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Game Theoretic Algorithm for Energy Efficient Mobile Edge Computing with Multiple Access Points

Mahn, Tobias ; Wirth, Maximilian ; Klein, Anja (2020)
Game Theoretic Algorithm for Energy Efficient Mobile Edge Computing with Multiple Access Points.
8th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (Mobile Cloud 2020). Oxford, United Kingdom (03.-06.08.2020)
doi: 10.1109/MobileCloud48802.2020.00013
Conference or Workshop Item, Bibliographie

Abstract

This paper considers a Mobile Edge Computing scenario with multiple mobile units (MUs), multiple access points (APs) and one cloudlet server. The MUs have to decide whether offloading their computation tasks to the cloudlet is energy wise beneficial. As there are multiple APs available to connect the MUs to the cloudlet and communication and computation resources have to be shared among all MUs, each MU also has to choose the AP for transmission that minimizes its offloading energy under the given fraction of the overall resources. The problem is formulated as a energy minimization problem with a maximum offloading time constraint. MUs not only need to consider the energy required for local computation or offloading, but simultaneously avoid an overlong processing time of offloaded computation. This joint offloading decision and resource allocation is divided into two subproblems in the proposed approach. The resource allocation problem is reformulated by using Lagrange multipliers and closed-forms for the calculation of the shared resources are found. These results can be integrated into the proposed game theoretic algorithm for the offloading decision problem. The algorithm is based on a potential game and therefore, can be proven to converge to a Nash equilibrium. Numerical results show a benefit of the proposed resource allocation strategy, a performance of the proposed game algorithm near the optimal solution and a fast algorithm execution time that can even be significantly improved by proposed sorting metrics.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Mahn, Tobias ; Wirth, Maximilian ; Klein, Anja
Type of entry: Bibliographie
Title: Game Theoretic Algorithm for Energy Efficient Mobile Edge Computing with Multiple Access Points
Language: English
Date: 8 July 2020
Publisher: IEEE
Book Title: Proceedings: 2020 8th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering
Event Title: 8th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (Mobile Cloud 2020)
Event Location: Oxford, United Kingdom
Event Dates: 03.-06.08.2020
DOI: 10.1109/MobileCloud48802.2020.00013
Abstract:

This paper considers a Mobile Edge Computing scenario with multiple mobile units (MUs), multiple access points (APs) and one cloudlet server. The MUs have to decide whether offloading their computation tasks to the cloudlet is energy wise beneficial. As there are multiple APs available to connect the MUs to the cloudlet and communication and computation resources have to be shared among all MUs, each MU also has to choose the AP for transmission that minimizes its offloading energy under the given fraction of the overall resources. The problem is formulated as a energy minimization problem with a maximum offloading time constraint. MUs not only need to consider the energy required for local computation or offloading, but simultaneously avoid an overlong processing time of offloaded computation. This joint offloading decision and resource allocation is divided into two subproblems in the proposed approach. The resource allocation problem is reformulated by using Lagrange multipliers and closed-forms for the calculation of the shared resources are found. These results can be integrated into the proposed game theoretic algorithm for the offloading decision problem. The algorithm is based on a potential game and therefore, can be proven to converge to a Nash equilibrium. Numerical results show a benefit of the proposed resource allocation strategy, a performance of the proposed game algorithm near the optimal solution and a fast algorithm execution time that can even be significantly improved by proposed sorting metrics.

Uncontrolled Keywords: DAAD
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Communications Engineering
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > B: Adaptation Mechanisms
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > B: Adaptation Mechanisms > Subproject B3: Economics of Adaption
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms > Subproject C5: Context-Centered Perspective
Date Deposited: 03 Apr 2024 13:12
Last Modified: 03 Apr 2024 13:12
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