The rapid rise of microservice architecture poses severe challenges to workflow scheduling, resource allocation, and goal optimization. However, faults and failures usually happen during workflow running. To ensure the workflow's successful execution during scheduling microservices, this article proposes a dynamic workflow scheduling algorithm by integrating cognition cost and reliability QoS for using reinforcement learning (WS-CCR). First, we explore the ‘restart policy’ of containers in the Kubernetes architecture, which lays the foundation for our work that adopts the redundancy strategy to ensure workflow operation. Then we consider the cognitive cost based on the fact that users have a cognitive process for different microservices in selecting microservices. Additionally, another optimization goal is the reliability of workflows. On this basis, we design a reasonable reward function in reinforcement learning to generate dynamic strategies. Furthermore, following some generated strategies, our engine will schedule candidate microservices for tasks to execute step by step. A series of experiments on Alibaba and business areas workflows have proven the superior performance of our algorithm. Our WS-CCR can generate better Pareto solution sets than other baselines in terms of improving the reliability of running workflows. Finally, we give a case study to prove the practicability of our method.

Workflow scheduling for microservice has become an important and challenging research topic. To design a high-performance and reliable scheduling model, we propose a dependable workflow scheduling algorithm for microservice quality of service (QoS) based on deep-Q-Network, namely the DWSM. Firstly, we utilize the redundancy strategy based on the restart strategy of the Kubernetes container to optimize the dependability of workflow. Then this paper quantifies the dependability indicator and designs the reward function based on three QoS attributes including execution time, resources consumption and dependability to generate the scheduling strategy through DQN. Finally, we utilize a real-world data set to evaluate our algorithm and compare it with several state-of-art baselines including heterogeneous earliest-finish-time (HEFT), greedy algorithm and nondominated sorting genetic algorithm (NSGA-III). Experimental results show that our DWSM algorithm achieves higher performance in dependability and saves more CPU resources. In addition to simulation, we have implemented it on a workflow engine and deployed real workflow cases to test its effectiveness in practice.