The integration of localization capabilities within the cellular architecture through dedicated 5G network functions has notably enhanced cellular positioning accuracy and enabled new location-based services. However, this architectural shift requires placing measurement acquisition and computation at the network edge and core, resulting in distributed computational resources and increased latency and security risks. As a result, minimizing latency and ensuring resilience against security threats, in addition to achieving high accuracy, become critical performance indicators in location-based services. This paper examines both 3GPP-standardized and O-RAN-based 5G architectures, detailing the key functions, interfaces, and parameters influencing the localization process, from measurement acquisition to position estimation. We define performance indicators for evaluating localization services and develop a system model that quantifies costs related to latency, accuracy, computation, and resilience against security threats. By jointly considering these factors, we formulate a multi-objective optimization problem that guides the selection of an optimal system configuration to simultaneously satisfy multiple localization requirements. We validate our approach through a case study of an end-to-end 5G system using both simulations and experimental data. Specifically, we evaluate various algorithms and implementations across standardized channels and scenarios. Furthermore, we conduct experimental measurements using Software-Defined Radios (SDRs) and open-source 5G platforms to assess operational latency with commercial-off-the-shelf (COTS) devices.

Providing 5G seamless and secure services for train passengers is very challenging. In addition to the challenges related to high-speed, many others, such as low signal quality due to 5G coverage holes, tunnels, and complex orography, may compromise the Quality of Service (QoS) provided to train passengers, especially in trains crossing international borders. To overcome these challenges, the EU-funded Horizon 2020 5GMED project has devised a cross-border 5G network architecture that allows train passengers to have the same QoS they have at home in whichever country they are. The network architecture is based on the concept of train neutral host, which operates as a service provider for other mobile network operators. The communication between the train and the ground is facilitated by a heterogeneous network infrastructure comprised of 5G StandAlone (SA), IEEE 802.11ad, and satellite networks to minimize the probability of having low quality signals along the rail track. The seamless switching between these network technologies is performed using Adaptive Communication System-Gateways (ACS-GW) developed in the project. In this paper, we propose the train neutral host architecture, and present the performance evaluation results obtained in a realistic environment over 5G SA networks deployed on the Mediterranean cross-border corridor between Spain and France. Experimental results show satisfying results in terms of service throughput, latency and train inter-cell handover.

Despite extensive research and standardization efforts aimed at developing and enhancing 5G localization services, a significant gap persists between theoretical findings and experimental deployments, impeding the validation of key results in real-world operational settings. This paper contributes to fill this gap by proposing an End-to-End (E2E) implementation of a 5G localization system to explore the existing support of commercial off-the-shelf (COTS) user devices and existing RAN solutions for the localization functionality. To this end, we first develop a standard-compliant implementation of the location management function (LMF), i.e., the standard network function responsible for managing location information in the 5G core network. Then, we integrate the LMF with open-source core networks to conduct comprehensive testing on a suite of COTS user equipments and existing 5G RAN solutions, comparing commercial with open-source alternatives. By documenting encountered limitations and releasing our LMF software implementation as open-source, our work significantly contributes to the advancement of 5G localization research and testing in real environments and advocates for increased experiment-readiness in 5G positioning systems.

The communication services of future trains require hyperconnectivity between trains and track. Future Railway Mobile Communication System (FRMCS) reserved bands will not be able to cover all digital train requirements including passengers’ services. The 5GMED project adopts the idea where Infrastructure Managers (IMs) will collaborate with Railway Undertaking (RU) to facilitate the connectivity to Mobile Network Operator (MNO) communication services and vice versa (complemented with the use of satellite networks), with an architecture open to build new business models and relationships between stakeholders. This approach encompasses an adaptative Gigabit train-to-track connectivity solution able to fit Digital Train requirements. The 5GMED project will demonstrate these capabilities through four representative services deployed on a TGV of SNCF (French Train Operator) in a challenging scenario: a cross-border section of the Mediterranean corridor between Figueres (Spain) and Perpignan (France).

Container technology plays an important role in the virtualization landscape today. A container uses its own file system consisting of a stack of layers, which are stored on the execution server's disk. Containers running on the same server share the layers they have in common, and this sharing results in valuable savings in server storage space. Many containers can run on a single server, but when their resource demands grow enough, they are distributed across a cluster of nodes/servers by orchestration systems, such as Kubernetes. In this work, we found that for the same amount of containers to run, the storage required is higher for clusters consisting of a larger number of nodes. The severity of this storage overhead depends on the scheduling policy used to select the nodes that run the containers. By comparing different storage-saving scheduling policies that differ from each other in the depth of storage knowledge they leverage to make decisions, our analysis reveals that only deep, layer-level knowledge can effectively counter the growth in storage demand as the cluster size increases. Policies with coarser-grained knowledge achieve limited benefit because they achieve performance that is nearly equal to that of a random, zero-knowledge policy.

$\mu$Bench is an open-source tool for benchmarking cloud/edge computing platforms that run microservice applications. The tool creates dummy microservice applications that can be customized and executed on a Kubernetes cluster.$\mu$Bench allows users to control fundamental properties of the microservice applications it creates, such as service mesh topology, microservices’ behaviors using a portfolio of stress functions (e.g., for CPU, memory, I/O, network) or implementing new ones, microservice-to-microservice API (HTTP or gRPC), etc. Application performance can be evaluated by stochastic or trace-driven workloads. μBench is aimed at researchers and cloud platform developers who lack real microservice applications to benchmark their findings (e.g., new resource control mechanisms, artificial intelligence-driven orchestration, etc.) or wish to thoroughly evaluate their proposals versus a broad set of heterogeneous applications that μBench can create. In addition to the description of μBench, in this article, we show one possible use of it. We compared advantages and disadvantages of microservice architectures versus monolithic ones, and analyzed the performance impact of key architectural choices, such as service mesh topology and the use of replication. For this analysis, we generated several microservice applications with different properties, and two of them are derived from a real cloud dataset.

Programmable dataplanes are emerging as a disruptive technology to implement network function virtualization in an SDN environment. This technology can be further enhanced by using data plane abstraction with stateful processing. In this paper we focus on real world use cases with stateful forwarding requirements to validate Open Packet Processor data plane abstraction. We first demonstrate the suitability of OPP for implementing complex stateful network functions by providing the detailed implementation of three use cases. Second, we assess the scalability of the use case implementations in the context of a datacenter deployment.

Stateful programmable dataplanes are emerging as a disruptive technology and an enabler factor for network function virtualization in SDN environments. In this demo paper we show three real world use cases with stateful forwarding requirements to validate both the hardware and software implementations of the Open Packet Processor data plane.