文献类型：期刊文献

英文题名：M-E-AWA: A Novel Task Scheduling Approach Based on Weight Vector Adaptive Updating for Fog Computing

作者：Dai, Zhiming[1,2];Ding, Weichao[1];Min, Qi[1];Gu, Chunhua[1];Yao, Baohua[3];Shen, Xiaohan[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Jian Qiao Univ, Sch Informat Technol, Shanghai 201306, Peoples R China;[3]Shanghai Inst Civil Def Sci, Shanghai 200020, Peoples R China

年份：2023

卷号：11

期号：4

外文期刊名：PROCESSES

收录：;EI(收录号：20241515858432);WOS:【SCI-EXPANDED(收录号:WOS:000979341000001)】；

基金：This work was sponsored by the Nature Science Foundation of Shanghai, China, 23ZR1414900; Shanghai Sailing Program, China, 20YF1410900; Shanghai Science and Technology Innovation Action Plan, China, 22ZR1416500; Shanghai Science and Technology Innovation Action Plan, China, 20dz1201400.

语种：英文

外文关键词：fog computing; task scheduling; multi-objective evolutionary algorithm; MOEA; D

摘要：Task offloading and real-time scheduling are hot topics in fog computing. This paper aims to address the challenges of complex modeling and solving multi-objective task scheduling in fog computing environments caused by widely distributed resources and strong load uncertainties. Firstly, a task unloading model based on dynamic priority adjustment is proposed. Secondly, a multi-objective optimization model is constructed for task scheduling based on the task unloading model, which optimizes time delay and energy consumption. The experimental results show that M-E-AWA (MOEA/D with adaptive weight adjustment based on external archives) can effectively handle multi-objective optimization problems with complex Pareto fronts and reduce the response time and energy consumption costs of task scheduling.