Presentation
LLM-Based Optimization Algorithm Selection for High-Performance Network Orchestration
SessionINDIS SCinet Theater talks
DescriptionIn this work, we propose a large language model (LLM)-based framework for optimization algorithm selection to address the growing complexity of high-performance, multi-domain network orchestration. The proposed solution introduces a context-aware abstraction layer where LLMs analyze network logs, service requests, and algorithm metadata to dynamically select the most suitable optimization strategy. We validate our framework through a prototype deployed on the FABRIC FAB international testbed and through simulations across diverse scenarios (eMBB, URLLC, mMTC, V2X, AR/VR), showing that LLM-driven selection achieves higher success rates and SLA compliance while balancing efficiency, accuracy, and inference latency. Our preliminary results demonstrate the feasibility of this method and highlight its potential to enable scalable, adaptive, and privacy-preserving orchestration.
Event Type
SCinet
TimeThursday, 20 November 202510:20am - 10:40am CST
LocationBooth 3537 - SCinet Theater