PhD “Supervised Learning of Availability for Digital Twin of Infrastructures” M/F

Job title:

PhD “Supervised Learning of Availability for Digital Twin of Infrastructures” M/F

Company:

Orange

Job description

about the roleYour role is to conduct a thesis on: « Supervised Learning of Availability for Digital Twin of Virtualization Infrastructures. »Global context and problematic of the subject
Hosting interruption-sensitive applications on distributed data centers, also known as “edge computing,” requires the establishment of multi-site protection schemes and a deep understanding of the associated failure risks [1], [2]. Furthermore, the recent development of Digital Twins [3] for the automated operation of infrastructure networks necessitates the creation of network models and the implementation of automated information collection to synchronize the state of the digital twin with that of the infrastructure elements. The dynamic orchestration of virtualization containers (Kubernetes) and the associated monitoring architectures (Prometheus) open new perspectives for information collection and automatic adaptation in the face of potential infrastructure degradation [4], [5], [6].Scientific Objective
The objective of this thesis is to model the failure statistics of infrastructure elements and to develop supervised learning of its parameters, with the aim of compressing measurements taken by infrastructure probes for a Digital Twin.Challenges to Overcome
1) Avoid the loss of short failure events during historical data collection through subsampling.2) Aggregate rare failure event statistics from a set of infrastructures in production.3) Build an availability model that generates synthetic parameters (availability, state probability).4) Prototype an exporter to be integrated into a Prometheus measurement chain.References
[1] I. Narayanan, Right-sizing Geo-distributed Data Centers for Availability and Latency, 2017[2] K. Sayad, Interdependency-Aware Resource Allocation for High Availability of 5G-enabled Critical Infrastructures Services, 2022[3] A. Thelen, A Comprehensive Review of Digital Twin – Part 1: Modeling and Twinning Enabling Technologies, 2022[4] D. Tazzioli, Stateful Service Migration Support for Kubernetes-based Orchestration in Industry 4.0, 2024[5] T. Trung Le, Hidden Markov Models for diagnostics and prognostics of systems under multiple deterioration modes, 2014[6] A. Samir, Self-Adaptive Healing for Containerized Cluster Architectures with Hidden Markov Models, 2019about youExpected expertise (scientific and technical) and personal qualitiesVery good skills in applied mathematics (probability, statistics, availability computation, supervised learning, algorithmic complexity computation, Markov chains, …).Required background (master, engineer degree, scientific and technical area …)Master degree in informatics and algorithmics.

Expected working experience (trainings, …)Training in a Research and Development laboratory for telecommunication or informatic systems would be appreciated.additional informationWhat is the added value of this offer?This thesis will provide the doctoral candidate with both theoretical and technological experience, as it will involve applying supervised learning concepts to network-deployed virtualization infrastructures.The supervised learning conducted initially on a development platform may be extended to larger-scale platforms, notably through software development that may be carried out by an intern or by the doctoral candidate himself.At the end of this thesis, the doctoral candidate will have gained experience in the operation and degraded modes of virtualization infrastructures, as well as their use for deploying virtualized network functions by a telecommunications operator.departmentThe ambition of the Innovation Division is to advance Orange’s innovation and strengthen its technological leadership by leveraging our research capabilities to foster responsible innovation that serves humanity, inform the Group’s long-term strategic choices, and influence the global digital ecosystem. The Innovation Division brings together 6,000 employees dedicated to research and innovation worldwide, including 740 researchers.Within the Innovation entity, in the CISS (Cloud Infrastructure Solutions and Services) department, responsible for providing private cloud services for Orange subsidiaries and comprises 180 engineers in France, Romania, and India, you will be integrated into the NAVI team. This team is in charge of studying the evolution of automated deployment methods for hosting infrastructures for network function virtualization. It includes research engineers at the forefront of open-source software development in collaboration with the Linux Foundation Europe, focusing on hardware performance and energy efficiency in partnership with hardware manufacturers. It also includes network architects and integrators who implement experimental platforms and provide support for production deployment in Orange subsidiaries.contractThesis

Expected salary

Location

Lannion, Côtes-d’Armor

Job date

Sat, 22 Mar 2025 23:29:48 GMT

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