KFTO: Kuhn-Munkres based fair task offloading in fog networks
In the fog network with multiple terminals and fog nodes, how to make a tradeoff between energy consumption fairness among fog nodes (FNs) and task processing delay of terminal nodes (TNs) is still a challenging problem. To solve this problem, this paper proposes a Kuhn-Munkres (KM) based Fair Task...
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Veröffentlicht in: | Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2021-08, Vol.195, p.108131, Article 108131 |
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creator | Yao, Yingbiao Qin, Yuancheng Feng, Wei Li, Pei Xu, Xiaorong Xu, Xin Liang, Xuesong |
description | In the fog network with multiple terminals and fog nodes, how to make a tradeoff between energy consumption fairness among fog nodes (FNs) and task processing delay of terminal nodes (TNs) is still a challenging problem. To solve this problem, this paper proposes a Kuhn-Munkres (KM) based Fair Task Offloading (KFTO) scheme, which includes two optimization models: FN selection model and task offloading decision model. FN selection model employs the KM algorithm to obtain the optimal matching scheme between TNs and FNs that maximizes the total network potential. The second model is used to obtain the task size offloaded to FN that minimizes the task processing delay of TN while satisfying the energy consumption constraint. Numerical simulation results reveal that the proposed KFTO scheme achieves a satisfactory tradeoff between the energy fairness among FNs and the task processing delay of TNs. |
doi_str_mv | 10.1016/j.comnet.2021.108131 |
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To solve this problem, this paper proposes a Kuhn-Munkres (KM) based Fair Task Offloading (KFTO) scheme, which includes two optimization models: FN selection model and task offloading decision model. FN selection model employs the KM algorithm to obtain the optimal matching scheme between TNs and FNs that maximizes the total network potential. The second model is used to obtain the task size offloaded to FN that minimizes the task processing delay of TN while satisfying the energy consumption constraint. 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To solve this problem, this paper proposes a Kuhn-Munkres (KM) based Fair Task Offloading (KFTO) scheme, which includes two optimization models: FN selection model and task offloading decision model. FN selection model employs the KM algorithm to obtain the optimal matching scheme between TNs and FNs that maximizes the total network potential. The second model is used to obtain the task size offloaded to FN that minimizes the task processing delay of TN while satisfying the energy consumption constraint. 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To solve this problem, this paper proposes a Kuhn-Munkres (KM) based Fair Task Offloading (KFTO) scheme, which includes two optimization models: FN selection model and task offloading decision model. FN selection model employs the KM algorithm to obtain the optimal matching scheme between TNs and FNs that maximizes the total network potential. The second model is used to obtain the task size offloaded to FN that minimizes the task processing delay of TN while satisfying the energy consumption constraint. Numerical simulation results reveal that the proposed KFTO scheme achieves a satisfactory tradeoff between the energy fairness among FNs and the task processing delay of TNs.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.comnet.2021.108131</doi></addata></record> |
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subjects | Algorithms Computation offloading Constraint modelling Delay Energy consumption energy fairness Fog network Kuhn-Munkres algorithm Nodes Optimization task offloading Tradeoffs |
title | KFTO: Kuhn-Munkres based fair task offloading in fog networks |
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