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

Abstract

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 simultaneous jobs, Harmony uses a data spill/reload mechanism optimized for multiple jobs with the iterative execution pattern. Our evaluation shows that Harmony improves cluster resource utilization by up to 1.65×, resulting in a reduction of the mean ML training job time by about 53%, and makespan, the total time to process all given jobs, by about 38%, compared to the traditional approaches that allocate dedicated resources to each job.