PipeSim: Trace-driven Simulation of Large-Scale AI Operations Platforms

no code implementations • 22 Jun 2020 • Thomas Rausch, Waldemar Hummer, Vinod Muthusamy

To optimize operations of production-grade AI workflow platforms we can leverage existing scheduling approaches, yet it is challenging to fine-tune operational strategies that achieve application-specific cost-benefit tradeoffs while catering to the specific domain characteristics of machine learning (ML) models, such as accuracy, robustness, or fairness.

Fairness Scheduling