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.
Highlights
As one of the key technologies of 5G networks, mobile edge computing (MEC) can effectively augment the computation and energy capacity of mobile device (MD) by offloading tasks to edge servers (ESs) [1]–[3]
Our objective is to find a permutation of π =, where πmd denotes a permutation of MDs and πes denotes a permutation of ESs, that minimizes the sum of energy consumption values of MDs subject to the constraints of total computational resources and transmission rate
In this paper, a neural network architecture called Mptr-Net is proposed to solve a mobile edge computation offloading problem, which is formulated as a multidimensional multiple knapsack problem with constraints
Summary
As one of the key technologies of 5G networks, MEC can effectively augment the computation and energy capacity of MDs by offloading tasks to edge servers (ESs) [1]–[3]. The third category consists of approximation algorithms that can provide theoretically bounded results in polynomial time They are usually suitable to particular problem formulations and not generally applicable. A neural network can automatically learn heuristic rules from the training data, and has significant potential for finding the optimal solution of the problem. In contrast to the traditional method, we propose a neural network approach to optimize the NP-hard MEC offloading problem. The proposed training algorithm is dedicated to training neural networks to learn heuristic rules for solving the MMKP problem.
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