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

il y a 4 jours

Saclay, Île-de-France CEA Temps plein

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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