RISK ZONING AND RESILIENT ROUTING FOR URBAN ELASTIC OPTICAL INTER DATA-CENTERNETWORKS

In this PhD thesis, we aim at achieving realistic disaster resilience for Elastic Optical Inter-Data Center Networks (EO-DCNs), where two critical problems arise. The first phase ishow to precisely track the disaster zones and/or predict the natural disasters, while the second phase is how to build resource-efficientdisaster-resilient strategies to minimize or even avoid communication interruptions and huge data loss. (1) For the first phase, theresearchers in computer science often adopt a rough estimation of disaster risk zoning in the literature. Instead, this PhD thesis willexplore accurate geograhy methodologies for floodings and earthquakes zoning, leveraging advanced modeling techniques (e.g,machine learning, LLM) and disaster databases to improve resilience and response strategies. (2) Leveraging the obtained risk zones, wethen address the resilient network routing and data evacuation against failures induced by the disasters. Before a disaster, proactiveprotection mechanisms, e.g., pre-allocate the alternative transmission paths in case of failure occurrence, could be an efficient solutionagainst failures induced by unpredictable disaster as earthquakes. On the other hand, mitigation strategies can also be helpful in theshort time frame between the reception of a disaster alert and the actual occurrence of a disaster, for instance evacuating criticaldata/service just before an incoming flood actually reaches the region where the network infrastructure is located. Hence, optimizationproblems like resilient routing and scheduling for data evacuation as well as the resilient content placement must be investigated, whichaim at maximizing the number of migrated VMs and minimizing the service downtime at the same time. As these optimization problemsare generally NP-hard, efficient optimization techniques like integer linear programming, (meta)-heuristic as well as deep reinforcementlearning will be very helpful.   For more information about the PhD subject, please try to refer to its page on ADUM.

https://adum.fr/as/ed/voirproposition.pl?site=adumR&matricule_prop=62607#version

This PhD thesis will be co-supervised by a researcher from the Computer Science Laboratory (LIA) and a researcher from theGeography Laboratory (UMR ESPACE). The candidate will conduct regular visits to both laboratories, which are located in the beautiful city of Avignon, in the south of France.

This PhD thesis is primarily considered under the Computer Science discipline (Doctoral School ED536) and, as a secondary option,under Quantitative Geography (ED537). The final classification will depend on the candidates' qualifications and profiles. The research will be conducted either the LIA laboratory or at the UMR ESPACE laboratory, with regular visits planned to both throughout the project.

We encourage applications from second-year master’s students or final-year engineering students with a strong background inmodeling and/or mathematics (such as operations research, AI, machine learning, or applied mathematics for social sciences) and akeen interest in interdisciplinary research (computer science, quantitative geography). Experience in geography and/or network analysis would be a plus.