Fault-tolerant Scheduling Method for Cloud Computing Based on Reliability Decomposition

Workflow is a commonly adopted execution paradigm in cloud computing environments. Reliability is a crucial Quality of Service (QoS) metric in the process of executing cloud workflow tasks. Currently, methods that can simultaneously satisfy the reliability requirements of workflow computation while optimizing both time and cost are scarce. Neural network-based algorithms require substantial time to search for optimized parameter models when handling large-scale workflows, and the decomposition strategies of existing reliability-based algorithms require further improvement. To address these issues, this paper proposes a reliability decomposition-based fault-tolerant scheduling method. This heuristic method consists of the following steps: calculating task-scheduling priorities, determining reliability allocation weights, performing an initial decomposition of the overall reliability requirement, and selecting Virtual Machines (VMs) for task replicas. The core of this method lies in the optimization of two strategies, namely reliability decomposition and VM selection. The reliability decomposition strategy is designed based on the computational size of workflow tasks and their predecessor-successor dependencies, while the VM selection strategy operates based on a weighted function that balances relative task completion time and execution cost. Experiments are conducted using various workflow types, scales, and reliability requirements. The results indicate that the proposed method satisfies the specified reliability requirements. Moreover, it demonstrates superior comprehensive performance in balancing completion time and cost, outperforming three baseline algorithms: QFEC, QEEC+, and C_GM. This paper provides new solutions and insights for research on reliability decomposition and fault-tolerant scheduling in cloud workflow execution.