The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans cet article, nous proposons un algorithme hiérarchique d'allocation de ressources basé sur le jeu de Stackelberg (HGRAA) pour allouer conjointement les ressources sans fil et informatiques d'un système de calcul mobile de pointe (MEC). Le HGRAA proposé est composé de deux niveaux : le jeu évolutif de niveau inférieur (LEG) minimise le coût des terminaux mobiles (MT) et le jeu de potentiel exact de niveau supérieur (UEPG) maximise l'utilité des serveurs MEC. Au niveau inférieur, les MT sont divisés en MT sensibles au retard (DSMT) et MT non sensibles au retard (NDSMT) en fonction de leurs différentes exigences en matière de qualité de service (QoS). La concurrence entre les DSMT et les NDSMT dans différentes zones de service pour partager les ressources informatiques et sans fil disponibles limitées est formulée comme un jeu évolutif dynamique. Le réplicateur dynamique est appliqué pour obtenir l'équilibre évolutif afin de minimiser les coûts imposés aux MT. Au niveau supérieur, le jeu potentiel exact est formulé pour résoudre le problème de partage de ressources entre les serveurs MEC et le problème de partage de ressources est transféré à la complémentarité non linéaire. L'existence de l'équilibre de Nash (NE) est prouvée et obtenue grâce à la condition de Karush-Kuhn-Tucker (KKT). Les simulations illustrent que des améliorations substantielles des performances telles que l'utilité moyenne et l'utilisation des ressources des serveurs MEC peuvent être obtenues en appliquant le HGRAA proposé. De plus, le coût des MT est nettement inférieur à celui des autres algorithmes existants avec la taille croissante des données d'entrée, et les exigences de QoS des différents types de MT sont bien garanties en termes de délai moyen et de débit de transmission.
Weiwei XIA
Southeast University
Zhuorui LAN
Southeast University
Lianfeng SHEN
Southeast University
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Weiwei XIA, Zhuorui LAN, Lianfeng SHEN, "Joint Wireless and Computational Resource Allocation Based on Hierarchical Game for Mobile Edge Computing" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 11, pp. 1395-1407, November 2021, doi: 10.1587/transcom.2020EBP3171.
Abstract: In this paper, we propose a hierarchical Stackelberg game based resource allocation algorithm (HGRAA) to jointly allocate the wireless and computational resources of a mobile edge computing (MEC) system. The proposed HGRAA is composed of two levels: the lower-level evolutionary game (LEG) minimizes the cost of mobile terminals (MTs), and the upper-level exact potential game (UEPG) maximizes the utility of MEC servers. At the lower-level, the MTs are divided into delay-sensitive MTs (DSMTs) and non-delay-sensitive MTs (NDSMTs) according to their different quality of service (QoS) requirements. The competition among DSMTs and NDSMTs in different service areas to share the limited available wireless and computational resources is formulated as a dynamic evolutionary game. The dynamic replicator is applied to obtain the evolutionary equilibrium so as to minimize the costs imposed on MTs. At the upper level, the exact potential game is formulated to solve the resource sharing problem among MEC servers and the resource sharing problem is transferred to nonlinear complementarity. The existence of Nash equilibrium (NE) is proved and is obtained through the Karush-Kuhn-Tucker (KKT) condition. Simulations illustrate that substantial performance improvements such as average utility and the resource utilization of MEC servers can be achieved by applying the proposed HGRAA. Moreover, the cost of MTs is significantly lower than other existing algorithms with the increasing size of input data, and the QoS requirements of different kinds of MTs are well guaranteed in terms of average delay and transmission data rate.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020EBP3171/_p
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@ARTICLE{e104-b_11_1395,
author={Weiwei XIA, Zhuorui LAN, Lianfeng SHEN, },
journal={IEICE TRANSACTIONS on Communications},
title={Joint Wireless and Computational Resource Allocation Based on Hierarchical Game for Mobile Edge Computing},
year={2021},
volume={E104-B},
number={11},
pages={1395-1407},
abstract={In this paper, we propose a hierarchical Stackelberg game based resource allocation algorithm (HGRAA) to jointly allocate the wireless and computational resources of a mobile edge computing (MEC) system. The proposed HGRAA is composed of two levels: the lower-level evolutionary game (LEG) minimizes the cost of mobile terminals (MTs), and the upper-level exact potential game (UEPG) maximizes the utility of MEC servers. At the lower-level, the MTs are divided into delay-sensitive MTs (DSMTs) and non-delay-sensitive MTs (NDSMTs) according to their different quality of service (QoS) requirements. The competition among DSMTs and NDSMTs in different service areas to share the limited available wireless and computational resources is formulated as a dynamic evolutionary game. The dynamic replicator is applied to obtain the evolutionary equilibrium so as to minimize the costs imposed on MTs. At the upper level, the exact potential game is formulated to solve the resource sharing problem among MEC servers and the resource sharing problem is transferred to nonlinear complementarity. The existence of Nash equilibrium (NE) is proved and is obtained through the Karush-Kuhn-Tucker (KKT) condition. Simulations illustrate that substantial performance improvements such as average utility and the resource utilization of MEC servers can be achieved by applying the proposed HGRAA. Moreover, the cost of MTs is significantly lower than other existing algorithms with the increasing size of input data, and the QoS requirements of different kinds of MTs are well guaranteed in terms of average delay and transmission data rate.},
keywords={},
doi={10.1587/transcom.2020EBP3171},
ISSN={1745-1345},
month={November},}
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TY - JOUR
TI - Joint Wireless and Computational Resource Allocation Based on Hierarchical Game for Mobile Edge Computing
T2 - IEICE TRANSACTIONS on Communications
SP - 1395
EP - 1407
AU - Weiwei XIA
AU - Zhuorui LAN
AU - Lianfeng SHEN
PY - 2021
DO - 10.1587/transcom.2020EBP3171
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2021
AB - In this paper, we propose a hierarchical Stackelberg game based resource allocation algorithm (HGRAA) to jointly allocate the wireless and computational resources of a mobile edge computing (MEC) system. The proposed HGRAA is composed of two levels: the lower-level evolutionary game (LEG) minimizes the cost of mobile terminals (MTs), and the upper-level exact potential game (UEPG) maximizes the utility of MEC servers. At the lower-level, the MTs are divided into delay-sensitive MTs (DSMTs) and non-delay-sensitive MTs (NDSMTs) according to their different quality of service (QoS) requirements. The competition among DSMTs and NDSMTs in different service areas to share the limited available wireless and computational resources is formulated as a dynamic evolutionary game. The dynamic replicator is applied to obtain the evolutionary equilibrium so as to minimize the costs imposed on MTs. At the upper level, the exact potential game is formulated to solve the resource sharing problem among MEC servers and the resource sharing problem is transferred to nonlinear complementarity. The existence of Nash equilibrium (NE) is proved and is obtained through the Karush-Kuhn-Tucker (KKT) condition. Simulations illustrate that substantial performance improvements such as average utility and the resource utilization of MEC servers can be achieved by applying the proposed HGRAA. Moreover, the cost of MTs is significantly lower than other existing algorithms with the increasing size of input data, and the QoS requirements of different kinds of MTs are well guaranteed in terms of average delay and transmission data rate.
ER -