Workflow task scheduling optimization strategy in moving edge computing environment

Abstract

With the explosive growth of mobile devices, the computing power and resource storage of the mobile terminal have higher demand, and the workflow task scheduling in the moving edge computing environment can solve complex data dependencies between tasks in workflows. The proposal of moving edge computing techniques is to solve computing resources caused by massive mobile device access, which can meet the low delay and high computing power of the mobile device. This paper proposes a workflow task scheduling optimization algorithm (WTS-OSM) in moving edge computing environment. First, the workflow tasks are generated into directed acyclic graph (DAG), and then the tasks in DAG are layered. The tasks in the same layer do not have dependencies, but the tasks in two adjacent layers do. Finally, an optimized genetic algorithm (GA) is used to determine whether the layer task computes unloading. Experimental results show that the proposed algorithm is superior to the traditional algorithms in terms of the task execution time.