Workflow task offloading mechanism based on A3C under computing network integration

Computing Power Network (CPN) overcome the performance limitations of computing power islands by integrating computing and network resources, dynamically scheduling business traffic to optimal nodes. However, effectively and collaboratively utilizing computing resources to reduce the delay of computing tasks has become a challenging issue in CPN due to the heterogeneity of resources and dynamic load. Existing works often treat workflow tasks as atomic tasks for offloading, disregarding subtask dependencies within a task. This approach leads to increased overall waiting time due to varying execution delays of each subtask. To address this problem, this paper proposes an optimization algorithm for workflow tasks based on slack quantity (CSA_WTO). The algorithm optimizes the arrival order of workflow task graphs before offloading, considering subtask dependencies and arranging them appropriately for subsequent offloading. This effectively reduces waiting delays for subtasks. Additionally, we utilize the Dependent Task Offloading algorithm (DTO) based on A3C to offload optimized workflow tasks, thereby improving execution efficiency in CPN. Simulation results demonstrate that compared with other algorithms, CSA_WTO significantly reduces task waiting delays by up to 85%, and DTO achieves a request acceptance rate of up to 96% while reducing the average completion time by 71%.