A collaborative scheduling method for cloud computing heterogeneous workflows based on deep reinforcement learning

Aiming at the problem of low overall service quality caused by the disordered collaboration of heterogeneous workflows and discontinuous task execution in cloud computing scenarios, this paper proposes a collaborative scheduling method for heterogeneous workflows in cloud computing based on deep reinforcement learning. The method optimizes workflow makespan, cost, fairness and continuity in cloud computing under the constraints of task execution continuity. First, the structure and time sequence features are extracted for the dynamic scheduling process, and a reasonable scheduling decision support feature set is constructed. Second, a time-step adaptive scheduling mechanism is designed to simplify redundant information in the scheduling process and enables the agent to achieve efficient learning. In addition, using equilibrium, priority and preference scheduling strategies, an immediate-lag compound reward mechanism and a scheduling-switching hybrid action are designed to achieve a unification of the agent’s learning objectives and actual scheduling requirements. Finally, by constructing a simulation platform and conducting comparative experiments with four other algorithms, the results show that the proposed method has advantages in collaborative optimization of high-dimensional objectives under task continuity constraints. Including the task loading strategy can optimize the makespan performance by 16.6% and improve the fairness index by 5.3%.