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Resolving Multi-task Competition for Constrained Resources in Dispersed Computing: A Bilateral Matching Game

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

Resolving Multi-task Competition for Constrained Resources in Dispersed Computing : A Bilateral Matching Game. / Wu, H.; Zhang, J.; Cai, Z. et al.

In: IEEE Internet of Things Journal, Vol. 8, No. 23, 31.12.2021, p. 16972-16983.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Wu, H, Zhang, J, Cai, Z, Ni, Q, Zhou, T, Yu, J, Chen, H & Liu, F 2021, 'Resolving Multi-task Competition for Constrained Resources in Dispersed Computing: A Bilateral Matching Game', IEEE Internet of Things Journal, vol. 8, no. 23, pp. 16972-16983. https://doi.org/10.1109/JIOT.2021.3075673

APA

Wu, H., Zhang, J., Cai, Z., Ni, Q., Zhou, T., Yu, J., Chen, H., & Liu, F. (2021). Resolving Multi-task Competition for Constrained Resources in Dispersed Computing: A Bilateral Matching Game. IEEE Internet of Things Journal, 8(23), 16972-16983. https://doi.org/10.1109/JIOT.2021.3075673

Vancouver

Wu H, Zhang J, Cai Z, Ni Q, Zhou T, Yu J et al. Resolving Multi-task Competition for Constrained Resources in Dispersed Computing: A Bilateral Matching Game. IEEE Internet of Things Journal. 2021 Dec 31;8(23):16972-16983. Epub 2021 Apr 26. doi: 10.1109/JIOT.2021.3075673

Author

Wu, H. ; Zhang, J. ; Cai, Z. et al. / Resolving Multi-task Competition for Constrained Resources in Dispersed Computing : A Bilateral Matching Game. In: IEEE Internet of Things Journal. 2021 ; Vol. 8, No. 23. pp. 16972-16983.

Bibtex

@article{882ada27242b407e9d664ad0b51c2544,
title = "Resolving Multi-task Competition for Constrained Resources in Dispersed Computing: A Bilateral Matching Game",
abstract = "With the explosive emergence of computation-intensive and latency-sensitive applications, data processing could be envisioned to perform closer to the data source. Similar to edge and fog computing, dispersed computing is considered as a complementary computing paradigm, which can excavate potential computation resources in the network to users, and serve as a supplement for sharing computational burden when the edge is overloaded. In this paper, we first make full use of idle and geographically dispersed computation resources via task offloading, contributing to conserve energy for mobile devices. Specially, a dispersed computing offloading framework concerning the interests of users and networked computation points is proposed. We further transform the initial problem into a multi-objective optimization problem subject to latency and resource constraints. To tackle such a complex problem, an energy-saving bilateral matching algorithm is designed to obtain the optimal task offloading strategy. The simulation results demonstrate that our proposed algorithm can outperform the benchmark schemes in terms of user fairness and can achieve a relatively balanced energy cost ratio. Furthermore, comparative experiments with edge computing are implemented in Amber Response and Disaster Relief scenarios respectively to reveal the advantages of the proposed framework.",
keywords = "bilateral matching, Collaboration, Computational modeling, Dispersed computing, Edge computing, energy-saving, Heuristic algorithms, idle computation resources, Internet of Things, multi-objective, offloading., Servers, Task analysis, Computer games, Data handling, Disaster prevention, Energy conservation, Multiobjective optimization, Comparative experiments, Complementary computing, Computation intensives, Computation resources, Computational burden, Constrained resources, Multi-objective optimization problem, Sensitive application, Fog computing",
author = "H. Wu and J. Zhang and Z. Cai and Q. Ni and T. Zhou and J. Yu and H. Chen and F. Liu",
note = "{\textcopyright}2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2021",
month = dec,
day = "31",
doi = "10.1109/JIOT.2021.3075673",
language = "English",
volume = "8",
pages = "16972--16983",
journal = "IEEE Internet of Things Journal",
issn = "2327-4662",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "23",

}

RIS

TY - JOUR

T1 - Resolving Multi-task Competition for Constrained Resources in Dispersed Computing

T2 - A Bilateral Matching Game

AU - Wu, H.

AU - Zhang, J.

AU - Cai, Z.

AU - Ni, Q.

AU - Zhou, T.

AU - Yu, J.

AU - Chen, H.

AU - Liu, F.

N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2021/12/31

Y1 - 2021/12/31

N2 - With the explosive emergence of computation-intensive and latency-sensitive applications, data processing could be envisioned to perform closer to the data source. Similar to edge and fog computing, dispersed computing is considered as a complementary computing paradigm, which can excavate potential computation resources in the network to users, and serve as a supplement for sharing computational burden when the edge is overloaded. In this paper, we first make full use of idle and geographically dispersed computation resources via task offloading, contributing to conserve energy for mobile devices. Specially, a dispersed computing offloading framework concerning the interests of users and networked computation points is proposed. We further transform the initial problem into a multi-objective optimization problem subject to latency and resource constraints. To tackle such a complex problem, an energy-saving bilateral matching algorithm is designed to obtain the optimal task offloading strategy. The simulation results demonstrate that our proposed algorithm can outperform the benchmark schemes in terms of user fairness and can achieve a relatively balanced energy cost ratio. Furthermore, comparative experiments with edge computing are implemented in Amber Response and Disaster Relief scenarios respectively to reveal the advantages of the proposed framework.

AB - With the explosive emergence of computation-intensive and latency-sensitive applications, data processing could be envisioned to perform closer to the data source. Similar to edge and fog computing, dispersed computing is considered as a complementary computing paradigm, which can excavate potential computation resources in the network to users, and serve as a supplement for sharing computational burden when the edge is overloaded. In this paper, we first make full use of idle and geographically dispersed computation resources via task offloading, contributing to conserve energy for mobile devices. Specially, a dispersed computing offloading framework concerning the interests of users and networked computation points is proposed. We further transform the initial problem into a multi-objective optimization problem subject to latency and resource constraints. To tackle such a complex problem, an energy-saving bilateral matching algorithm is designed to obtain the optimal task offloading strategy. The simulation results demonstrate that our proposed algorithm can outperform the benchmark schemes in terms of user fairness and can achieve a relatively balanced energy cost ratio. Furthermore, comparative experiments with edge computing are implemented in Amber Response and Disaster Relief scenarios respectively to reveal the advantages of the proposed framework.

KW - bilateral matching

KW - Collaboration

KW - Computational modeling

KW - Dispersed computing

KW - Edge computing

KW - energy-saving

KW - Heuristic algorithms

KW - idle computation resources

KW - Internet of Things

KW - multi-objective

KW - offloading.

KW - Servers

KW - Task analysis

KW - Computer games

KW - Data handling

KW - Disaster prevention

KW - Energy conservation

KW - Multiobjective optimization

KW - Comparative experiments

KW - Complementary computing

KW - Computation intensives

KW - Computation resources

KW - Computational burden

KW - Constrained resources

KW - Multi-objective optimization problem

KW - Sensitive application

KW - Fog computing

U2 - 10.1109/JIOT.2021.3075673

DO - 10.1109/JIOT.2021.3075673

M3 - Journal article

VL - 8

SP - 16972

EP - 16983

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

IS - 23

ER -