Federated Transfer Component Analysis Towards Effective VNF Profiling
This addresses the challenge of efficient and private VNF profiling for network orchestration, though it appears incremental as it combines existing techniques like GANs and federated learning.
The paper tackles the problem of profiling Virtual Network Functions (VNFs) for resource consumption prediction by proposing a Federated Transfer Component Analysis (FTCA) method that transfers knowledge from well-profiled to lack-profiled VNFs while preserving data privacy, resulting in a 38.5% decrease in RMSE and up to 68.6% improvement in R-squared metrics.
The increasing concerns of knowledge transfer and data privacy challenge the traditional gather-and-analyse paradigm in networks. Specifically, the intelligent orchestration of Virtual Network Functions (VNFs) requires understanding and profiling the resource consumption. However, profiling all kinds of VNFs is time-consuming. It is important to consider transferring the well-profiled VNF knowledge to other lack-profiled VNF types while keeping data private. To this end, this paper proposes a Federated Transfer Component Analysis (FTCA) method between the source and target VNFs. FTCA first trains Generative Adversarial Networks (GANs) based on the source VNF profiling data, and the trained GANs model is sent to the target VNF domain. Then, FTCA realizes federated domain adaptation by using the generated source VNF data and less target VNF profiling data, while keeping the raw data locally. Experiments show that the proposed FTCA can effectively predict the required resources for the target VNF. Specifically, the RMSE index of the regression model decreases by 38.5% and the R-squared metric advances up to 68.6%.