DDQN-TS: A novel bi-objective intelligent scheduling algorithm in the cloud environment

Task scheduling has always been one of the crucial problem in cloud computing. With the transition of task types from static batch processing to dynamic stream processing, the dynamic online task scheduling problem has attracted widespread attention. At this stage, explore an effective task scheduling method to implement high quality of service (QoS) requests with limited resources is a considerable challenge. This paper proposes a novel scheduling algorithm called double deep Q-network task scheduling (DDQN-TS), which uses the adaptive learning ability of double deep Q-network (DDQN) to explore the optimal task scheduling strategy. Experiments conducted using the Random, Google, and Alibaba benchmarks to compare several classic algorithms show that the proposed DDQN-TS can guarantee a high task completion rate and efficiently reduce the task average response time.