List-Based Workflow Scheduling Utilizing Deep Reinforcement Learning

Workflow scheduling is a well-known NP-complete research problem with wide applications and increasing importance. Traditionally, heuristic and guided random search methods, e.g. genetic algorithm, are the two major categories of scheduling approaches developed to tackle this challenging problem. With the rise of deep reinforcement learning (DRL), this paper tries to apply it to solve the workflow scheduling problem in two different ways. The first way utilizes DRL as an iterative optimization method to find the best schedule for a specific workflow. In the second way, DRL is used to train a neural network which could be adopted to schedule new workflows not in the training set. Our DRL-based workflow scheduling method is based on the policy gradient (PG) reinforcement learning algorithm and utilizes a convolutional neural network (CNN). Experimental results show that our DRL-based method can produce more efficient workflow execution schedules, compared to the state of the art of heuristic-based scheduling algorithms. The superior performance of the DRL-based method indicates a promising direction for future research work on workflow scheduling.