  In this paper, we introduce MARS, a new scheduling system for HPC-cloud
infrastructures based on a cost-aware, flexible reinforcement learning
approach, which serves as an intermediate layer for next generation HPC-cloud
resource manager. MARS ensembles the pre-trained models from heuristic
workloads and decides on the most cost-effective strategy for optimization. A
whole workflow application would be split into several optimizable dependent
sub-tasks, then based on the pre-defined resource management plan, a reward
will be generated after executing a scheduled task. Lastly, MARS updates the
Deep Neural Network (DNN) model based on the reward. MARS is designed to
optimize the existing models through reinforcement mechanisms. MARS adapts to
the dynamics of workflow applications, selects the most cost-effective
scheduling solution among pre-built scheduling strategies (backfilling, SJF,
etc.) and self-learning deep neural network model at run-time. We evaluate MARS
with different real-world workflow traces. MARS can achieve 5%-60% increased
performance compared to the state-of-the-art approaches.
