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Design Of a Reinforcement Learning-Driven Scheduler For Efficient And Frugal Container Orchestr[...]
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Design of a Reinforcement Learning–Driven Scheduler for Efficient and Frugal Container Orchestration H/F Category Engineering science Contract Internship Job location Saclay 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. 6 months 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 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. 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 Interest in machine learning and distributed systems, Curiosity, creativity, and strong problem-solving abilities. #J-18808-Ljbffr
