LLM-based Optimization Algorithm Selection for High-Performance Networks Orchestration

The rapid growth of Artificial Intelligence (AI) applications, particularly through the widespread adoption of Large Language Models (LLMs), has caused an unprecedented growth in computing and network infrastructures. Current infrastructure expansion cannot keep pace, resulting in suboptimal performance. This creates an urgent need for network automation capable of dynamically orchestrating services and exploiting all available resources. Manual optimization processes are slow, error-prone, and unable to meet the requirements of complex, multi-domain, and data-intensive networks. A fundamental challenge is the absence of a universal optimization algorithm that performs effectively across all scenarios. In this paper, we present preliminary work on an LLM-based optimization algorithm selection framework for multi-domain, high-performance networks orchestration. The proposed framework utilizes LLM-generated descriptive embeddings of algorithms, network state logs, and service requests to identify the most suitable optimization method from a pool of algorithms, curating optimization to the current scenario.