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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -