Workflow Scheduling in Serverless Edge Computing for the Industrial Internet of Things: A Learning Approach

Serverless edge computing is seen as a promising enabler to execute differentiated Industrial Internet of Things (IIoT) applications without managing the underlying servers and clusters. In IIoT serverless edge computing, IIoT workflow scheduling for cloud-edge collaborative processing is closely re...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial informatics 2023-07, Vol.19 (7), p.8242-8252
Main Authors: Xie, Renchao, Gu, Dier, Tang, Qinqin, Huang, Tao, Yu, Fei Richard
Format: Article
Language:English
Subjects:
Cloud computing
Computational modeling
Deep reinforcement learning (DRL)
Edge computing
Industrial applications
Industrial Internet of Things
Internet of Things
Job shop scheduling
multiobjective optimization
Multiple objective analysis
Optimization
Processor scheduling
Quality of service architectures
Scheduling
serverless edge computing
Task analysis
Wireless communication
workflow scheduling
Workflow software
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Serverless edge computing is seen as a promising enabler to execute differentiated Industrial Internet of Things (IIoT) applications without managing the underlying servers and clusters. In IIoT serverless edge computing, IIoT workflow scheduling for cloud-edge collaborative processing is closely related to the service quality of users. However, serverless functions decomposed by IIoT applications are limited in their deployment at the edge due to the resource-constrained nature of edge infrastructures. In addition, the scheduling of complex IIoT applications supported by serverless computing is more challenging. Therefore, considering the limited function deployment and the complex dependencies of serverless workflows, we model the workflow application as directed acyclic graph and formulate the scheduling problem as a multiobjective optimization problem. A dueling double deep Q-network-based solution is proposed to make scheduling decisions under dynamically changing systems. Extensive simulation experiments are conducted to validate the superiority of the proposed scheme.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3217477