AI@EDGE Scientific Publications
AI@EDGE: A Secure and Reusable Artificial Intelligence Platform for Edge Computing
Artificial Intelligence (AI) has become a major innovative force and a major pillar in the fourth industrial revolution. This trend has been acknowledged by the European Commission, who has pointed out how high-performance, intelligent, and secure networks are fundamental ….
FaaS and Curious: Performance implications of serverless functions on edge computing platforms
Serverless is an emerging paradigm that greatly simplifies the usage of cloud resources providing unprecedented auto-scaling, simplicity, and cost-efficiency features. Thus, more and more individuals and organizations adopt it, to increase their productivity and focus exclusively ….
Towards sharing one FPGA SoC for both low-level PHY and high-level AI/ML computing at the edge
International Mediterranean Conference. on Communications and Networking (meditcom 2021), Workshop 1 on Acceleration for Edge Computing
Delay-Sensitive Wireless Content Delivery: An Interpretable Artificial Intelligence Approach
1st International Workshop on Network Programmability (NetP 2021) co-located with CNSM 2021
Auto-configuration des systèmes de détection d’intrusions grâce aux expériences passées
RESSI (Rendez-Vous de la Recherche et de l’Enseignement de la Sécurité des Systèmes d’Information)
An AI-empowered framework for cross-layer softwarized infrastructure state assessment
Network softwarization technologies challenge legacy fault management systems. Coordination and dependency among different novel software components for orchestration, switching, virtual machine and container management creates novel monitoring points, ….
Robust Access Point Clustering in Edge Computing Resource Optimization
Multi-access Edge Computing (MEC) technology has emerged to overcome traditional cloud computing limitations, challenged by the new 5G services with heavy and heterogeneous requirements on both latency and bandwidth. …
A Lightweight Southbound Interface for Standalone P4-NetFPGA SmartNICs
We present a lightweight Southbound Interface (SBI) for P4→NetFPGA devices, aimed at enhancing the capability of NetFPGA Smart Network Interface Cards (SmartNICs) to work in standalone mode. ….
An Open Dataset for Beyond-5G Data-driven Network Automation Experiments
In this paper, we present the 5G3E (5G End-to-End Emulation) dataset created to support 5G network automation. The dataset contains thousands of time-series, built at different sampling rates, related to the observation of multiple resources involved in 5G network operation: radio, computing and network resources. ….
Anomaly Detection for 5G Softwarized Infrastructures with Federated Learning
We present how to distribute an anomaly detection framework at the state of the art, called SYRROCA (SYstem Radiography and ROot Cause Analysis), for edge computing and 5G environment, using federated learning. The goal is to leverage ….
AI at the extreme edge: the role of FPGAs for enabling onboard AI in space missions
HIPEAC 2022 (WRC workshop)
LSTM acceleration with FPGA and GPU devices for edge computing applications in B5G MEC
The advent of AI/ML in B5G and Multi-Access Edge Computing will rely on the acceleration of neural networks. The current work focuses on the acceleration of Long Short-Term Memory (LSTM) kernels playing a key role in numerous applications. We assume various LSTM sizes while targeting FPGA and GPU hardware for both embedded and server MEC purposes. ….
Roadrunner: O-RAN-based Cell Selection in Beyond 5G Networks
O-RAN is currently emerging as the way to build a virtualized 5G and beyond Radio Access Network (RAN) that is based on open interfaces and off-the-shelf hardware. O-RAN consolidates the intelligence of several gNodeBs at the Near-realtime RAN Intelligent Controller (RIC) making it more programmable and aware of the mobile users’ surroundings. ….
Sequence Clock: A Dynamic Resource Orchestrator for Serverless Architectures
Function-as-a-service (FaaS) represents the next frontier in the evolution of cloud computing being an emerging paradigm that removes the burden of configuration and management issues from users. This is achieved by replacing the well-established monolithic approach with ….
Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks
Mobile networks are facing an unprecedented demand for high-speed connectivity originating from novel mobile applications and services and, in general, from the adoption curve of mobile devices. However, coping with the service requirements imposed by current and future applications and services is very difficult since ….
Function Placement and Acceleration for In-Network Federated Learning Services
Edge intelligence combined with federated learning is considered as a way to distributed learning and inference tasks in a scalable way, by analyzing data close to where it is generated, unlike traditional cloud computing where data is offloaded to remote servers. …
Beyond 5G/6G KPIs and Target Values. A white paper from the Test, Measurement and KPIs Validation Working Group”
The main objective of this document is to present the current view of the available B5G and 6G KPIs from 5G PPP phase III projects with a focus on projects of the ICT-52 call. This view includes mapping to KPIs previously defined for 5G and evaluating how they might evolve to fit the B5G and 6G visions. ….
5G PPP Architecture Working Group – View on 5G Architecture, Version 4.0
The overall goal of the Architecture Working Group (WG) within the 5G PPP Initiative is to consolidate the main technology enablers and the bleeding-edge design trends in the context of the 5G Architecture. As a result, it provides a consolidated view of the architectural efforts developed in the projects part of the 5G PPP initiative and other research efforts, including standardization.
Machine Learning at the Mobile Edge: The Case of Dynamic Adaptive Streaming Over HTTP (DASH)
Dynamic Adaptive Streaming over HTTP (DASH) is a standard for delivering video in segments and adapting each segment’s bitrate (quality), to adjust to changing and limited network bandwidth. We study segment prefetching, informed by machine learning predictions of bitrates of client segment requests, implemented at the network edge. ….