Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks
With the explosion of data traffic, mobile edge computing (MEC) has emerged to solve the problem of high time delay and energy consumption. In order to cope with a large number of computing tasks, the deployment of edge servers is increasingly intensive. Thus, server service areas overlap. We focus...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/7016307 |
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| _version_ | 1832563172965351424 |
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| author | Shuang Chen Ying Chen Xin Chen Yuemei Hu |
| author_facet | Shuang Chen Ying Chen Xin Chen Yuemei Hu |
| author_sort | Shuang Chen |
| collection | DOAJ |
| description | With the explosion of data traffic, mobile edge computing (MEC) has emerged to solve the problem of high time delay and energy consumption. In order to cope with a large number of computing tasks, the deployment of edge servers is increasingly intensive. Thus, server service areas overlap. We focus on mobile users in overlapping service areas and study the problem of computation offloading for these users. In this paper, we consider a multiuser offloading scenario with intensive deployment of edge servers. In addition, we divide the offloading process into two stages, namely, data transmission and computation execution, in which channel interference and resource preemption are considered, respectively. We apply the noncooperative game method to model and prove the existence of Nash equilibrium (NE). The real-time update computation offloading algorithm (RUCO) is proposed to obtain equilibrium offloading strategies. Due to the high complexity of the RUCO algorithm, the multiuser probabilistic offloading decision (MPOD) algorithm is proposed to improve this problem. We evaluate the performance of the MPOD algorithm through experiments. The experimental results show that the MPOD algorithm can converge after a limited number of iterations and can obtain the offloading strategy with lower cost. |
| format | Article |
| id | doaj-art-a92f26555df040f8b5c77e39223335f3 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a92f26555df040f8b5c77e39223335f32025-02-03T01:20:47ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/70163077016307Distributed Task Offloading Game in Multiserver Mobile Edge Computing NetworksShuang Chen0Ying Chen1Xin Chen2Yuemei Hu3School of Computer Science, Beijing Information Science and Technology University, Beijing, ChinaSchool of Computer Science, Beijing Information Science and Technology University, Beijing, ChinaSchool of Computer Science, Beijing Information Science and Technology University, Beijing, ChinaNetwork Engineering Department, Information Science and Technology School, QuFu Normal University, Rizhao Campus, Shandong, ChinaWith the explosion of data traffic, mobile edge computing (MEC) has emerged to solve the problem of high time delay and energy consumption. In order to cope with a large number of computing tasks, the deployment of edge servers is increasingly intensive. Thus, server service areas overlap. We focus on mobile users in overlapping service areas and study the problem of computation offloading for these users. In this paper, we consider a multiuser offloading scenario with intensive deployment of edge servers. In addition, we divide the offloading process into two stages, namely, data transmission and computation execution, in which channel interference and resource preemption are considered, respectively. We apply the noncooperative game method to model and prove the existence of Nash equilibrium (NE). The real-time update computation offloading algorithm (RUCO) is proposed to obtain equilibrium offloading strategies. Due to the high complexity of the RUCO algorithm, the multiuser probabilistic offloading decision (MPOD) algorithm is proposed to improve this problem. We evaluate the performance of the MPOD algorithm through experiments. The experimental results show that the MPOD algorithm can converge after a limited number of iterations and can obtain the offloading strategy with lower cost.http://dx.doi.org/10.1155/2020/7016307 |
| spellingShingle | Shuang Chen Ying Chen Xin Chen Yuemei Hu Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks Complexity |
| title | Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks |
| title_full | Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks |
| title_fullStr | Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks |
| title_full_unstemmed | Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks |
| title_short | Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks |
| title_sort | distributed task offloading game in multiserver mobile edge computing networks |
| url | http://dx.doi.org/10.1155/2020/7016307 |
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