Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift

2025-12-14

AI Summary

This paper addresses the joint optimization of training and deployment resources for a model provider serving multiple clients under concept drift and budget constraints: clients support only local inference—not retraining—while model aging, dynamic drift, and communication limitations collectively exacerbate maintenance challenges. To tackle this, we first propose a model-agnostic “resource–drift–deployment” co-optimization framework. We theoretically characterize how DMRL/IMRL-type model aging fundamentally shapes optimal policies. We then develop an optimal control method based on residual lifetime modeling and conduct quasi-convexity analysis, rigorously proving the quasi-convexity of the communication-constrained deployment problem. Finally, we design a near-optimal randomized scheduling policy. Experiments demonstrate that our approach achieves near-optimal client-side inference performance under budget constraints, significantly outperforming existing heuristic methods.

Technology Category

• Machine Learning: Optimization
• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

We study how to allocate resources for training and deployment of machine learning (ML) models under concept drift and limited budgets. We consider a setting in which a model provider distributes trained models to multiple clients whose devices support local inference but lack the ability to retrain those models, placing the burden of performance maintenance on the provider. We introduce a model-agnostic framework that captures the interaction between resource allocation, concept drift dynamics, and deployment timing. We show that optimal training policies depend critically on the aging properties of concept durations. Under sudden concept changes, we derive optimal training policies subject to budget constraints when concept durations follow distributions with Decreasing Mean Residual Life (DMRL), and show that intuitive heuristics are provably suboptimal under Increasing Mean Residual Life (IMRL). We further study model deployment under communication constraints, prove that the associated optimization problem is quasi-convex under mild conditions, and propose a randomized scheduling strategy that achieves near-optimal client-side performance. These results offer theoretical and algorithmic foundations for cost-efficient ML model management under concept drift, with implications for continual learning, distributed inference, and adaptive ML systems.

Problem

• Allocates resources for ML training and deployment under concept drift
• Optimizes training policies based on concept duration aging properties
• Manages model deployment under communication constraints for client performance

Innovation

• Model-agnostic framework for resource allocation under drift
• Optimal training policies based on concept duration aging properties
• Randomized scheduling for near-optimal deployment under constraints