Grandet: Cost-aware Traffic Scheduling Without Prior Knowledge in SD-WAN

The rapid growth of traffic demands on wide-area networks (WANs) has resulted in escalated transmission costs for cross-national enterprises. Many researchers have proposed traffic scheduling methods that can effectively reduce transmission costs and improve network performance. However, the majority of research in this field assumes that traffic demands and network link quality are known in advance, disregarding the impact of information agnostic. While some works try to obtain this knowledge through prediction, they lack awareness of prediction errors, which makes it difficult for their scheduling strategies to achieve theoretical results. In this paper, we propose a novel scheduler Grandet that aims to reduce transmission costs without any prior knowledge. First, instead of requiring prior knowledge or accurate prediction, Grandet determines the intervals of flow sizes and link quality parameters through confidence-based Bootstrap method combined with neural network model, thus quantifying the uncertainty of these information. Then, we design a cost-aware online traffic scheduling framework using the uncertainty intervals from interval determination to optimize the cost minimization problem. Through rigorous theoretical analysis, we prove the approximate optimality of Grandet in minimizing transmission costs. Trace-driven and large-scale simulations show that Grandet successfully reduces transmission costs by over 23%, reduces deadline miss rate by over 31%, and reduces Service Level Agreement (SLA) dissatisfaction rate by over 37%.