Ericsson (Hungary)

The papers in this Special Issue highlight significant progress toward fully convergent sensing and communication in optical networks, underscoring the growing role of these technologies in enabling autonomous, resilient, and environmentally aware fiber-optic networks. We present a brief introduction followed by an overview of the topics covered in the papers.

In 5G networks, the deployment of network slices enabled by Software-Defined Networking (SDN) is becoming a critical component for delivering tailored services to meet diverse application needs. However, this introduces challenges in network management, particularly in efficiently allocating resources to ensure that each network slice meets its specific Quality of Service (QoS) and availability requirements. Simultaneously, it must optimize overall network performance and network operator’s profit, which is linked to the Quality of Experience (QoE) of the end-users. Existing works offer either an availability-based solution or a QoE-aware solution to this problem, but not both. This paper addresses the end-to-end network slice resource allocation problem by simultaneously considering QoS and availability requirements in slice placement, while employing a QoE-aware strategy for resource allocation. We propose a framework that optimizes the network operator profit i.e. the highest QoE with the least resource usage, and can be flexibly configured to model realistic scenarios. Arbitrary network slice requirements can be defined using slice-specific QoS/QoE mapping, resource requirements, end-to-end latency and availability. For solving the formulated problem a Mixed Integer Nonlinear Programming (MINLP) formulation and efficient heuristic methods are proposed. Our solution accounts for the non-linear QoS/QoE relationship, utilizes redundantly placed Service Function Chains (SFCs) to increase availability, and supports the sharing of Virtual Network Functions (VNFs) among SFCs to optimize resource usage. Through extensive simulations on realistic network topologies and slice requests, we demonstrate the framework’s effectiveness in offering flexible and efficient network slice placement and resource allocation, utilizing a baseline heuristic from related studies. The results indicate that while the exact method delivers an optimal solution, heuristic approaches are suitable for time-sensitive tasks, such as dynamic slice configuration.

The advent of 6G is expected to transform connectivity by necessitating robust and scalable transport networks, capable of managing the escalating demands for enhanced bandwidth, negligible latency, and heightened network automation. The progression towards centralized radio access networks and the cloudification of network functions introduce additional requirements to the transport network. Addressing these demands, the integration of packet and optical systems combines packet flexibility with the high bandwidth and low latency of optical systems, aiming for a balance between performance, efficiency, and functionality. This process considers cost and the reuse of existing infrastructure towards a seamless transition to 6G. The concept of a Mini-ROADM, a cost-effective, energy-efficient optical switch created using silicon photonics, is presented and demonstrated in a ring network application. The role of a transport-aware end-to-end orchestrator in coordinating resources across radio, transport, and cloud domains to ensure a diverse range of quality-of-service levels is also discussed. A system demonstrator that highlights the integration of packet and optical layers and the concrete application of these concepts in a network environment is presented.

<p>Time-Sensitive Networking (TSN) is expected to provide reliable, low-latency communication for critical systems. Leveraging the Frame Replication and Elimination for Reliability (FRER) protocol, it protects against packet loss by replicating individual packets and delivering them on disjoint forwarding paths. FRER does not contain any in-built security solutions, and several FRER-related security vulnerabilities have recently been identified that could undermine TSN’s ultimate goal of providing extreme reliability. In this paper, we introduce a comprehensive security-focused testbed for analyzing FRER vulnerabilities. Our self-contained setup employs open and adaptable components, runnable on a single server, ensuring flexibility and accessibility. Leveraging eBPF/XDP, it efficiently implements FRER’s data plane functionalities, accommodating both fast-path and slow-path attacks. Parameters like delay, jitter, loss rate, and bandwidth are easily customizable. We validate the testbed’s effectiveness with various attack scenarios.</p>

In the last decade, static analysis has been gaining more and more ground as it is a powerful method to detect errors in software code early during development. However, most tools primarily focus on the application layer and therefore try to enforce the use of higher-level abstractions. These design decisions often result in low-level code being marked as dangerous. In this paper, we examine the effect of static analysers’ design on their applicability for system software, where direct access to hardware resources requires the use of very low-level methods. These codes, while idiomatic for the field, are considered as “hacks” or dangerous constructs by static analysers, resulting in an increased number of false positives. We present the list of Clang Static Analyzer, Clang-Tidy, and Cppcheck checkers which we observed to produce low false positive rate for system software. Based on these observations, we suggest developers to use these checks, as they can be efficiently used to detect real bugs. We implemented our suggestions as an analysis profile in the CodeChecker static analysis driver framework.

Cloud gaming has emerged as a cost-effective and accessible gaming solution, with platforms like NVIDIA GeForce NOW leading the way. The rapid growth of this industry, projected to reach 6.8 billion USD by 2028, has sparked the need for enhanced user experience models to optimize cloud and network infrastructure. In our study, we conducted a comprehensive analysis of the in-game performance of the popular NVIDIA GeForce NOW cloud gaming platform under varying network conditions. Our research focused on quality of service (QoS) metrics, particularly the WebRTC logs, and their relationship with user experience, defined as in-game performance. Standardized and repeatable measurements from the GeForce NOW platform were used, where the player was asked to complete training exercises of fast-paced games under different network conditions. This paper analyses and proposes machine learning (ML) models that estimate the user experience of cloud gaming. The models are trained on the low-level network- and application-related QoS metrics extracted from WebRTC logs. Our contribution demonstrates that ML models can accurately estimate in-game performance from QoS parameters, highlighting network latency's greater impact on the player's gaming experience than packet loss, bandwidth, and jitter. With our novel model, internet service providers (ISPs) can effectively estimate user experience using only network-related metrics, enabling network optimization and enhancing gaming services. This research deepens our understanding of cloud gaming user experience and offers insights for refining cloud gaming services.

Network Function Virtualization (NFV) enables the decoupling of network functions from dedicated hardware to run them as software instances on commodity servers through virtualization, replacing hardware-based network functions with software-based Virtual Network Functions (VNFs). In this paper, we study the joint problem of VNF decomposition and migration to address VNF embedding in NFV resource allocation (NFV-RA). More specifically, we investigate how VNF migration and VNF decomposition can be mutually beneficial to minimize the embedding cost of network services. After presenting a novel formulation of the problem as an integer linear programming (ILP), we validate it by CPLEX and show that our joint VNF decomposition and migration approach can outperform the VNF decomposition-only approach by 20% in terms of embedding cost.

The network edge's role in Artificial Intelligence (AI) inference processing is rapidly expanding, driven by a plethora of applications seeking computational advantages. These applications strive for data-driven efficiency, leveraging robust AI capabilities and prioritizing real-time responsiveness. However, as demand grows, so does system complexity. The proliferation of AI inference accelerators showcases innovation but also underscores challenges, particularly the varied software and hardware configurations of these devices. This diversity, while advantageous for certain tasks, introduces hurdles in device integration and coordination. In this paper, our objectives are three-fold. Firstly, we outline the requirements and components of a framework that accommodates hardware diversity. Next, we assess the impact of device heterogeneity on AI inference performance, identifying strategies to optimize outcomes without compromising service quality. Lastly, we shed light on the prevailing challenges and opportunities in this domain, offering insights for both the research community and industry stakeholders. Edge