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

**Position description** **Category** Engineering science **Contract** Internship **Job title** Design of a Reinforcement Learning–Driven Scheduler for Efficient and Frugal Container Orchestration H/F **Subject** Context: Modern distributed systems (such as cloud and edge computing platforms) rely on orchestration frameworks like Kubernetes or Docker Swarm to manage the deployment and execution of applications. A key challenge in these environments is how to schedule containers efficiently, deciding which node should run each task, while balancing performance, energy efficiency, and resource usage. **Contract duration (months)** 6 months **Job Description** **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 **Applicant Profile** **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. **Position location** **Site** Saclay **Job location** France, Ile-de-France **Location** Palaiseau **Candidate criteria** **Prepared diploma** Bac\+5 - Diplôme École d'ingénieurs