Neural Combinatorial Optimization for Energy-Efficient Offloading in Mobile Edge Computing

Computation offloading is an efficient approach to reduce the energy consumption of a mobile device (MD). In this paper, we consider the multi-user offloading problem for mobile edge computing (MEC) in a multi-server environment. Its aim is to minimize the total energy consumption of MDs. This probl...

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Veröffentlicht in:IEEE access Jg. 8; S. 35077 - 35089
Hauptverfasser: Jiang, Qingmiao, Zhang, Yuan, Yan, Jinyao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Approximation algorithms
Combinatorial analysis
Computation offloading
Computational modeling
Computer architecture
Edge computing
Electronic devices
Energy consumption
Heuristic methods
Machine learning
Mathematical programming
Mobile computing
Mobile edge computing
multi-pointer networks
multidimensional multiple knapsack problem
Neural networks
Optimization
reinforcement learning
Servers
Task analysis
ISSN:2169-3536, 2169-3536
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Zusammenfassung:Computation offloading is an efficient approach to reduce the energy consumption of a mobile device (MD). In this paper, we consider the multi-user offloading problem for mobile edge computing (MEC) in a multi-server environment. Its aim is to minimize the total energy consumption of MDs. This problem has been proven to be NP-hard. We formulate the problem as a multidimensional multiple knapsack (MMKP) problem with constraints, and propose a neural network architecture called Multi-Pointer networks (Mptr-Net) to solve the problem. We train Mptr-Net based on the reinforcement learning method, and design an algorithm to search for feasible solutions that meet the constraints. The simulation results show that the probability of a Mptr-Net obtaining an optimal solution can exceed 98%, which is approximately 25% more than that of a baseline heuristic algorithm. Additionally, the time needed to solve the problem by our neural network is stable compared with that of a mathematical programming solver named or-tools.
Bibliographie:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2974484