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Deep learning based task scheduling in a cloud RAN enabled edge environment

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Jude Fletcher,

David WallomAuthors Info & Claims

SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

Pages 283 - 285

https://doi.org/10.1145/3318216.3363319

Published: 07 November 2019 Publication History

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Abstract

As mobile traffic continues to grow, network operators are constantly on a journey, looking for ways to optimize their network. The aim is to improve network operation performance as well as cut down cost without compromising on the overall quality of service. Many network operators as part of their optimization exercise are adopting Cloud Radio Access Network (C-RAN) and it is necessary to address the challenges that this technology poses in an edge environment. One of the major challenges or an area of improvement is how tasks are scheduled as a user moves within the edge environment. This ongoing research adopts a machine learning technique in scheduling tasks efficiently in order for applications and services to adhere to stringent performance requirements even at geographically dispersed "edge" locations.

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Index Terms

Deep learning based task scheduling in a cloud RAN enabled edge environment
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. Computing methodologies
  1. Machine learning

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Published In

SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

November 2019

455 pages

ISBN:9781450367332

DOI:10.1145/3318216

General Chairs:
Songqing Chen
George Mason University
,
Ryokichi Onishi
Toyota
,
Program Chairs:
Ganesh Ananthanarayanan
Microsoft Research
,
Qun Li
College of William & Mary

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IEEE-CS\DATC: IEEE Computer Society

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Association for Computing Machinery

New York, NY, United States

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Published: 07 November 2019

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SEC '19: The Fourth ACM/IEEE Symposium on Edge Computing

November 7 - 9, 2019

Virginia, Arlington

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SEC '19 Paper Acceptance Rate 20 of 59 submissions, 34%;

Overall Acceptance Rate 40 of 100 submissions, 40%

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Using machine learning to orchestrate cloud resources in a RAN enabled edge environment: poster abstract

DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing

Optimizing Task Scheduling in Cloud Computing: An Enhanced Shortest Job First Algorithm

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