2023 LINCS Annual Workshop

Speaker : LINCS researchers + members of the Scientific Committee
Date: 05/07/2023 - 06/07/2023
Time: 9:30 am - 6:00 pm
Location: Amphi Rose Dieng


The LINCS organizes its Annual Workshop with the Scientific Committee.

It will be a 2-day workshop with:

  • LINCS members “scientific highlights”
  • Scientific Committee members invited talks
  • PhD students “elevator pitch” session + posters

We’ll have coffe-breaks just outside the Amphi Rose Dieng and we’ll have lunch at the cantine of Télécom-Paris.

On the evening of Wednesday July 5th we’ll have a shuttle bringing us from Palaiseau to Jussieu (Paris 5e arr.) for a dinner cocktail on top of the Zemansky Tower.

The official program will be published in early June.

Confirmed talks by members of the Scientific Committee who will be in Palaiseau:

  • Prof. Marco Ajmone Marsan (Politecnico di Torino)
  • Prof. Roch Guerin (Washington University in Saint Luis)
  • Prof. Nick Bambos (Stanford University)

LINCS members scientific highlights:


Title: Quantum networking at LINCS

Abstract: Quantum networking is an emerging scientific domain. Quantum networks are distributed systems of quantum devices that utilize fundamental
quantum mechanical phenomena such as superposition, entanglement, and
quantum measurement to achieve capabilities beyond what is possible with
classical networks. The potential applications of quantum networks are
quantum cryptography (Quantum Key Distribution), quantum consensus,
privacy-preserving quantum computing or distributed quantum computing
applications. In this talk, we will describe the past, current and
future activities at LINCS related to this prospective research domain
on quantum networking.



Title: Corsort: An anytime sorting algorithm

Abstract:  An anytime algorithm is an algorithm that is able to give an estimation of the result after each step of execution. Ee study the problem of anytime sorting. We consider that each comparison is a step of execution, and we measure the proximity between the estimation and the sorted list with the Kendall tau distance. We present Corsort, a family of anytime sorting algorithms using estimators. By simulation, we show that a well-configured Corsort has a quasi-optimal termination time, and gives better estimations than the other algorithms of our benchmark.



Title: Causal Reasoning for configurable network systems

Abstract: With the rapid advancement in B5G, IoT, and network softwarization, modern ICT network systems are becoming increasingly diverse, disaggregated, and complex. Consequently, understanding and managing these systems has thus become a daunting task. Although AI/ML techniques can lend sound predictive services, they need more robust, counterfactual reasoning and decision-making. In this talk, I will present our ongoing work exploring causal research for network diagnosis and optimization. Our study focuses on real-world systems capable of processing network traffic at extremely high speed, e.g., 10-100 Gbps. We take two paths to approach causal reasoning: i) causal discovery from observational/interventional data and ii) causal inference for insight extraction. The ultimate goal is to implement a generic, robust, production-ready toolset that can effectively uncover performance bottlenecks and guide optimizations for different network systems.



Title: fAST: How to find relevant regular expression from a small set of positive examples

Abstract : Regular expressions are ubiquitous in computer science but are cumbersome to code. In this work, we present a new algorithm, named fAST (find Abstract Syntax Tree), that infers a regular expression from a small set of positive examples. Its main strength (with respect to the start of the art) resides in its ability to perform this inference without counter examples.

Authors: Maxime Raynal (LIG, NBLF), Marc-Olivier Buob (NBLF), Georges Quénot (LIG)



Title: Predicting network hardware faults through layered treatment of alarms logs

Abstract: Maintaining and managing ever more complex telecommunication networks is an increasingly complex task, which often challenges the capabilities of human experts. There is a consensus both in academia and in the industry on the need of enhancing human capabilities with sophisticated algorithmic tools for decision-making, with the aim of transitioning towards more autonomous, self-optimizing networks. We aim at contributing to this larger project. We tackle the problem of detecting and predicting the occurrence of faults in hardware components in a radio access network, leveraging the alarm logs produced by the network elements. We design a range of algorithmic solutions, and we test them on real data, collected from a major telecommunication operator. We are able to predict the failure of a network component, with satisfying precision and recall.



Title: Inference of network characteristics using non-invasive data exploration
Abstract: Recent years witnessed a trend of “softwarization” of network components. Instead of static, expensive hardware, operators have started to adopt a more flexible approach based on Virtual Network Functions. This paradigm (aka Network Function Virtualization) advocates implementing network middleboxes such as firewalls or NATs as pieces of software to be deployed and executed on commercial off-the-shelf (COTS) hardware. This has boosted the development of several packet processing frameworks and software switches, which show nowadays multi 10-Gbps capabilities in COTS servers. In parallel, network systems are increasingly adopting machine learning (ML) techniques to solve complex networking tasks such as traffic classification or resource allocation.

As ML techniques require a large amount of data to be collected for both training and validation, when done in software, such measurements can highly affect the measured values, thus biasing the collected data. The intensity of this becomes stronger when measurements are taken close to the data path. Second, even after the training phase, complex model calculations may require dedicated hardware such as external GPUs or custom hardware designed for neural network processing such as TPUs or VPUs.
In this talk, we present a novel approach based on non-invasive data collection relying on pure software.
Our methodology consists in (i) low-impact network measurements with both direct and indirect observations; (ii) inference/predictive modeling of a complete system with ML and/or classical approaches; (iii) deployment of low-resource models for runtime query/action operations and automated recovery. The project (acronym: IONOS-DX) has received an individual grant from the ANR (French Agency of Research).

SWAPNIL DHAMAL (Télécom-SudParis)