The scheduling problem on a single batching machine with family jobs was proposed.The single batching machine can process a group of jobs simultaneously as a batch.Jobs in the same batch complete at the same time.The ...The scheduling problem on a single batching machine with family jobs was proposed.The single batching machine can process a group of jobs simultaneously as a batch.Jobs in the same batch complete at the same time.The batch size is assumed to be unbounded.Jobs that belong to different families can not be processed in the same batch.The objective function is minimizing maximum lateness.For the problem with fixed number of m families and n jobs,a polynomial time algorithm based on dynamic programming with time complexity of O(n(n/m+1)m)was presented.展开更多
Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competi...Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competition between batch jobs and online services,co-location frequently impairs the performance of online services.This study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for co-locatedworkloads.The performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on randomforest.On-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch jobs.When the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling strategy.Compared with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on average.Experiments show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.展开更多
为探讨差异尺寸作业批调度研究现状和进展,以发表在2000~2021年、收录于Web of Science(WOS)数据库的相关文献为研究对象,借助CiteSpace软件对这些文献的期刊共被引、主要研究力量的发文与合作、关键词共现与突现等情况进行可视化分析...为探讨差异尺寸作业批调度研究现状和进展,以发表在2000~2021年、收录于Web of Science(WOS)数据库的相关文献为研究对象,借助CiteSpace软件对这些文献的期刊共被引、主要研究力量的发文与合作、关键词共现与突现等情况进行可视化分析。文献计量结果表明,尽管我国的研究机构在差异尺寸作业批调度上的文献产出量大,但产出文献的整体学术影响力还有待提升;高频关键词集中在加工环境、优化目标、问题求解技术3方面;截至2021年仍保持高突现强度的关键词有增材制造、恶化作业、能源消耗、多目标优化。最后给出差异尺寸作业批调度研究的未来发展方向,以期为后续研究提供有益参考。展开更多
基金National Natural Science Foundation of China(No.70832002)Graduate Student Innovation Fund of Fudan University,China
文摘The scheduling problem on a single batching machine with family jobs was proposed.The single batching machine can process a group of jobs simultaneously as a batch.Jobs in the same batch complete at the same time.The batch size is assumed to be unbounded.Jobs that belong to different families can not be processed in the same batch.The objective function is minimizing maximum lateness.For the problem with fixed number of m families and n jobs,a polynomial time algorithm based on dynamic programming with time complexity of O(n(n/m+1)m)was presented.
基金supported by the NationalNatural Science Foundation of China(No.61972118)the Key R&D Program of Zhejiang Province(No.2023C01028).
文摘Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competition between batch jobs and online services,co-location frequently impairs the performance of online services.This study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for co-locatedworkloads.The performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on randomforest.On-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch jobs.When the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling strategy.Compared with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on average.Experiments show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.
文摘为探讨差异尺寸作业批调度研究现状和进展,以发表在2000~2021年、收录于Web of Science(WOS)数据库的相关文献为研究对象,借助CiteSpace软件对这些文献的期刊共被引、主要研究力量的发文与合作、关键词共现与突现等情况进行可视化分析。文献计量结果表明,尽管我国的研究机构在差异尺寸作业批调度上的文献产出量大,但产出文献的整体学术影响力还有待提升;高频关键词集中在加工环境、优化目标、问题求解技术3方面;截至2021年仍保持高突现强度的关键词有增材制造、恶化作业、能源消耗、多目标优化。最后给出差异尺寸作业批调度研究的未来发展方向,以期为后续研究提供有益参考。
