AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies

Imagine you're managing a datacenter, trying to optimize how resources are allocated, workloads are placed, and power is managed. The challenge lies in the complexity of the hardware and software involved, where designing effective control policies can take months of trial and error. Current methods often fall short because they lack a formal structure, fail to transfer knowledge between tasks, and tend to get stuck in local optima, which is what's called inefficiency in policy design. This means that even with advanced AI, the process remains slow and cumbersome. AtumAI offers a solution by formalizing the problem of policy generation. It starts with a clear, machine-readable specification of the task at hand, which allows for a more structured search for solutions. By combining a diffusion model, an evolutionary algorithm, and a surrogate model, AtumAI systematically explores a broader design space, generating and refining policies autonomously. This approach significantly reduces the time needed to onboard new tasks, transforming what used to take months into a matter of writing a description. Compared to traditional methods, AtumAI not only speeds up the process but also consistently outperforms expert-engineered policies across various control-plane tasks. For anyone building in this space, this means you can leverage AI to create more effective and efficient control policies without the lengthy engineering cycles that have been the norm.

AtumAI introduces a structured and systematic approach to policy generation in datacenters, addressing key limitations of existing methods.

The evaluation against expert-engineered baselines across multiple tasks provides solid evidence of AtumAI's effectiveness.