Paper Info
Title
Dynamic Computation Offloading and Resource Allocation for Multi-user Mobile Edge Computing.
Abstract
We study the problem of dynamic computation offloading and resource allocation in mobile edge computing (MEC) systems consisting of multiple mobile users (MUs) with stochastic task arrivals and wireless channels. Each MU can execute its task either locally or remotely in an MEC server. The objective is to identify the optimum scheduling scheme that can minimize the long-term average weighted sum of energy consumption and delay of all MUs, under the constraints of limited transmission power per MU and limited computation resources at the MEC server. The optimum design is performed with respect to three decision parameters: whether to offload a given task, how much transmission power to be allocated for offloading, and how much MEC resources to be allocated for an offloaded task. We propose to solve the problem by developing a dynamic scheduling strategy based on deep reinforcement learning (DRL) with deep deterministic policy gradient (DDPG). Simulation results show that the proposed algorithm outperforms other existing strategies such as deep Q-network (DQN).
Year
DOI
Venue
2020
10.1109/GLOBECOM42002.2020.9348161
GLOBECOM
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
11
2
Name
Order
Citations
PageRank
Samrat Nath141.43
Jingxian Wu2605.00