AFAS: Arbitrary-Freedom Adaptive Scheduling for Multiworkflow Cloud Computing via Deep Reinforcement Learning

The in-depth development of artificial intelligence models has supported the high-quality allocation of cloud computing resources. The optimization of workflow scheduling issues in cloud computing has become increasingly critical due to the complexity of computing tasks, constraints on computing resources, and the growing demand for high-quality service. To address the increasingly complex workflow scheduling problems in cloud computing, this paper presents an arbitrary-freedom adaptive scheduling method for cloud computing with multiple workflows based on deep reinforcement learning (termed AFAS), with the workflow makespan and response time as the optimization objectives. First, we define the concept of degrees of freedom in the scheduling context to establish the feature space and foundational decision patterns relevant to multiworkflow scheduling. Second, an adaptive real-time scheduling strategy generation (ARS) algorithm is proposed for multiworkflow scheduling tasks. Third, a composite reward mechanism with an advanced-time-window real-time-reward (ATR) algorithm is designed for intelligent model optimization. Finally, the generation algorithm and intelligent model are fused to perform arbitrary-freedom multiworkflow adaptive scheduling. The experiments show that ATR can significantly increase the frequency of reward generation, AFAS can achieve at least 6.6% better performance than existing methods can achieve, and the incorporation of intelligent models improves the performance of ARS by 2.7%.