A PSO Improved with Imbalanced Mutation and Task Rescheduling for Task Offloading in End-Edge-Cloud Computing  

作　　者：Kaili Shao Hui Fu Ying Song Bo Wang 

机构地区：[1]Faculty of Engineering,Huanghe Science and Technology University,Zhengzhou 450003,China [2]Computer School,Beijing Information Science and Technology University,Beijing,100192,China [3]Software Engineering College,Zhengzhou University of Light Industry,Zhengzhou,450001,China

出　　处：《Computer Systems Science & Engineering》2023年第11期2259-2274,共16页计算机系统科学与工程（英文）

基　　金：supported by the key scientific and technological projects of Henan Province with Grant No.232102211084;the Natural Science Foundation of Henan with Grant No.222300420582;the Key Scientific Research Projects of Henan Higher School with Grant No.22A520033;Zhengzhou Basic Research and Applied Research Project with Grant No.ZZSZX202107;China Logistics Society with Grant No.2022CSLKT3-334.

摘　　要：To serve various tasks requested by various end devices with different requirements,end-edge-cloud(E2C)has attracted more and more attention from specialists in both academia and industry,by combining both benefits of edge and cloud computing.But nowadays,E2C still suffers from low service quality and resource efficiency,due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility.To address these issues,this paper focuses on task offloading,which makes decisions that which resources are allocated to tasks for their processing.This paper first formulates the problem into binary non-linear programming and then proposes a particle swarm optimization(PSO)-based algorithm to solve the problem.The proposed algorithm exploits an imbalance mutation operator and a task rescheduling approach to improve the performance of PSO.The proposed algorithm concerns the resource heterogeneity by correlating the probability that a computing node is decided to process a task with its capacity,by the imbalance mutation.The task rescheduling approach improves the acceptance ratio for a task offloading solution,by reassigning rejected tasks to computing nodes with available resources.Extensive simulated experiments are conducted.And the results show that the proposed offloading algorithm has an 8.93%–37.0%higher acceptance ratio than ten of the classical and up-to-date algorithms,and verify the effectiveness of the imbalanced mutation and the task rescheduling.

关 键 词：Cloud computing edge computing edge cloud task scheduling task offloading particle swarm optimization 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]