arXiv subjects

Paul Almasan

Publications and source records attributed to Paul Almasan.

21 records · Page 2Linked to original sources

arXiv2021

The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge'', an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020''. We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

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arXiv2020

RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN

Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE=15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.

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arXiv2018

Securing the Control-plane Channel and Cache of Pull-based ID/LOC Protocols

Pull-based ID/LOC split protocols, such as LISP (RFC6830), retrieve mappings from a mapping system to encapsulate and forward packets. This is done by means of a control-plane channel. In this short paper we describe three attacks against this channel (Denial-of-Service and overflowing) as well as the against the local cache used to store such mappings. We also provide a solution against such attacks that implements a per-source rate-limiter using a Count-Min Sketch data-structure.

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