Deep reinforcement learning based computation offloading for not only stack architecture

Xiangyun Zheng; Lu Ge; Jie Zeng; Bei Liu; Xin Su

doi:10.1109/GCWkshps45667.2019.9024617

Deep reinforcement learning based computation offloading for not only stack architecture

Xiangyun Zheng
, Lu Ge
, Jie Zeng^*
, Bei Liu
, Xin Su

^*此作品的通讯作者

University of Electronic Science and Technology of China
Tsinghua University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

New technologies emerge with regard to computation offloading, allocating parts of applications to powerful servers to meet demands for the massive traffic of mobile devices. In this paper, we study an offloading framework enabled by Not Only Stack (NO Stack). NO Stack, a promising architecture adopts the virtualization technology, guarantees the implementation of machine learning. A deep reinforcement algorithm triggers energy-efficient strategies with handover control when a mobile device moves among cells. Offloading decisions are formulated by an agent with the feedback from the complex environment. Simulation results demonstrate that our proposed offloading system based on NO Stack reduces the energy consumption and cuts down handover rates for mobile devices, compared with the conventional system when executing a service workflow.

源语言	英语
主期刊名	2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings
出版商	Institute of Electrical and Electronics Engineers Inc.
ISBN（电子版）	9781728109602
DOI	https://doi.org/10.1109/GCWkshps45667.2019.9024617
出版状态	已出版 - 12月 2019
已对外发布	是
活动	2019 IEEE Globecom Workshops, GC Wkshps 2019 - Waikoloa, 美国期限: 9 12月 2019 → 13 12月 2019

出版系列

姓名	2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings

会议

会议	2019 IEEE Globecom Workshops, GC Wkshps 2019
国家/地区	美国
市	Waikoloa
时期	9/12/19 → 13/12/19

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

访问文件

10.1109/GCWkshps45667.2019.9024617

其它文件与链接

链接到 Scopus 的出版物

引用此

Zheng, X., Ge, L., Zeng, J., Liu, B., & Su, X. (2019). Deep reinforcement learning based computation offloading for not only stack architecture. 在 2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings 文章 9024617 (2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCWkshps45667.2019.9024617

@inproceedings{d8b61351cf4848f9a6062c1e0b3595e3,

title = "Deep reinforcement learning based computation offloading for not only stack architecture",

abstract = "New technologies emerge with regard to computation offloading, allocating parts of applications to powerful servers to meet demands for the massive traffic of mobile devices. In this paper, we study an offloading framework enabled by Not Only Stack (NO Stack). NO Stack, a promising architecture adopts the virtualization technology, guarantees the implementation of machine learning. A deep reinforcement algorithm triggers energy-efficient strategies with handover control when a mobile device moves among cells. Offloading decisions are formulated by an agent with the feedback from the complex environment. Simulation results demonstrate that our proposed offloading system based on NO Stack reduces the energy consumption and cuts down handover rates for mobile devices, compared with the conventional system when executing a service workflow.",

keywords = "Computation offloading, Deep reinforcement learning, Energy-efficiency, Handover, Not Only Stack",

author = "Xiangyun Zheng and Lu Ge and Jie Zeng and Bei Liu and Xin Su",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Globecom Workshops, GC Wkshps 2019 ; Conference date: 09-12-2019 Through 13-12-2019",

year = "2019",

month = dec,

doi = "10.1109/GCWkshps45667.2019.9024617",

language = "English",

series = "2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings",

address = "United States",

}

Zheng, X, Ge, L, Zeng, J, Liu, B & Su, X 2019, Deep reinforcement learning based computation offloading for not only stack architecture. 在 2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings., 9024617, 2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Globecom Workshops, GC Wkshps 2019, Waikoloa, 美国, 9/12/19. https://doi.org/10.1109/GCWkshps45667.2019.9024617

Deep reinforcement learning based computation offloading for not only stack architecture. / Zheng, Xiangyun; Ge, Lu; Zeng, Jie 等.
2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 9024617 (2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Deep reinforcement learning based computation offloading for not only stack architecture

AU - Zheng, Xiangyun

AU - Ge, Lu

AU - Zeng, Jie

AU - Liu, Bei

AU - Su, Xin

PY - 2019/12

Y1 - 2019/12

N2 - New technologies emerge with regard to computation offloading, allocating parts of applications to powerful servers to meet demands for the massive traffic of mobile devices. In this paper, we study an offloading framework enabled by Not Only Stack (NO Stack). NO Stack, a promising architecture adopts the virtualization technology, guarantees the implementation of machine learning. A deep reinforcement algorithm triggers energy-efficient strategies with handover control when a mobile device moves among cells. Offloading decisions are formulated by an agent with the feedback from the complex environment. Simulation results demonstrate that our proposed offloading system based on NO Stack reduces the energy consumption and cuts down handover rates for mobile devices, compared with the conventional system when executing a service workflow.

AB - New technologies emerge with regard to computation offloading, allocating parts of applications to powerful servers to meet demands for the massive traffic of mobile devices. In this paper, we study an offloading framework enabled by Not Only Stack (NO Stack). NO Stack, a promising architecture adopts the virtualization technology, guarantees the implementation of machine learning. A deep reinforcement algorithm triggers energy-efficient strategies with handover control when a mobile device moves among cells. Offloading decisions are formulated by an agent with the feedback from the complex environment. Simulation results demonstrate that our proposed offloading system based on NO Stack reduces the energy consumption and cuts down handover rates for mobile devices, compared with the conventional system when executing a service workflow.

KW - Computation offloading

KW - Deep reinforcement learning

KW - Energy-efficiency

KW - Handover

KW - Not Only Stack

UR - https://www.scopus.com/pages/publications/85082307209

U2 - 10.1109/GCWkshps45667.2019.9024617

DO - 10.1109/GCWkshps45667.2019.9024617

M3 - Conference contribution

AN - SCOPUS:85082307209

T3 - 2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings

BT - 2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Globecom Workshops, GC Wkshps 2019

Y2 - 9 December 2019 through 13 December 2019

ER -

Deep reinforcement learning based computation offloading for not only stack architecture

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此