Proactive resource pre-scheduling for campus optical networks using informer under high-concurrency education events

Abstract

The digital transformation of education introduces high-concurrency events (e.g., course registration, online exams) that cause sudden traffic surges in campus optical networks, leading to severe packet loss and delay jitter. Traditional static bandwidth allocation or reactive dynamic bandwidth allocation (DBA) fails to anticipate these bursts. This paper proposes an anomaly traffic prediction and resource pre-scheduling mechanism based on the Informer model. The model fuses historical traffic data with academic calendar events as prior knowledge, using ProbSparse self-attention to capture long-range dependencies and a generative decoder for multi-step prediction. Based on the predicted traffic trends, an SDN/ONU cooperative framework pre-allocates optical bandwidth before congestion occurs. Experiments on one-year real campus network data show that Informer achieves a MAPE of 8.64% (6.5% during peak events), reduces packet loss by 52%, and maintains end-to-end delay below 5 ms. The proposed mechanism requires no hardware upgrades, offering a low-cost, high-precision solution for deterministic low-latency smart campus networks.