Karlla B. Chaves Rodrigues

The coupling of content caching at the wireless network edge and video streaming recommendation systems has been thoroughly investigated to enhance the cache hit and improve the user quality of experience (QoE). However, the existing literature lacks studies addressing the joint problem of QoE and cache hit ratio maximization while considering device characteristics and dynamic network resources of mobile users. This study introduces On-RAViR, an online framework comprising a Channel Quality Indicator (CQI) prediction module and a heuristic algorithm. This framework aims to maximize both cache hit ratio and user QoE under two constraints: the quality of the user equipment (UE) wireless link and the computing capabilities of the UE.We evaluate our framework employing a real-world video content dataset and a third-party 5G trace dataset. The results demonstrate that our framework produces rapid and high-quality solutions, increasing user QoE by 20% on average when compared to a state-of-the-art caching and recommendation heuristic unaware of computing and network resources.

The advent of B5G networks has revolutionized the telecommunications landscape by transitioning hardware resources to software components, predominantly running on cloud­based infrastructures. However, this ‘softwarization’ extends across the radio access, transport, and core networks, introducing complex challenges in realtime network management. In this context of the ‘softwarization’, it is imperative to make the behavior of B5G systems readily observable for effective management and fault diagnosis. This article presents a comprehensive empirical investigation of observability within a B5G system, specifically focusing on its radio access and core networks. The study enhances the system’s observability by combining advanced metric analysis and log parsing. Our method integrates Commercial Off­-The-­Shelf machine learning algorithms to diagnose anomalies and automate failure tasks. Besides that, our evaluation of the Cloud­-Native Observability Tools services revealed a significant memory footprint, accounting for 86% of the total memory usage and 22% overall CPU utilization. The findings also highlight that our approach mitigates the issue of non­standardization in log data, thereby facilitating proactive failure anticipation. This study can aggregate significant value for developing automated, self­healing B5G network systems.