Harmony: A Scheduling Framework Optimized for Multiple Distributed Machine Learning Jobs

We introduce Harmony, a new scheduling framework that executes multiple Parameter-Server ML training jobs together to improve cluster resource utilization. Harmony coordinates a fine-grained execution of co-located jobs with complementary resource usages to avoid contention and to efficiently share resources between the jobs. To resolve the memory pressure due to the increased number of simultaneo...