Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization  

作　　者：Lei Zeng Qi Liu Shigen Shen Xiaodong Liu 

机构地区：[1]School of Computer Science,Nanjing University of Information Science and Technology,Nanjing 210044,China [2]School of Software,Nanjing University of Information Science and Technology,Nanjing 210044,China [3]School of Information Engineering,Huzhou University,Huzhou 313000,China [4]School of Computing,Edinburgh Napier University,Edinburgh,EH105DT,UK

出　　处：《Tsinghua Science and Technology》2024年第3期806-817,共12页清华大学学报（自然科学版（英文版）

基　　金：supported by the National Key Research and Development Program of China(No.2021YFE0116900);National Natural Science Foundation of China(Nos.42275157,62002276,and 41975142);Major Program of the National Social Science Fund of China(No.17ZDA092).

摘　　要：Edge computing nodes undertake an increasing number of tasks with the rise of business density.Therefore,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical challenge.This study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the agent.The management of experience data are also modified to fully utilize its value and improve learning efficiency.Reinforcement learning agents usually learn from an ignorant state,which is inefficient.As such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task scheduling.These optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition level.The proposed algorithm is compared with six other methods in simulation experiments.Results show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关 键 词：edge computing task scheduling reinforcement learning MAKESPAN Double Deep Q Network(DQN) 

分 类 号：TH186[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]