Explore a conference talk on improving coflow scheduling for enhanced data-intensive application performance. Learn about Philae, a novel online coflow scheduler that leverages the spatial dimension of coflows to reduce overhead in coflow size learning. Discover how this approach utilizes flow sampling to estimate average flow size and implements Shortest Coflow First scheduling. Examine the robustness of sampling-based learning to flow size skew and its scalability benefits. Analyze comparative performance results against prior art Aalo, showcasing significant reductions in coflow completion time across various testbed sizes and production cluster traces. Gain insights into the technical aspects of coflow scheduling, including challenges, practical issues, and comparisons with other approaches like Coda.