NetBoost: Towards efficient distillation and service differentiation of network information exposure

Abstract

Exposing network information such as distances between end hosts is useful to improve the quality of experiences for network users. Network providers calculate such information based on private topology and routing data and share it with users through well-established protocols such as Application-Layer Traffic Optimization. However, it is usually not intended to expose the original model, which can face scalability, user heterogeneity, efficacy & efficiency challenges.

In this paper, we introduce NetBoost, a system that efficiently distills and differentiates ALTO-based network information to address these challenges concurrently. NetBoost significantly reduces the size of exposed network information by orders of magnitude. In particular, by utilizing a gradient boosting algorithm for classification and regression based on IP prefix matching, NetBoost provides high-order information exposure models, allowing network providers to offer differentiated services to clients with privacy-preserving. Our experimental results demonstrate that NetBoost performs effectively in resource-constrained scenarios, surpassing state-of-the-art lossy compression algorithms and achieving greater accuracy than the XGBoost gradient boosting algorithm, while maintaining a comparable compression rate. Furthermore, in simulation experiments conducted using the real-world networking software BIND, NetBoost achieved 6.96% and 5.2% higher accuracy compared to XGBoost under the same number of rules, with NetBoost’s accuracy set at 95% and 90%, respectively. Additionally, NetBoost reduced resolve time by 44.35% and 52.47%, respectively.