AI infrastructure framework models diverse system states and decisions
AID: A Framework for AI Infrastructure Dynamics
Distributed, Parallel, and Cluster ComputingMachine LearningPerformance
Summary
Managing AI systems involves juggling physical machines, computing, networks, and user demand, all interacting over time. The authors present a framework called AID that models these complex, intertwined parts to help predict and manage how AI services perform. They also highlight the limits of prediction when information is incomplete, and how to simplify the system's state without losing important details. Their work aids in understanding and controlling AI infrastructure decisions across different conditions.
What this means in practice
- •For infra engineers: Design models that better predict AI system performance and adapt infrastructure management policies under different operating conditions.
- •For network operators: Use the framework to understand how service demand reacts to network actions and improve resource allocation decisions.
A theory result. No direct application yet.
Authors
Abi Aryan
Abstract
A useful model of AI inference infrastructure must specify the system state, the information available to an observer, and the decisions the model is intended to support. We introduce AID (AI Infrastructure Dynamics), a framework for describing this learning problem across coupled physical, computational, networking, and serving processes. The formulation allows structured and variable-size state, asynchronous observations, multiple physical timescales, and demand that responds to service. We distinguish representations that support prediction under an existing policy from those that preserve service outcomes under changed actions, and separate both from identifying intervention responses. Two analytical results describe a lower bound on prediction error when available observations cannot distinguish models and a sufficient condition for exact controlled state reduction. These results apply established information and state-abstraction principles to AI infrastructure. We then describe a validation protocol for cache representations, workload histories, measurement availability, and imposed actions.