OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization

Problem
This paper addresses the limitations of existing network planning optimization frameworks, which primarily rely on mixed integer programming (MIP) solvers, heuristics, and deep reinforcement learning (DRL) models. These methods often struggle with adaptability to diverse and dynamic user intents, resulting in a trade-off between execution time and optimality. The authors highlight the need for a more flexible solution that can effectively interpret heterogeneous user preferences and optimize planning decisions accordingly. This work is presented as a preprint and has not undergone peer review.

Method
OmniPlan introduces a novel adaptive framework that leverages a large language model (LLM) to interpret user intents expressed in natural language, converting them into a unified user-preference vector. The core architecture employs a mixture-of-experts model, integrating specialized components: MIP solvers, heuristics, and DRL models. This architecture allows for dynamic selection of the most appropriate expert based on the user intent, facilitating timely and near-optimal planning decisions. Additionally, a DRL-based expert configuration module fine-tunes the optimization objective weights to align with user-specific preferences. The framework is evaluated in the context of distributed machine learning (ML) inference tasks, where it offloads various ML models onto a network of hardware devices.

Results
The evaluation of OmniPlan on a real-world testbed demonstrates significant performance improvements. Specifically, the framework achieves a latency reduction of up to 97.8% compared to baseline methods, while also decreasing network device resource consumption by up to 11.5%. These results indicate that OmniPlan not only meets the timeliness requirement but also approaches optimality in planning decisions for ML inference tasks, outperforming traditional optimization techniques.

Limitations
The authors acknowledge that while OmniPlan shows promising results, its performance may vary depending on the complexity of user intents and the specific characteristics of the network environment. Additionally, the reliance on LLMs for intent interpretation may introduce challenges related to model accuracy and robustness, particularly in ambiguous or poorly defined user requests. The paper does not address potential scalability issues when applied to larger or more complex networks, nor does it explore the implications of varying hardware configurations on performance.

Why it matters
The implications of OmniPlan extend beyond network planning optimization, as it provides a framework for integrating user preferences into decision-making processes across various domains. Its adaptive nature and ability to handle diverse intents can significantly enhance the efficiency of resource allocation in real-time systems. This work contributes to the ongoing discourse on improving the adaptability of AI systems in complex environments, as published in arXiv.