Traditional telecommunication network functions often require configuring thousands of parameters, resulting in large configuration files. The built-in capabilities of Kubernetes struggle to manage the vast number of parameters in a single configuration file, leading to workarounds that are not native to Kubernetes. This paper proposes an automated, Kubernetes-native configuration deployment procedure based on GitOps principles and the Kubernetes API aggregation layer. This approach can solve the problems originating from the existence of large configuration files. Furthermore, we show numerical results about the deployment times to illustrate the engineering cost of the new approach.
A major challenge for telecommunications operators is to dimension and plan network capacity for all types of content delivered over the network, much of this being so-called “over-the-top” (OTT) traffic from third party content providers and aggregators. While Content Delivery Networks (CDN) are a very useful means of realising efficient delivery of large volumes of traffic, they can also be limited in flexibility and cost-efficiency due to the hardware-oriented nature of the architecture. Virtual CDNs (vCDNs) enable a more flexible and optimal delivery model whereby telco compute infrastructure offers a hosting environment for softwarised CDN caches. Such an approach also lends itself to increased levels of distribution and de-centralisation; edge compute to host vCDNs can provide both service quality advantages on the one hand, and network capacity planning benefits, on the other. This latter advantage arises from the opportunity for “local turn-around” of traffic due to cache hits on popular cached content. Only real-world trials of vCDN can determine these offload benefits in quantitative terms. This paper explains a major trial with two CDN partners (one delivering video content, the other delivering game content) performed in summer 2024 for a “metro” area; significant cache efficiency levels ranging from 50% to above 90% were observed confirming that significant traffic offload and thus capacity planning benefits - can be realised.
Modern enterprises depend on uninterrupted WideArea Network (WAN) connectivity between geographically dispersed sites and cloud services. Software-Defined Wide Area Networks (SD-WANs) have emerged as a compelling solution, offering greater flexibility and cost-efficiency compared to conventional WAN technologies. Ensuring robust availability-defined as the network's readiness to provide connectivity-is crucial for a successful SD-WAN implementation. This work presents a comprehensive, model-based evaluation framework designed to quantify service-oriented availability between pairs of SDWAN endpoints. Our hierarchical modeling approach effectively addresses the complexity and accurately reflects the dependencies among various SD-WAN components. Furthermore, we investigate the impact of a centralized control plane and its associated non-idealities on availability, providing insights into communication delays, sampling frequency of network characteristics, and algorithm execution times through simulation. In-depth experiments demonstrate the evaluation capabilities of our framework, including sensitivity analysis to identify critical components, the effects of various non-idealities, and how different control plane policies can mitigate them. These results offer valuable guidance for optimizing SD-WAN design before real-world implementation.
As the demand for mobile data continues to grow, the energy consumption of mobile networks becomes a major concern. Specifically, base stations account for over 76% of energy usage in mobile networks. We consider a two-tier cellular system, one offering basic coverage while the other offers extra capacity. To save energy, existing network features can opportunistically shut down capacity layer cells when physical resources are lightly utilized. Nevertheless, this is performed without guaranteeing coverage cells can maintain the sought user-centric service quality. To address this challenge, we propose a machine learning (ML)-aided search approach that dynamically designs energy-saving configurations for each cell in each hour while being constrained with a pre-defined quality of service (QoS) measure. This ML model was trained using data collected from 10,283 cells of a live network. We introduce two different approaches to provide these settings: Adaptive QoS Threshold Optimization Algorithm (AQTOA) and an Exhaustive Search (ES) baseline. AQTOA is a low complexity ML-aided search algorithm designed to determine the optimal shutdown threshold for capacity cells while ensuring that QoS requirements are met. Through extensive live network experimentation, the AQTOA results indicate a 1.8% improvement in energy savings compared to the earlier static settings models while maintaining a more strict QoS level than the one addressed in the previous work.
Virtual reality (VR) cloud gaming faces significant challenges in terms of latency and bandwidth consumption, particularly in softwarized 5 G and beyond (5G&B) networks. Ensuring an immersive experience on low-end devices, such as smartphones and cardboard-like headsets, requires innovative solutions to mitigate delay effects and optimize resource usage. This research focuses on enhancing VR cloud gaming performance by leveraging Digital Twins (DTs) and hierarchical compression techniques. The proposed system adopts a client-server architecture, where computationally intensive tasks are offloaded to the server, minimizing processing requirements on the client side. A key contribution is a novel delay estimation and management framework that employs DTs to synchronize video streaming and user input, reducing the impact of latency. Additionally, viewport-based hierarchical compression is applied to optimize bandwidth consumption while maintaining high perceived quality. The proposed architecture is evaluated through extensive experiments analyzing the effects of input delay, delay fluctuations, and compression methods on user performance and perceived quality. Results demonstrate that the system effectively mitigates adverse network conditions, ensuring a seamless and immersive VR gaming experience even in suboptimal environments.
