Fair resource allocation in virtualized O-RAN platforms

O-RAN systems and their deployment in virtualized general-purpose computing platforms (O-Cloud) constitute a paradigm shift expected to bring unprecedented performance gains. However, these architectures raise new implementation challenges and threaten to worsen the already-high energy consumption of mobile networks. This paper presents first...

EdgeBOL: A Bayesian Learning Approach for the Joint Orchestration of vRANs and Mobile Edge AI

Future mobile networks need to support intelligent services which collect and process data streams at the network edge, so as to offer real-time and accurate inferences to users. However, the widespread deployment of these services is hindered by the unprecedented energy cost they induce to the network, and by the difficulties in optimizing...

Orchestration Procedures for the Network Intelligence Stratum in 6G Networks

The quest for autonomous mobile networks introdu-ces the need for fully native support for Network Intelligence (NI) algorithms, typically based on Artificial Intelligence tools like Machine Learning, which shall be gathered into a NI stratum. The NI stratum is responsible for the full automation of the NI operation in the network, including...

Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results

Virtualized base stations (vBS) can be implemented in diverse commodity platforms and are expected to bring unprecedented operational flexibility and cost efficiency to the next generation of cellular networks. However, their widespread adoption is hampered by their complex configuration options that affect in a non-traditional fashion both...

An Optimal Deployment Framework for Multi-Cloud Virtualized Radio Access Networks

Virtualized radio access networks (vRAN) are emerging as a key component of wireless cellular networks, and it is therefore imperative to optimize their architecture. vRANs are decentralized systems where the Base Station (BS) functions can be split between the edge Distributed Units (DUs) and Cloud computing Units (CUs); hence they have many...

Bayesian online learning for energy-aware resource orchestration in virtualized RANs

Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We perform an in-depth experimental analysis of the energy consumption of...

EdgeBOL: Automating energy-savings for mobile edge AI

Supporting Edge AI services is one of the most exciting features of future mobile networks. These services involve the collection and processing of voluminous data streams, right at the network edge, so as to offer real-time and accurate inferences to users. However, their widespread deployment is hampered by the energy cost they induce to...

Demonstrating a Bayesian Online Learning for Energy-Aware Resource Orchestration in vRANs

Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration...

Experimental Evaluation of Power Consumption in Virtualized Base Stations

Network virtualization is intended to be a key element of new generation networks. However, it is no clear how the implantation of this new paradigm will affect the power consumption of the network. To shed light on this relatively unexplored topic, we evaluate and analyze the power consumption of virtualized Base Station (vBS) experimentally....