Design of a Reinforcement Learning-Driven Scheduler for Efficient and Frugal Container Orchestr[...]

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

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