Datacenter network topology contains multiple paths between server machines, with each path assigned aweight. Software switches perform traffic splitting, an essential networking operation in datacenters. Previous studies leveraged software switches to distribute network connections across paths, under the assumption that the software switches accurately divide connections according to path weights. However, we reveal that current traffic splitting techniques exhibit significant inaccuracy and resource inefficiency. Consequently, real-world datacenter services (e.g., deep learning) experience ∼2.7× longer communication completion times than the ideal scenario. To address these problems, we propose VALO, a new traffic splitting technique for software switches, for 1) high accuracy and 2) resource-efficiency. For the goals, we introduce new concepts: score graph and VALO gravity. We implement VALO using the de-facto software switch and evaluate it thoroughly. On average, VALO achieves ∼34.8× better accuracy and ∼67.6× better resource efficiency compared to existing techniques, respectively. As a result, VALO demonstrates 1.3×--2.5× faster average communication completion times for real-world services than existing techniques.