文献类型：期刊文献

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

作者：Song, Yao[1,2];Xiao, Limin[1,2];Wang, Liang[2];Qin, Guangjun[3];Wei, Bing[1,2];Yan, Baicheng[1,2];Zhang, Chenhao[1,2]

第一作者：Song, Yao

通讯作者：Xiao, LM[1];Xiao, LM[2];Qin, GJ[3]

机构：[1]Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China;[2]Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China;[3]Beijing Union Univ, Smart City Coll, Beijing 100101, Peoples R China

第一机构：Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China

通讯机构：[1]corresponding author), Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China;[2]corresponding author), Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China;[3]corresponding author), Beijing Union Univ, Smart City Coll, Beijing 100101, Peoples R China.|[1141733]北京联合大学继续教育学院;[11417]北京联合大学;

年份：2022

卷号：16

期号：5

外文期刊名：FRONTIERS OF COMPUTER SCIENCE

收录：;EI(收录号：20220311463735);Scopus(收录号：2-s2.0-85122684610);WOS:【SCI-EXPANDED(收录号:WOS:000740246400002)】；

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), and the fund of the State Key Laboratory of Software Development Environment (SKLSDE-2020ZX-15).

语种：英文

外文关键词：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.