SUPER aims to develop programmable networks and services that are user-centric, reliable, sustainable, and quickly adaptable. This involves creating a modular, open, lightweight, and scalable edge-device platform, utilizing Machine Learning for monitoring and prediction. Additionally, the project focuses on a programmable heterogeneous core network with optimal traffic management and service composition, AI-driven programmable and heterogeneous Radio Access Networks, and advanced algorithms for managing distributed services and resource optimization.
SUPER is part of Spoke 4 – Programmable Networks for Future Services and Media
Project PI: Carla Fabiana Chiasserini
It also worked on data plane programmability tools, such as eBPF and P4, for program composition, in-network traffic analysis, delay control, as well as on the related resource orchestration approaches.
Other activities focused on frameworks to run time-sensitive applications on virtualized resources, implement semantic network slicing for optimized offloading, and optimize resources for O-RAN applications.
The project then worked on the design of models, data-driven optimization algorithms and dynamic control policies for the orchestration of highly distributed, resource-intensive, and latency sensitive applications.
A set of nine guiding use cases were designed and supported by the industrial partners, and were mapped to seventeen innovations that were proposed by the project partners.
- orchestration strategies and architectures, e.g. for service composition in the edge-to-cloud continuum or machine-learning model training;
- frameworks for offloading tasks to the edge-cloud, or for forecasting energy demands in electric vehicle charging infrastructures;
- algorithms for distributed, latency-sensitive applications and federated learning support;
- O-RAN application optimization, targeting energy-saving and quality of service;
- key technologies and software tools for advanced Linux-kernel networking programmability, real-time traffic feature extraction and in-network measurements, SD-WAN tunnel quality measurement and selection, and edge computing resource planning.
A number of use cases were proposed by industrial partners, including optimizing mobile video delivery, end-to-end orchestration for managing 4G/5G private networks, interoperability with 5G core network.
These use cases were mapped to seventeen innovations that were proposed by the project partners, focusing on service provisioning and service/resource orchestration on heterogeneous infrastructures, including open radio access networks, efficient monitoring and network traffic analysis tools, data path acceleration.
- Università di Roma, Tor Vergata
- Consiglio Nazionale delle Ricerche
- Politecnico di Milano
- Politecnico di Torino
- Alma Mater Studiorum - Università di Bologna
- Università degli Studi di Catania
- Università degli Studi di Napoli - Federico II
- Università degli Studi Mediterranea di Reggio Calabria
- Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)
- TIM S.p.A.
- Vodafone Italia S.p.A.
- ITALTEL S.p.A.
- Hewlett Packard Enterprise (HPE)
Scientific peer-reviewed publications in journals/magazines:
Expected: ≥ 8
Accomplished: 13
Readiness level: 1.625/0.40 > 100%
Scientific peer-reviewed publications at international conferences: Expected: ≥ 12
Accomplished: 23
Readiness level: 1.92/0.40 > 100%
Scientific peer-reviewed joint publications co-authored by at least two partners:
Expected: ≥ 30%
Accomplished: 7 out of the expected 20 = 35%
Readiness level: 1.17/0.40 > 100%
Seminars, invited/keynote talks and other dissemination events:
Expected: ≥ 2
Accomplished: 4 invited talks + 7 keynotes
Readiness level: 5.5/0.40 > 100%
Project Meetings:
Expected: ≥ 6
Accomplished: 3
Readiness level: 0.50/0.40 > 100%
Open source contributions (including data sets):
Expected: ≥ 1 available data sets, ≥ 3 FOSS communities
Accomplished: 1 data set
Readiness level (data set): 1/0.40 > 100%
Standardization contributions:
Expected: ≥ 1
Accomplished: 1
Readiness level: 1/0.40 > 100%
Other KPIs:
-Scientific peer-reviewed joint publications co-authored with industry: 2
-Scientific peer-reviewed joint publications co-authored with international collaborators: 13
-Master Theses with acknowledgement to SUPER: 8
-Courses covering SUPER relevant topics: 3
-Applications for national/international funding: 3
-PhD Theses with acknowledgement to SUPER: 7
-International visitors (PhD students/researchers): 2
-Expected: First release of the lightweight resource provisioning platform and experimental evaluation; and first release of the AI-based network intelligence solution.
-Accomplished: Software regarding a selection of modules of the platform was released privately alongside deliverable 2.1. This included part of the service placement and the monitoring intelligence. Therefore, this can be considered as a successful “first release.” Completed by 31/12/2023.
-Readiness level: 1.00/0.40 = 2.50
M3.1:
-Expected: Tools for programmable dataplanes: report on the design of tools.
-Accomplished: The final design of the toolkit has been completed, and an initial prototype for some of the tools has been developed. In addition, a set of preliminary results was documented in deliverable D3.1. Consequently, this can be considered as a successful “first release.” Completed by 31/12/2023.
-Readiness level: 1.00/0.40 = 2.50
M4.1:
-Expected: Design of data-plane interfaces and orchestration enablers for advanced, heterogeneous RANs.
-Accomplished: T4.1 and T4.2 outputs have been published in D4.1, which reports data-plane interfaces and orchestration enablers for advanced and heterogeneous RANs. The release of D4.1 marks the achievement of M4.1. Completed by 31/12/2023.
-Readiness level: 1.00/0.40 = 2.5
M5.1:
-Expected: Design of model and data-driven algorithmic solutions for global optimization and dynamic control agents. Initial evaluation, comparison, and analysis of combination of model and data-driven solutions, driving refinement plan. Definition of relevant application/vertical use cases.
-Accomplished: Competitive set of optimization algorithms and control policies designed. Relevant use cases identified. Results reported in deliverable D5.1. Completed by 31/12/2023.
-Readiness level: 1.00/0.40 = 2.5
M6.1:
-Expected: Definition of use cases, scenarios and requirements. Five Vertical Scenarios have been defined from which nine Guiding Use Cases were stemmed, covering various areas of programmable networks.
-Accomplished: completed on 31/08/2023.
-Readiness level: 1.00/0.40 = 2.5
Collaboration proposals: for any proposal of collaboration within the project please contact the project PI.
SUPER News: