Encoding for Reinforcement Learning Driven Scheduling

Reinforcement learning (RL) is exploited for cluster scheduling in the field of high-performance computing (HPC). One of the key challenges for RL driven scheduling is state representation for RL agent (i.e., capturing essential features of dynamic scheduling environment for decision making). Existing state encoding approaches either lack critical scheduling information or suffer from poor scalability. In this study, we present SEM (Scalable and Efficient encoding Model) for general RL driven scheduling in HPC. It captures system resource and waiting job state, both being critical information for scheduling. It encodes these pieces of information into a fixed-sized vector as an input to the agent. A typical agent is built on deep neural network, and its training/inference cost grows exponentially with the size of its input. Production HPC systems contain a large number of computer nodes. As such, a direct encoding of each of the system resources would lead to poor scalability of the RL agent. SEM uses two techniques to transform the system resource state into a small-sized vector, hence being capable of representing a large number of system resources in a vector of 100–200. Our trace-based simulations demonstrate that compared to the existing state encoding methods, SEM can achieve 9X training speedup and 6X inference speedup while maintaining comparable scheduling performance.