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

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

13/02/2026 Contrat doctoral

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

Elastic Optical DataCenter Networks, Natural Disaster Zoning, Risk Evaluation, Resilient Routing, Scheduling for data evacuation ,Network Optimization

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 is how to precisely track the disaster zones and/or predict the natural disasters, while the second phase is how to build resource-efficient disaster-resilient strategies to minimize or even avoid communication interruptions and huge data loss. (1) For the first phase, researchers in computer science often adopt a rough estimation of disaster risk zoning in the literature. Instead, this PhD thesis will explore 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, we then address the resilient network routing and data evacuation against failures induced by the disasters. Before a disaster, proactive protection mechanisms, e.g., pre-allocate the alternative transmission paths in case of failure occurrence, could be an efficient solution against failures induced by unpredictable disaster as earthquakes. On the other hand, mitigation strategies can also be helpful in the short time frame between the reception of a disaster alert and the actual occurrence of a disaster, for instance evacuating critical data/service just before an incoming flood actually reaches the region where the network infrastructure is located. Hence, optimization problems like resilient routing and scheduling for data evacuation as well as the resilient content placement must be investigated, which aim at maximizing the number of migrated VMs and minimizing the service downtime at the same time. As these optimization problems are generally NP-hard, efficient optimization techniques like integer linear programming, (meta)-heuristic as well as deep reinforcement learning will be very helpful. For more information about the PhD subject, please try to refer to its page on ADUM (will be available shortly).

01/10/2026

FR Agorantic

This PhD thesis will be co-supervised by a researcher from the Computer Science Laboratory (LIA) and a researcher from the Geography 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.

France

This PhD thesis is primarily considered under the Computer Science discipline (Doctoral School ED536). 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 in modeling and/or mathematics (such as operations research, AI, machine learning, or applied mathematics for social sciences) and keen interest in interdisciplinary research (computer science, quantitative geography). Experience in geography and/or network analysis would be a plus.