Reinforcement Learning Framework for Server Placement and Workload Allocation in Multiaccess Edge Computing-Reference-Cited by-同舟云学术

Reinforcement Learning Framework for Server Placement and Workload Allocation in Multiaccess Edge Computing

Published:2023-01-15 Issue:2 Volume:10 Page:1376-1390
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Mazloomi Anahita¹,Sami Hani¹^ORCID,Bentahar Jamal¹^ORCID,Otrok Hadi²^ORCID,Mourad Azzam³^ORCID

Affiliation:

1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

2. Department of Electrical Engineering and Computer Science, Center of Cyber-Physical Systems, Khalifa University, Abu Dhabi, UAE

3. Department of CSM, Cyber Security Systems and Applied AI Research Center, Lebanese American University Division of Science, Lebanese American University, Beirut, Lebanon

Funder

FRQNT, Quebec

NSERC and DnD, Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10007705/09881565.pdf?arnumber=9881565

Reference44 articles.

1. Joint Heterogeneous Server Placement and Application Configuration in Edge Computing

2. Exploring Placement of Heterogeneous Edge Servers for Response Time Minimization in Mobile Edge-Cloud Computing

3. Profit-Aware Edge Server Placement

4. Load-Aware Edge Server Placement for Mobile Edge Computing in 5G Networks

5. Reinforcement learning for combinatorial optimization: A survey;mazyavkina;arXiv 2003 03600,2020

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