  Ensuring the highest training throughput to maximize resource efficiency,
while maintaining fairness among users, is critical for deep learning (DL)
training in heterogeneous GPU clusters. However, current DL schedulers provide
only limited fairness properties and suboptimal training throughput, impeding
tenants from effectively leveraging heterogeneous resources. The underlying
design challenge stems from inherent conflicts between efficiency and fairness
properties.
  In this paper, we introduce OEF, a new resource allocation framework
specifically developed for achieving optimal resource efficiency and ensuring
diverse fairness properties in heterogeneous GPU clusters. By integrating
resource efficiency and fairness within a global optimization framework, OEF is
capable of providing users with maximized overall efficiency, as well as
various guarantees of fairness, in both cooperative and non-cooperative
environments. We have implemented OEF in a cluster resource manager and
conducted large-scale experiments, showing that OEF can improve the overall
training throughput by up to 32% while improving fairness compared to
state-of-the-art heterogeneity-aware schedulers.
