give examples of patterns monitored in different types of fibre infrastructures. The monitoring system consists of single-unit rack-mount instruments connected to taps from live optical transmission signals. Each instrument has local storage for 1-2 years of data, and is periodically automatically uploading data to a server for backup and data-access purposes. Examples of observed patterns are impact from a thunderstorm on a Fibre-To-The-Home (FTTH) cable, 50 Hz on a fibre-cable spun around a high-voltage power air-cable, as well as animal impact on a patch-cord.

Network Function Virtualization is a key enabler to building future mobile networks in a flexible and cost-efficient way. Such a network is expected to manage and maintain itself with least human intervention. With early deployments of the fifth generation of mobile technologies – 5G – around the world, setting up 4G/5G experimental infrastructures is necessary to optimally design Self-Organising Networks (SON). In this demo, we present a custom small-scale 4G/5G testbed. As a step towards self-healing, the testbed integrates four Programming Protocol-independent Packet Processors (P4) virtual switches, that are placed along interfaces between different components of transport and core network. This demo not only shows the administration and monitoring of the Evolved Packet Core (EPC) VNF components, using Open Source MANO, but also as a proof of concept for the potential of P4-based telemetry in detecting anomalous behaviour of the mobile network, such as a congestion in the transport part.

IT systems monitoring is a crucial process for managing and orchestrating network resources, allowing network providers to rapidly detect and react to most impediment causing network degradation. However, the high growth in size and complexity of current operational networks (2022) demands new solutions to process huge amounts of data (including alarms) reliably and swiftly. Further, as the network becomes progressively more virtualized, the hosting of nfv on cloud environments adds a magnitude of possible bottlenecks outside the control of the service owners. In this paper, we propose two deep learning anomaly detection solutions that leverage service exposure and apply it to automate the detection of service degradation and root cause discovery in a cloudified mobile network that is orchestrated by ETSI OSM. A testbed is built to validate these AI models. The testbed collects monitoring data from the OSM monitoring module, which is then exposed to the external AI anomaly detection modules, tuned to identify the anomalies and the network services causing them. The deep learning solutions are tested using various artificially induced bottlenecks. The AI solutions are shown to correctly detect anomalies and identify the network components involved in the bottlenecks, with certain limitations in a particular type of bottlenecks. A discussion of the right monitoring tools to identify concrete bottlenecks is provided.

Open Source MANO is a helpful tool to manage and orchestrate the instantiation of core network setups, like Network Service (NS) instances of our SimulaMet OpenAirInterface Virtual Network Function (VNF) for Enhanced Packet Cores (EPC). We furthermore extended our NS with VNF instances of Programming Protocol-independent Packet Processors (P4) switches, in order to allow for in-band telemetry. With in-band telemetry, it is possible to flexibly add, process, and remove telemetry information to traffic within the packet core, in order to allow for fine-granular evaluation of the system performance and the users' experienced quality of service. In our presentation and demo, we would like to provide an overview of our ongoing work on P4-based in-band telemetry in an OSM-orchestrated 4G core, which is used for detecting performance problems and anomalies in the network based on machine learning. We would furthermore like to demonstrate the details of our setup to the audience in a live demo.

Mobile Edge Computing (MEC) places cloud resources nearby the user, to provide support for latency-sensitive applications. Offloading workload from resource-constrained mobile devices (such as smartphones) into the cloud ecosystem is becoming increasingly popular. In this demonstration, we show how to deploy a mobile network (with OpenAirInterface and Open Source MANO), as well as to adapt the Reliable Server Pooling (RSerPool) framework to efficiently manage MEC as well as multi-cloud resources to run an interactive demo application.

With the increasing deployment of the Multi-Path Transmission Control Protocol (MPTCP) in heterogeneous network setups, there is a need to understand how its performance is affected in practice both by traditional factors such as round-trip time measurements, buffer predictive modelling and by calculating the impact factors of network subflows. Studies have shown that path management and packet scheduling have a large effect on overall performance and required limited resources with different congestion control parameters. Unfortunately, most of the previous studies have focused almost exclusively on the improvement of a single parameter, without a holistic view. To deal with this issue effectively, this paper puts forward a Multi-Parameter Comprehensive Optimized Algorithm (MPCOA), which can find the smaller buffer size and select the appropriate congestion control and path management algorithm on the premise of ensuring larger throughput. Experiments of three scenarios show that MPCOA can save the buffer space and subflow resources, and achieve high throughput. Meanwhile, a set of quantitative improvement results given by MPCOA is convenient for us to evaluate the quality of MPTCP network, and provide reference for our ongoing future work, like for 4G/5G, Internet of Things and Star Link networks.

5G, the fifth generation of mobile broadband networks, is going to make a large range of new applications possible. However, further research is necessary, and the basic step, i.e. setting up a 4G/5G testbed infrastructure, is a complicated and error-prone task. In this abstract and poster, we introduce our open source SimulaMet EPC Virtual Network Function (VNF), as an easy way to set up a 4G/5G testbed based on Open Source MANO and OpenAirInterface. We would like to showcase how a researcher can use our VNF as part of his own research testbed setup. Therefore, the focus is particularly on the user interface details and features of the SimulaMet EPC VNF.

Setting up Enhanced Packet Cores (EPC) – like the Mosaic5G OpenAirInterface-based EPC – for 4G/5G Testbeds is a complicated and error-prone task. Therefore, we developed the SimulaMet OpenAirInterface VNF, a complex 4-VDU VNF, which upon instantiation builds the components of the EPC from scratch from given source Git repositories. That is, based on the parametrisation, users can easily create tailor-made EPCs for their projects, particularly EPCs based on the Mosaic5G FlexRAN sources. In this presentation, we would like to shortly highlight the solutions chosen to efficiently use OSM for handling the instantiation process, performing telemetry, and debugging issues. That is, we particularly would like to present to the Mosaic5G audience some lessons learned during the ongoing development.

Today, a steadily increasing number of users not just passively consumes Internet content, but also creates and publishes content. Users publish text, photos and videos. With the availability of 5G high-speed, low-latency mobile broadband networks, also real-time video streaming will be possible. We believe this will become a very popular application in the coming years. But the more popular a service is, the higher the need for resilience. In this paper, we introduce our work-in-progress live video streaming platform for future mobile edge computing scenarios, which makes use of MPTCP+IPv6 to support multi-homing for resilience, and multi-path transport for load balancing. As a proof of concept, we show that the platform is (1) compatible with IPv6, (2) utilizes load balancing when possible, and (3) provides robustness by network redundancy.

In former times, it was necessary to operate and maintain powerful personal computers to run applications. Nowadays, many "normal" users just use laptops, tablet PCs or smartphones. Their applications are powered by cloud systems in the background, which are operated in data centres at remote locations and being connected over the Internet. This presentation first introduces the basics of cloud computing: virtualisation, virtual machines, containers, and software as a service. A challenge of using cloud computing is to deploy services to cloud providers, in order to operate them in a cost-efficient way while providing the best application experience to the users. The vision of the Multi-Cloud Execution-Ware for Large-scale Optimised Data-Intensive Computing (MELODIC) project is to enable federated cloud computing for data-intensive applications. Furthermore, it provides the user with an easy-to-use, unified cloud environment, which hides the complexity of a multi-cloud. The second part of this presentation therefore provides an overview of the basic ideas and application use cases of MELODIC.

This talk gives a short overview over the possibilities of testing applications in the NorNet infrastructure. Furthermore, it presents a short overview of the ongoing work on integrating NorNet Core with the MELODIC multi-cloud infrastructure, including the NorNet Core setup at Hainan University and the Haikou College of Economics.

It has been shown that the Multi-Path Transmission Control Protocol~(MPTCP) can improve throughput, robustness and resilience of network transport. This paper seeks to discover the relationship of buffer size with throughput and congestion control algorithms, based on the statistical predictive modelling method. In spite of rapid growth of the implementations of MPTCP, the theoretical and fundamental question –- how large the buffer size of MPTCP should be to meet the network traffic -– remains unaddressed, although there were graphic illustrations and descriptive discussions about it.

There is a growing concern that the Internet transport layer has become ossified in the face of emerging novel applications, and that further evolution has become very difficult. The NEAT system is a novel and evolvable transport system that decouples applications from the underlying transport layer and network services. In so doing, it facilitates dynamic transport selection. This demo shows how the NEAT system is able to dynamically select the most appropriate transport solution for the Mozilla Firefox web browser.

The goal of NEAT (A New, Evolutive API and Transport-Layer Architecture for the Internet) is to allow network "services" offered to applications – such as reliability, low-delay communication or security – to be dynamically tailored based on application demands, current network conditions, hardware capabilities or local policies, and also to support the integration of new network functionality in an evolutionary fashion, without applications having to be rewritten. This talk gives a practical introduction to NEAT from a developer's perspective: after an introduction to NEAT, the APIs and in particular the NEAT Sockets API are explained. This is followed by pseudo-code examples and finally running-code examples. These running-code examples particularly also show how to use NEAT in NorNet Core