Dynamic Computation Offloading Based on Graph Partitioning in Mobile Edge Computing
Mobile edge computing is a new cloud computing paradigm that utilizes small-sized edge clouds to provide real-time services to users. These mobile edge clouds (MECs) are located near users, thereby enabling users to seamlessly access applications that are running on MECs and to easily access MECs. T...
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doaj-69a32594d8ac41a882fe33c4a6ef0d982021-03-29T23:15:51ZengIEEEIEEE Access2169-35362019-01-01718513118513910.1109/ACCESS.2019.29608878936864Dynamic Computation Offloading Based on Graph Partitioning in Mobile Edge ComputingGuangshun Li0https://orcid.org/0000-0001-6147-0637Qingyan Lin1https://orcid.org/0000-0002-4851-8410Junhua Wu2https://orcid.org/0000-0001-8723-2389Ying Zhang3https://orcid.org/0000-0002-4745-5243Jiahe Yan4https://orcid.org/0000-0001-9339-8545School of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaMobile edge computing is a new cloud computing paradigm that utilizes small-sized edge clouds to provide real-time services to users. These mobile edge clouds (MECs) are located near users, thereby enabling users to seamlessly access applications that are running on MECs and to easily access MECs. Terminal devices can transfer tasks to MEC servers nearby to improve the quality of computing. In this paper, we study multi-user computation offloading problem for mobile-edge computing in a multichannel wireless interference environment. Then, we analyze the overhead of each mobile devices, and we propose strategies for task scheduling and offloading in a multi-user MEC system. For reducing the energy consumption, we propose a server partitioning algorithm that is based on clustering. We formulate the task offloading decision problem as a multi-user game, which always has a Nash equilibrium. The simulation results demonstrate that our scheme outperforms the traditional offloading strategy in terms of energy consumption.https://ieeexplore.ieee.org/document/8936864/Mobile edge computingoffloading decisionnode clusteringoptimal strategyNash equilibrium |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guangshun Li Qingyan Lin Junhua Wu Ying Zhang Jiahe Yan |
spellingShingle |
Guangshun Li Qingyan Lin Junhua Wu Ying Zhang Jiahe Yan Dynamic Computation Offloading Based on Graph Partitioning in Mobile Edge Computing IEEE Access Mobile edge computing offloading decision node clustering optimal strategy Nash equilibrium |
author_facet |
Guangshun Li Qingyan Lin Junhua Wu Ying Zhang Jiahe Yan |
author_sort |
Guangshun Li |
title |
Dynamic Computation Offloading Based on Graph Partitioning in Mobile Edge Computing |
title_short |
Dynamic Computation Offloading Based on Graph Partitioning in Mobile Edge Computing |
title_full |
Dynamic Computation Offloading Based on Graph Partitioning in Mobile Edge Computing |
title_fullStr |
Dynamic Computation Offloading Based on Graph Partitioning in Mobile Edge Computing |
title_full_unstemmed |
Dynamic Computation Offloading Based on Graph Partitioning in Mobile Edge Computing |
title_sort |
dynamic computation offloading based on graph partitioning in mobile edge computing |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Mobile edge computing is a new cloud computing paradigm that utilizes small-sized edge clouds to provide real-time services to users. These mobile edge clouds (MECs) are located near users, thereby enabling users to seamlessly access applications that are running on MECs and to easily access MECs. Terminal devices can transfer tasks to MEC servers nearby to improve the quality of computing. In this paper, we study multi-user computation offloading problem for mobile-edge computing in a multichannel wireless interference environment. Then, we analyze the overhead of each mobile devices, and we propose strategies for task scheduling and offloading in a multi-user MEC system. For reducing the energy consumption, we propose a server partitioning algorithm that is based on clustering. We formulate the task offloading decision problem as a multi-user game, which always has a Nash equilibrium. The simulation results demonstrate that our scheme outperforms the traditional offloading strategy in terms of energy consumption. |
topic |
Mobile edge computing offloading decision node clustering optimal strategy Nash equilibrium |
url |
https://ieeexplore.ieee.org/document/8936864/ |
work_keys_str_mv |
AT guangshunli dynamiccomputationoffloadingbasedongraphpartitioninginmobileedgecomputing AT qingyanlin dynamiccomputationoffloadingbasedongraphpartitioninginmobileedgecomputing AT junhuawu dynamiccomputationoffloadingbasedongraphpartitioninginmobileedgecomputing AT yingzhang dynamiccomputationoffloadingbasedongraphpartitioninginmobileedgecomputing AT jiaheyan dynamiccomputationoffloadingbasedongraphpartitioninginmobileedgecomputing |
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1724189874748129280 |