The emergence of ML in various cloud system management tasks (e.g., workload autoscaling and job scheduling) has become a core driver of ML-centric cloud platforms. However, there are still numerous algorithmic and systems challenges that prevent …
Foundation models (FMs) are machine learning models that are trained broadly on large-scale data and can be adapted to a set of downstream tasks via fine-tuning, few-shot learning, or even zero-shot learning. Despite the successes of FMs in the …
Multi-agent reinforcement learning (MARL) has primarily focused on solving a single task in isolation, while in practice the environment is often evolving, leaving many related tasks to be solved. In this paper, we investigate the benefits of …