Maximal energy efficient task scheduling for homogeneous fog networks

Yang Yang; Kunlun Wang; Guowei Zhang; Xu Chen; Xiliang Luo; Ming Tuo Zhou

doi:10.1109/INFCOMW.2018.8406933

Maximal energy efficient task scheduling for homogeneous fog networks

Yang Yang, Kunlun Wang, Guowei Zhang, Xu Chen, Xiliang Luo, Ming Tuo Zhou

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

20 Scopus citations

Abstract

In this paper, a comprehensive analytical model that considers circuit, computation, offloading energy consumptions is developed for accurately evaluating the overall energy efficiency in homogeneous fog networks. With this model, the tradeoff between performance gains and energy costs in collaborative task offloading is investigated, thus enabling us to formulate the energy efficiency optimization problem for future intelligent internet of things (IoT) applications with practical constraints in available computing resources at helper nodes and unused spectrum in neighboring environments. Based on rigorous mathematical analysis, a maximal energy efficient task scheduling (MEETS) algorithm is proposed to derive the optimal scheduling decision for a task node and multiple neighboring helper nodes under feasible modulation schemes and time allocations. Extensive simulation results demonstrate the tradeoff relationship between energy efficiency and task scheduling performance in homogeneous fog networks. Compared with traditional strategies, the proposed MEETS algorithm can achieve much better energy efficiency performance under different network parameters and service conditions.

Original language	English
Title of host publication	INFOCOM 2018 - IEEE Conference on Computer Communications Workshops
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	274-279
Number of pages	6
ISBN (Electronic)	9781538659793
DOIs	https://doi.org/10.1109/INFCOMW.2018.8406933
State	Published - 6 Jul 2018
Externally published	Yes
Event	2018 IEEE Conference on Computer Communications Workshops, INFOCOM 2018 - Honolulu, United States Duration: 15 Apr 2018 → 19 Apr 2018

Publication series

Name	INFOCOM 2018 - IEEE Conference on Computer Communications Workshops

Conference

Conference	2018 IEEE Conference on Computer Communications Workshops, INFOCOM 2018
Country/Territory	United States
City	Honolulu
Period	15/04/18 → 19/04/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/INFCOMW.2018.8406933

Cite this

Yang, Y., Wang, K., Zhang, G., Chen, X., Luo, X., & Zhou, M. T. (2018). Maximal energy efficient task scheduling for homogeneous fog networks. In INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (pp. 274-279). (INFOCOM 2018 - IEEE Conference on Computer Communications Workshops). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFCOMW.2018.8406933

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title = "Maximal energy efficient task scheduling for homogeneous fog networks",

abstract = "In this paper, a comprehensive analytical model that considers circuit, computation, offloading energy consumptions is developed for accurately evaluating the overall energy efficiency in homogeneous fog networks. With this model, the tradeoff between performance gains and energy costs in collaborative task offloading is investigated, thus enabling us to formulate the energy efficiency optimization problem for future intelligent internet of things (IoT) applications with practical constraints in available computing resources at helper nodes and unused spectrum in neighboring environments. Based on rigorous mathematical analysis, a maximal energy efficient task scheduling (MEETS) algorithm is proposed to derive the optimal scheduling decision for a task node and multiple neighboring helper nodes under feasible modulation schemes and time allocations. Extensive simulation results demonstrate the tradeoff relationship between energy efficiency and task scheduling performance in homogeneous fog networks. Compared with traditional strategies, the proposed MEETS algorithm can achieve much better energy efficiency performance under different network parameters and service conditions.",

author = "Yang Yang and Kunlun Wang and Guowei Zhang and Xu Chen and Xiliang Luo and Zhou, \{Ming Tuo\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Conference on Computer Communications Workshops, INFOCOM 2018 ; Conference date: 15-04-2018 Through 19-04-2018",

year = "2018",

month = jul,

day = "6",

doi = "10.1109/INFCOMW.2018.8406933",

language = "英语",

series = "INFOCOM 2018 - IEEE Conference on Computer Communications Workshops",

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

pages = "274--279",

booktitle = "INFOCOM 2018 - IEEE Conference on Computer Communications Workshops",

address = "美国",

}

Yang, Y, Wang, K, Zhang, G, Chen, X, Luo, X & Zhou, MT 2018, Maximal energy efficient task scheduling for homogeneous fog networks. in INFOCOM 2018 - IEEE Conference on Computer Communications Workshops. INFOCOM 2018 - IEEE Conference on Computer Communications Workshops, Institute of Electrical and Electronics Engineers Inc., pp. 274-279, 2018 IEEE Conference on Computer Communications Workshops, INFOCOM 2018, Honolulu, United States, 15/04/18. https://doi.org/10.1109/INFCOMW.2018.8406933

Maximal energy efficient task scheduling for homogeneous fog networks. / Yang, Yang; Wang, Kunlun; Zhang, Guowei et al.
INFOCOM 2018 - IEEE Conference on Computer Communications Workshops. Institute of Electrical and Electronics Engineers Inc., 2018. p. 274-279 (INFOCOM 2018 - IEEE Conference on Computer Communications Workshops).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Maximal energy efficient task scheduling for homogeneous fog networks

AU - Yang, Yang

AU - Wang, Kunlun

AU - Zhang, Guowei

AU - Chen, Xu

AU - Luo, Xiliang

AU - Zhou, Ming Tuo

PY - 2018/7/6

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N2 - In this paper, a comprehensive analytical model that considers circuit, computation, offloading energy consumptions is developed for accurately evaluating the overall energy efficiency in homogeneous fog networks. With this model, the tradeoff between performance gains and energy costs in collaborative task offloading is investigated, thus enabling us to formulate the energy efficiency optimization problem for future intelligent internet of things (IoT) applications with practical constraints in available computing resources at helper nodes and unused spectrum in neighboring environments. Based on rigorous mathematical analysis, a maximal energy efficient task scheduling (MEETS) algorithm is proposed to derive the optimal scheduling decision for a task node and multiple neighboring helper nodes under feasible modulation schemes and time allocations. Extensive simulation results demonstrate the tradeoff relationship between energy efficiency and task scheduling performance in homogeneous fog networks. Compared with traditional strategies, the proposed MEETS algorithm can achieve much better energy efficiency performance under different network parameters and service conditions.

AB - In this paper, a comprehensive analytical model that considers circuit, computation, offloading energy consumptions is developed for accurately evaluating the overall energy efficiency in homogeneous fog networks. With this model, the tradeoff between performance gains and energy costs in collaborative task offloading is investigated, thus enabling us to formulate the energy efficiency optimization problem for future intelligent internet of things (IoT) applications with practical constraints in available computing resources at helper nodes and unused spectrum in neighboring environments. Based on rigorous mathematical analysis, a maximal energy efficient task scheduling (MEETS) algorithm is proposed to derive the optimal scheduling decision for a task node and multiple neighboring helper nodes under feasible modulation schemes and time allocations. Extensive simulation results demonstrate the tradeoff relationship between energy efficiency and task scheduling performance in homogeneous fog networks. Compared with traditional strategies, the proposed MEETS algorithm can achieve much better energy efficiency performance under different network parameters and service conditions.

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M3 - 会议稿件

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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Maximal energy efficient task scheduling for homogeneous fog networks

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