About ALLEGRO ‘Agile ultra low energy secure networks’
ALLEGRO aims at designing and validating a novel end-to-end sliceable, reliable, and secure architecture for next-generation optical networks, achieving high transmission/switching capacity
- with 10 Tb/s for optoelectronic devices and 1 Pbt/s for optical fiber systems
- low power consumption/cost
- with > 25% savings
- and secure infrastructures and data transfers.
The architecture relies on key enabling innovations:
- smart, coherent transceivers exploiting multi-band & multi-fiber technologies for P2P and P2MP applications, based on e.g., high-speed plasmonic modulators/photodetectors and programmable silicon photonic integrated waveguide meshes;
- loss-less, energy-efficient transparent photonic integrated optical switches, eliminating OEO conversions, e.g., with on-chip amplification in the O-band for datacom applications;
- a consistent approach to security, in terms of functional/ protocol architectures and communications, further improving QKD systems, enabling optical channel co-existence and researching on quantum-resistant (post-quantum) cryptography, developing systems based on physically unclonable functions; and
- a scalable AI/ML assisted control and orchestration system, responsible for autonomous networking, dynamic and constrained service provisioning, function placement and resource allocation, leveraging devices increasing programmability and overall network softwarization.
To achieve the target objectives and KPIs, ALLEGRO has defined a clear methodology ending in ambitious demonstrators. The consortium includes a good balance of industry and research/academia with know-how in complementary fields.
The results of ALLEGRO will be disseminated in leading conferences, events, and high-impact journals. They will have a concrete and measurable economic and social impact, contributing towards achieving key European objectives, reinforcing European leadership and digital sovereignty in the ongoing digital and green transition.
Project News
Advancing Resource Allocation with Multi-Agent Rollout ALLEGRO Project Update
In the context of the ALLEGRO Project , we developed a powerful Multi-Agent Rollout Mechanism to further optimize microservice placement across distributed edge/cloud infrastructures. 🌍⚙️ 🧠 Why Rollout?Standard heuristics can be fast but suboptimal. Exact methods? Too...
Project NEWS: ALLEGRO Greedy Resource Allocation Mechanism
As part of the #ALLEGRO project , we developed an intelligent Greedy Resource Allocation Mechanism for efficient microservice placement across edge/cloud infrastructure. 🌐⚙️ 🔍 What does it do?Our heuristic approach balances performance and resource efficiency by...
Containerized Applications in the Edge-Cloud Continuum: A Smarter Way Forward
As digital infrastructure evolves, traditional monolithic applications are rapidly reaching their limits. With the rise of 5G/6G, virtualization, and optical networks, application design must adapt to meet increasing Quality of Service (QoS) demands. Enter the...
Lexicographic Optimization in Action: Prioritizing What Matters in Multi-Objective Problems
In our latest work on the Allegro project, we applied lexicographic optimization to tackle complex combinatorial problems involving conflicting objectives like cost, latency, and availability. Rather than optimizing all objectives at once, lexicographic optimization...
Allegro Project Update: Performance Evaluation of DRL-based Storage Allocation
As part of our ongoing work in intelligent distributed storage, we evaluated the performance of our Deep Reinforcement Learning (DRL) mechanism during the inference stage, comparing it against an optimal solver across various optimization objectives. 🧠 Figure 26...
Allegro Project Update: Training Our Deep Reinforcement Learning Mechanism
Before deploying our DRL-powered distributed storage allocation system, we invested in an intensive training phase to ensure our agent’s efficiency and stability. 🎯 By carefully tuning the hyperparameters α (learning rate), β (latency weight), and γ (discount factor),...
Machine Learning Function Orchestrator (MLFO) in ALLEGRO
The ALLEGRO project introduces the MLFO, a dedicated orchestrator for managing distributed AI/ML pipelines across the network. Unlike traditional intent-based systems focused on single network entities, MLFO enables a global, scalable, and reconfigurable AI/ML...
AI/ML Agents in the ALLEGRO Project
In the ALLEGRO approach, AI/ML agents are designed as intelligent software entities responsible for managing and optimizing specific services. These agents are built with a modular architecture to ensure agility, security, and seamless integration. 🔍 Key Components:...
ALLEGRO Project: Transport Network Slicing for 5G & Beyond
As 5G services evolve and diversify, network slicing emerges as a foundational technology—enabling transport networks to deliver customized, on-demand, and performance-assured services across multiple domains and layers. In the ALLEGRO architecture, Transport Network...
ALLEGRO Project: Smart Orchestration for Multidomain Optical Networks
In today’s complex networking environments, efficient end-to-end service delivery demands multisegment and multidomain orchestration. Within the ALLEGRO project, we are pioneering a comprehensive framework to manage intent-based requests across multiple...