Robust Online Coflow Scheduling from Predictions

In this paper, we introduce an online scheduling framework for minimizing the total weighted average completion time of coflows, in scenarios where both flow sizes and release times are unknown. Instead of relying on exact flow sizes, the framework leverages potentially inaccurate predictions. At each decision point, it selects a subset of coflows by solving a Minimum Unscheduled Weight Problem (MUWP), and then applies a variant of the Sincronia algorithm-adapted to operate directly on the predicted sizes. We prove that the proposed approach achieves provably strong competitive guarantees, and extensive simulations demonstrate that it outperforms baseline methods both on average and in worst-case instances.