In this paper, we design a task scheduling scheme for Internet of Things (IoT) workflow applications in serverless edge computing. Notice the fact that complex applications in traditional serverless computing are decomposed into several stateless, dependent functions, whose execution environments are pre-deployed at the resource-finite edge domain, we model the workflow application as Directed Acyclic Graph (DAG) by considering the distribution of edge resources and the deployment of serverless functions. We further formulate the scheduling problem as a multi-objective optimization problem to reduce the time consumption, energy consumption, and cost simultaneously. Then, considering the diversity of solution space and the fast convergence to optimal solutions, an improved hybrid algorithm that combines Particle Swarm Optimization and Genetic Algorithm (PSO–GA) is introduced and utilized to make the scheduling decision. Finally, extensive simulation experiments are conducted to validate the superiority of the proposed scheme.
Workflow Scheduling Using Hybrid PSO-GA Algorithm in Serverless Edge Computing for the Internet of Things
2022-06-01
731382 byte
Conference paper
Electronic Resource
English
Internet of Things data terminal system based on 5G and edge computing
European Patent Office | 2023
|European Patent Office | 2021
|