Handling Data Plane Program Deployment Dynamics with High-Quality Generative Diffusion Models

Deploying data plane programs across the network is typically formulated as a mixed-integer programming task, leading to a long execution time. In response, existing studies carefully tailor heuristics for specific task properties such as objectives. However, they suffer from poor solution quality under dynamic task deployment since they overfit specific task properties. Recently, generative diffusion models have been widely adopted in network optimizations due to their strong adaptability and generalization. Accordingly, in this poster, we propose a diffusion model-based framework for data plane program deployment tasks. Our key idea is to leverage the reverse denoising process of diffusion models to react to dynamic task changes at runtime while maintaining high solution quality. Preliminary results on our testbed show that we reduce latency by 66.67% and resource overhead by 58.62% during dynamic deployment.