Search for: All records

As Cloud's adoption surges across industries, the limitations of its default scheduler, particularly on large scales or for jobs outside of its initial design scope, have become increasingly prominent. While the default schedulers in various cloud platforms were primarily engineered to focus on simple and predictable tasks, reinforcement learning (RL)-based schedulers are attracting attention as they can predict a larger and more diverse cloud environment. Nevertheless, there are practical constraints to the use of RL. Retraining for adaptation is necessary for each new environment, and exploration taken during each training may lead to unexpected performance degradation at runtime. To address these issues, this paper presents Dejavu which combines reinforcement learning with neural networks to learn and resolve scheduling problems more effectively. To tackle the extended training time and performance degradation by unexpected explorations, we apply pretraining using Demonstrations from existing heuristics. This guides the RL agent to explore in a safe and efficient manner. Furthermore, we design a robust reward function to push Dejavu to compete with and eventually outperform, the exploited heuristics and other baselines. The experimental results demonstrate the efficacy of Dejavu, showing remarkable improvements in key metrics. Compared to the default scheduler, it boosts resource utilization by 6 % and shortens scheduling time by 3% during the scheduling period.