Cost-Driven Scheduling for Workflow Decision Making Systems in Fuzzy Edge-Cloud Environments

Abstract

Workflow decision making is critical to performing many practical applications of scientific principles and data. Scheduling in edge-cloud environments can address the high complexity of workflow applications, while decreasing the data transmission delay between the cloud and end devices. However, due to the heterogeneous resources in edge-cloud environments and the complicated data dependencies between the tasks in a workflow, significant challenges for workflow scheduling remain, including the selection of an optimal tasks-servers solution from the possible numerous combinations. Existing studies are mainly done subject to rigorous conditions without fluctuations, ignoring the fact that workflow scheduling is typically present in uncertain environments. In this study, we focus on reducing the execution cost of multiple workflow applications mainly caused by data transmission and task computation, while satisfying the required deadline constraints. Triangular fuzzy numbers are employed to represent the computing performance of servers and transmission bandwidth in fuzzy edge-cloud environments. A cost-driven scheduling strategy for multiple Poisson-arrived workflow applications using partial critical paths is proposed. It firstly merges cut edges through preprocess to reduce the workflow scale, then uniformly schedules all tasks on each partial critical path to avoid data transmission between dependent tasks and reduce the data transmission cost. The experimental results show that our strategy can obtain the optimal feasible scheduling scheme and have better robustness and real-time performance with different deadline constraints, compared with other benchmark strategies. Note to Practitioners—Vehicle identification is one of the workflow decision making systems in transportation environments, whose core technology is Deep Neural Networks (DNN). Traffic cameras with limited process capacity periodically record the images of on-road vehicles, and usually fail to complete the applications within their deadlines. Workflow decision making is one of the key issues to performance DNNs in vehicle identification applications. The uncertain environments have a great impact on the system latency for such problems, which can easily lead to the misjudgement of the optimal scheduling. In addition, it is difficult to select an optimal layers-servers solution from the numerous combinations. Therefore, we can employ the scheduling strategy (i.e., SWPCP) to make intelligent and faster workflow decisions for vehicle identification applications, which can reduce the execution cost mainly caused by layer computation and data transmission between layers within their deadlines, even in uncertain edge-cloud environments. Complex DNN layers (tasks) in vehicle identification applications can be scheduled to the cloud for execution, while simple ones are processed on the edge. The cloud and edge platforms collaborate with each other and execute the DNN layers with low system cost and latency.