Inductive-bias-driven Reinforcement Learning for Efficient Schedules in Heterogeneous Clusters

Subho S. Banerjee, Saurabh Jha, Zbigniew T. Kalbarczyk, and Ravishankar K. Iyer

ICML 2020


The problem of scheduling of workloads onto heterogeneous processors (e.g., CPUs, GPUs, FPGAs) is of fundamental importance in modern data centers. Current system schedulers rely on application/system-specific heuristics that have to be built on a case-by-case basis. Recent work has demonstrated ML techniques for automating the heuristic search by using black-box approaches which require significant training data and time, which make them challenging to use in practice. This paper presents Symphony, a scheduling framework that addresses the challenge in two ways: (i) a domain-driven Bayesian reinforcement learning (RL) model for scheduling, which inherently models the resource dependencies identified from the system architecture; and (ii) a sampling-based technique to compute the gradients of a Bayesian model without performing full probabilistic inference. Together, these techniques reduce both the amount of training data and the time required to produce scheduling policies that significantly outperform black-box approaches by up to 2.2×.

In the News


  title = {Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters},
  author = {Banerjee, Subho and Jha, Saurabh and Kalbarczyk, Zbigniew and Iyer, Ravishankar},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  pages = {629--641},
  year = {2020},
  editor = {Hal Daumé III and Aarti Singh},
  volume = {119},
  series = {Proceedings of Machine Learning Research},
  address = {Virtual},
  month = {13--18 Jul},
  publisher = {PMLR},
  pdf = {},
  url = {},

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