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

Abstract

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.

Keywords

• Mobile edge computing
• multidimensional multiple knapsack problem
• multi-pointer networks
• reinforcement learning