Intelligent Business Resources Deployment Over Industrial Internet

Dou, Wanchun; Xu, Xiaolong; Yu, Shui

doi:10.1007/978-981-99-5732-3_10

Intelligent Industrial Internet Systems 2023

DOI: 10.1007/978-981-99-5732-3_10

|View full text |Cite

Intelligent Business Resources Deployment Over Industrial Internet

Wanchun Dou,

Xiaolong Xu,

Shui Yu

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

DRL‐based computing offloading approach for large‐scale heterogeneous tasks in mobile edge computing

He,

Li,

Chen

2024

Concurrency and Computation

View full text Add to dashboard Cite

In the last few years, the rapid advancement of the Internet of Things (IoT) and the widespread adoption of smart cities have posed new challenges to computing services. Traditional cloud computing models fail to fulfil the rapid response requirement of latency‐sensitive applications, while mobile edge computing (MEC) improves service efficiency and customer experience by transferring computing tasks to servers located at the network edge. However, designing an effective computing offloading strategy in complex scenarios involving multiple computing tasks, nodes, and services remains a pressing issue. In this paper, a computing offloading approach based on Deep Reinforcement Learning (DRL) is proposed for large‐scale heterogeneous computing tasks. First, Markov Decision Processes (MDPs) is used to formulate computing offloading decision and resource allocation problems in large‐scale heterogeneous MEC systems. Subsequently, a comprehensive framework comprising the "end‐edge‐cloud" along with the corresponding time‐overhead and resource allocation models is constructed. Finally, through extensive experiments on real datasets, the proposed approach is demonstrated to outperform existing methods in enhancing service response speed, reducing latency, balancing server loads, and saving energy.

show abstract

DRL‐based computing offloading approach for large‐scale heterogeneous tasks in mobile edge computing

He,

Li,

Chen

2024

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Intelligent Business Resources Deployment Over Industrial Internet

Cited by 1 publication

References 20 publications

DRL‐based computing offloading approach for large‐scale heterogeneous tasks in mobile edge computing

DRL‐based computing offloading approach for large‐scale heterogeneous tasks in mobile edge computing

Contact Info

Product

Resources

About