文献类型：期刊文献

中文题名：GCSS:a global collaborative scheduling strategy for wide-area high-performance computing

作者：Yao SONG[1,2];Limin XIAO[1,2];Liang WANG[2];Guangjun QIN[3];Bing WEI[1,2];Baicheng YAN[1,2];Chenhao ZHANG[1,2]

第一作者：Yao SONG

机构：[1]State Key Laboratory of Software Development Environment,Beihang University,Beijing 100191,China;[2]School of Computer Science and Engineering,Beihang University,Beijing 100191,China;[3]Smart City College,Beijing Union University,Beijing 100101,China

第一机构：State Key Laboratory of Software Development Environment,Beihang University,Beijing 100191,China

年份：2022

卷号：16

期号：5

起止页码：1-15

中文期刊名：中国计算机科学前沿：英文版

外文期刊名：Frontiers of Computer Science

收录：CSTPCD;;Scopus;CSCD:【CSCD2021_2022】；PubMed;

基金：This work was supported by the National key R&D Program of China(2018YFB0203901);the National Natural Science Foundation of China under(Grant No.61772053);the fund of the State Key Laboratory of Software Development Environment(SKLSDE-2020ZX15).

语种：英文

中文关键词：high-performance computing;scheduling strategy;task scheduling;data placement

摘要：Wide-area high-performance computing is widely used for large-scale parallel computing applications owing to its high computing and storage resources.However,the geographical distribution of computing and storage resources makes efficient task distribution and data placement more challenging.To achieve a higher system performance,this study proposes a two-level global collaborative scheduling strategy for wide-area high-performance computing environments.The collaborative scheduling strategy integrates lightweight solution selection,redundant data placement and task stealing mechanisms,optimizing task distribution and data placement to achieve efficient computing in wide-area environments.The experimental results indicate that compared with the state-of-the-art collaborative scheduling algorithm HPS+,the proposed scheduling strategy reduces the makespan by 23.24%,improves computing and storage resource utilization by 8.28%and 21.73%respectively,and achieves similar global data migration costs.