Design of a Reinforcement Learning–Driven Scheduler for Efficient and Frugal Container Orchestration H/F

Description de l'offre : Objective: The goal of this internship is to design and evaluate a new intelligent scheduling strategy using reinforcement learning (RL). The idea is to enable the system to learn how to make smarter scheduling decisions over time, optimizing container placement and sizing, dynamic resource allocation, response time and energy consumption and even inter-container dependencies such as shared data or communication patterns. Your missions: During this internship, you will: Explore and understand the orchestration framework developed within the team. Conduct a state-of-the-art study on RL-based scheduling in cloud and distributed environments. Design, implement, and train a new RL-based scheduler. Develop a feature extraction module to characterize container behavior and guide the RL agent’s decisions. Evaluate your approach through experiments and benchmark comparisons

Profil du candidat : Profile sought We are looking for a motivated student in the final year of a Master’s or Engineering program in Computer Science, Artificial Intelligence, or a related field, with: Good programming skills (Python preferred). Interest in machine learning and distributed systems. Curiosity, creativity, and strong problem-solving abilities.

Diplome préparé : Bac+5 - Diplôme École d'ingénieurs