摘要
随着各行业对遥感共性产品需求的不断增加,高性能遥感产品生产系统的应用范围不断扩大。优秀的任务调度算法作为该系统的关键部件,能显著提高生产效率。然而,在遥感共性产品的生产过程中面临特有的挑战,如果大量的工作流在短时间内被提交生产,这些工作流在处理中存在重复计算和数据处理的问题,且生成共性产品所需的数据量往往较大,流程处理时间长,很容易导致资源浪费和生产效率下降。为了解决这一问题,提出一种基于产品复用模型的任务划分策略。该策略着眼于优化工作流处理,首先将用户提交的工作流按照任务重复度打包成流程包,把带有重复任务的流程分配到同一个计算节点,旨在减少节点间的数据传输时间;然后引入一种产品复用模型,允许不同的处理流程复用已获得的产品结果,减少重复性计算和数据处理,从而提高生产效率,满足共性产品生产的高效化需求。为了验证所提算法的有效性,将所提算法和传统算法FCFS,SJF分别在CloudSim仿真模拟器中进行模拟实验。结果表明,所提调度算法任务的总完成时间和任务的平均响应时间均显著低于对比算法,展现出了更为优秀的性能。
With the increasing demand for remote sensing common products in various industries,the application of high-perfor-mance remote sensing product production system is increasing.As a key component of the system,excellent task scheduling algorithm can significantly improve its production efficiency.However,there are unique challenges in the production process of remote sensing generic products.If a large number of workflows are submitted for production in a short time,there are problems of double calculation and data processing in the processing of these workflows,and the amount of data required to generate generic products is often large,and the process processing time is long,which easily leads to resource waste and production efficiency decline.In order to solve this problem,this paper proposes a task division strategy based on product reuse model,which focuses on optimizing workflow processing.Firstly,workflow submitted by users is packaged into a process package according to task repetition,and processes with repetitive tasks are assigned to the same computing node to reduce the data transmission time between nodes.Then,a product reuse model is introduced to allow different processing processes to reuse the obtained product results,reduce repetitive calculation and data processing,so as to improve production efficiency and meet the high efficiency needs of common product production.In order to verify the effectiveness of the proposed algorithm,the proposed algorithm and other traditional algorithms FCFS and SJF are simulated in the CloudSim simulation simulator respectively.The results show that the proposed scheduling algorithm has significantly lower total task completion time and average task response time than the other two algorithms,showing better performance.
作者
左宪禹
周小虎
周黎明
谢毅
刘成
ZUO Xianyu;ZHOU Xiaohu;ZHOU Liming;XIE Yi;LIU Cheng(Henan Key Laboratory of Big Data Analysis and Processing,Henan University,Kaifeng,Henan 475000,China;School of Computer and Information Engineering,Henan University,Kaifeng,Henan 475000,China)
出处
《计算机科学》
北大核心
2025年第6期316-323,共8页
Computer Science
基金
国家重点研发计划国际合作专项(2019YFE0126600)
河南省科技重大专项(201400210300)
河南省高校科技创新团队(24IRTSTHN021)
河南省科技厅科技攻关项目(232102210009)
河南省科技攻关(242102240021)
2024河南省研究生联合培养基地项目(YJS2024JD30)。
