DRL-TA: A Type-aware Task Scheduling and Load Balancing Method based on Deep Reinforcement Learning in Heterogeneous Computing Environment

Abstract

Task scheduling and load balancing in heterogeneous computing environments has been a challenge for long, especially when dealing with multiple types of task input batches. In this scenario, existing methods cannot take into account both the high efficiency of task processing and the full utilization of cluster resources. However, the rise of artificial intelligence methods provides a new way to solve this problem. In this paper, we design a type-aware task scheduling method based on deep reinforcement learning to tackle multiple types of tasks in heterogeneous computing environment. First, we adopt prioritized dueling double deep q-learning network to make action decisions for each batch of input tasks. Then we build a task type prediction neural network to predict the task type of the input task, and then use the Monte Carlo algorithm based on reward value to realize the load balancing of the scheduled cluster. To verify the effectiveness of our proposed method, we use a widely used dataset Alibaba cluster trace dataset for our experiments. Experimental results show that our proposed algorithm can significantly shorten the average makespan of task batches and achieve better load balancing effect compared with other existing solutions.