Towards Intelligent Resource Allocation in Highly-Distributed Content Delivery Networks Using Graph Neural Networks

We introduce GNN4Alloc, a learning-based frame-work for resource allocation in highly distributed Content De-livery Networks (CDNs). Focusing on the core challenges of content placement and routing, GNN4Alloc leverages Graph Neural Networks (GNNs) to enhance decision-making efficiency in dynamic and...

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Bibliographic Details
Published in:2025 9th Network Traffic Measurement and Analysis Conference (TMA) pp. 1 - 4
Main Authors: Vanerio, Juan, Casas, Pedro, Schmid, Stefan
Format: Conference Proceeding
Language:English
Published: IFIP 10.06.2025
Subjects:
Focusing
Graph neural networks
Heuristic algorithms
Network topology
Optimization
Representation learning
Resource management
Routing
Scalability
Telecommunication traffic
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Summary:We introduce GNN4Alloc, a learning-based frame-work for resource allocation in highly distributed Content De-livery Networks (CDNs). Focusing on the core challenges of content placement and routing, GNN4Alloc leverages Graph Neural Networks (GNNs) to enhance decision-making efficiency in dynamic and large-scale environments. Building on prior work that employs mathematical optimization and heuristic algorithms, we reformulate these problems using graph representation learning, leveraging the bipartite nature of content-to-node assignment and routing decisions in CDN resource allocation. The framework incorporates GNN-based modules - including neural algorithm executors and constrained optimization layers - to develop adaptive allocation policies that generalize across diverse network topologies and demand profiles. By doing so, GNN4Alloc aims to improve both the scalability and solution quality of content allocation strategies, contributing to the broader goal of advancing GNN-based control in distributed systems.
DOI:10.23919/TMA66427.2025.11097019