Virtual Network Functions (VNFs) are essential components in modern networking that decouple network functions from dedicated hardware, thereby enhancing flexibility, scalability, and cost-efficiency. The inherently dynamic nature and fluctuating loads of networks, combined with evolving network demands, often lead to the over-allocation or underprovisioning of VNF resources, posing a significant challenge in optimal VNF resource usage. This issue becomes even more challenging when VNFs are connected in a specific sequence to fulfill a functional need-Service Function Chain (SFC). A promising solution to this challenge is proactive resource management-predicting resource demands of VNFs in advance. In this paper, we present a performance metrics prediction framework designed to anticipate multi-step resource demands for VNFs. The framework employs machine learning models, including RNN, LSTM, GRU, and Transformer models, within a robust meta-learner to capture complex usage patterns, accounting for both short-term and long-term dependencies in VNF resource consumption. This approach allows for precise prediction of critical key performance indicators (KPIs), including CPU usage, memory usage, processing latency, and traffic load for each VNF within an SFC. The evaluation results show that the proposed framework achieves a 75% reduction in mean absolute error (MAE) compared to the Transformer model and over 84% compared to RNN, LSTM, and GRU models. These results demonstrate the framework’s substantial improvement over state-of-the-art approaches.

The advent of 6G networks promises unprecedented advancements in communication technologies, demanding innovative solutions for service placement across the Compute Continuum (CC), where computing resources are distributed across the network area, from edge to cloud. This paper explores a novel approach for service placement in 6G networks using Reinforcement Learning (RL) techniques. By leveraging the dynamic decision-making capabilities of RL, this work addresses the complexities of distributing services across heterogeneous computing resources to enhance network performance, reduce latency, and improve resource utilization. An extensive evaluation, based on a real dataset collected from commercial 4G/5G networks, was conducted under various network conditions and workloads to evaluate the effectiveness of the proposed approach. Results highlight the adaptability of our RL-driven model to dynamic network environments, demonstrated by its capacity to optimize for multiple objectives simultaneously. Our analysis also reveals that while single-objective heuristics can outperform RL in specific, limited scenarios, these struggle to handle increasing complexity. These findings highlight the viability of RL as a powerful tool for intelligent service management in next-generation communication systems, paving the way for more resilient and efficient 6G network architectures.

Cloudified mobile networks are expected to deliver a multitude of services with reduced capital and operating expenses. A characteristic example is 5G networks serving several slices in parallel. Such mobile networks, therefore, need to ensure that the SLAs of customised end-to-end sliced services are met. This requires monitoring the resource usage and characteristics of data flows at the virtualised network core, as well as tracking the performance of the radio interfaces and UEs. A centralised monitoring architecture can not scale to support millions of UEs though. This paper, proposes a 2-stage distributed telemetry framework in which UEs act as early warning sensors. After UEs flag an anomaly, a ML model is activated, at network controller, to attribute the cause of the anomaly. The framework achieves 85% F1-score in detecting anomalies caused by different bottlenecks, and an overall 89% F1-score in attributing these bottlenecks. This accuracy of our distributed framework is similar to that of a centralised monitoring system, but with no overhead of transmitting UE-based telemetry data to the centralised controller. The study also finds that passive in-band network telemetry has the potential to replace active monitoring and can further reduce the overhead of a network monitoring system.

Leveraging virtualization technology, Mobile Edge Computing (MEC) deploys multiple services with different execution time requirements running as isolated processes. For instance, both real-time (RT) and non-RT applications may be (are) running on the same infrastructure using containerized virtualization. Nevertheless, sharing resources (e.g., CPU) with collocated workloads could impact the RT performance of RT applications. This paper presents PRINCIPIA, a dynamic CPU and CPU-shares allocation mechanism that opportunistically enables non-RT applications to run on underutilized CPUs while providing RT guarantees to RT applications. By monitoring MEC’s system metrics like processor’s CPU utilization and container’s CPU usage, PRINCIPIA dynamically allocates both CPU and CPU-shares to containers running non-RT applications aiming at opportunistically exploiting underutilized CPUs by containers running RT applications. We evaluate PRINCIPIA on a small-scale MEC server which uses containerized virtualization along with Linux RT Kernel to deploy both RT and non-RT applications. Our findings show that PRINCIPIA mitigates the impact on the RT performance of RT applications providing bounded processing latency in comparison with the default host Kernel scheduler.

Leveraging virtualization technology, Cloud-RAN deploys multiple virtual Base Band Units (vBBUs) along with collocated applications on the same Mobile Edge Computing (MEC) server. However, the performance of real-time (RT) applications such as the vBBU could potentially be impacted by sharing computing resources with collocated workloads. To address this challenge, this paper presents a dynamic CPU sharing mechanism, specifically designed for containerized virtualization in MEC servers, that hosts both RT and non-RT general-purpose applications. Initially, the CPU sharing problem in MEC servers is formulated as a Mixed-Integer Programming (MIP). Then, we present an algorithmic solution that breaks down the MIP into simpler subproblems that are then solved using efficient, constant factor heuristics. We assessed the performance of this mechanism against instances of a commercial solver. Further, via a small-scale testbed, we assessed various CPU sharing mechanisms and their effectiveness in reducing the impact of CPU sharing on RT application processing performance. Our findings indicate that our CPU sharing mechanism reduces the worst-case execution time by more than 150% compared to the default host RT-Kernel approach. This evidence is strengthened when evaluating this mechanism within Cloud-RAN, in which vBBUs share resources with collocated applications on a MEC server. Using our CPU sharing approach, the vBBU’s scheduling latency decreases by up to 21% in comparison with the host RT-Kernel.

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 minimum human intervention. With early deployments of the fifth generation of mobile technologies – 5G – around the world, setting up 4G/5G experimental infrastructure 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 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 VNF components, using Open Source MANO, but also serves 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.

Mobile broadband networks, although increasingly popular, suffer large fluctuations in performance. Download speeds can drop by 50% or more during peak hours. Hence, understanding and dissecting the causes of these fluctuations is central to improving current and future networks. In this paper, we propose a congestion detection and localisation method, Q-TSLP, that combines and extends the two state-of-the-art congestion detection tools: Q-Probe and TSLP. Q-Probe monitors patterns in packet arrivals, while TSLP tracks shifts in RTT to detect bottleneck at different segments of an end-to-end path. QProbe can attribute congestion, at a very coarse level, to either radio or non-radio related. TSLP on the other hand cannot pinpoint radio related congestion. Q-TSLP provides a per-hop congestion attribution thus addressing these limitations. To this end, we build two small scale LTE testbeds and experiment with a series of congestion scenarios. These controlled experiments show that apart from correct congestion localisation to finer granularity, the detection accuracy improves significantly with Q-TSLP, up to 100% in some cases. We then run a three-month long measurement campaign of congestion over two commercial operators in Norway. Overall, we run 17 million tests from a large number of geographically distributed probes. We find that both operators suffer congestion at different parts of the network. Our findings indicate that apart from mobile radio access, a non-trivial fraction of cases is related to congested mobile operator and Internet paths beyond the mobile network core. These findings hint that operators may need significant infrastructure upgrades to cope with potential 5G traffic volumes.

The Cloud-RAN architecture is a key enabler to building future mobile networks in a flexible and cost-efficient way. For instance, switched Ethernet is a prime candidate for mobile transport networks (Xhaul), due to its flexibility, ubiquity, and cost-effectiveness. Understanding its performance under different network configurations would allow concluding about its appeal for Cloud-RAN. On the other hand, evaluating resource sharing mechanisms is relevant to put in place best solutions to host multiple virtual Base Band Units (vBBUs) into the same compute infrastructure. This paper assesses the feasibility of using a switched Ethernet Xhaul, by instantiating two vBBUs using different functional splits. Moreover, this paper evaluates two mechanisms for sharing network interface cards (NIC) in a general purpose server (GPS) hosting vBBUs. Our results point to a marginal performance degradation caused by the switched Ethernet Xhaul and the NIC sharing mechanisms. Such deviations could be seen from the increase in average and maximum Jitter and RTT results.