选址问题是任何一个商业机构都要面临的重大决策问题之一,它受多种因素制约,比如社会经济学、地质学、生态学以及决策者的特定需求等。现有的选址方法(通常被经济学家采用)大多利用主观评价,可扩展性差。空间co-location模式挖掘是空间...选址问题是任何一个商业机构都要面临的重大决策问题之一,它受多种因素制约,比如社会经济学、地质学、生态学以及决策者的特定需求等。现有的选址方法(通常被经济学家采用)大多利用主观评价,可扩展性差。空间co-location模式挖掘是空间数据挖掘的一个重要研究方向。一个频繁co-location模式是一组空间特征的子集,它们的实例在空间中频繁关联。利用co-location模式的这种特征间"共存"关系,提出了一种基于co-location模式的地址选择算法,该算法基于本体描述空间数据的分类信息,并在本体的指导下对用户感兴趣的兴趣点(Point of Interest)进行关键co-location模式挖掘,同时针对实际情况对数据进行了预处理以增加算法的有效性。在真实数据集(北京市的兴趣点数据)上的评估实验显示该算法具有较高的准确率,选择的地址具有高可靠性。展开更多
Workload characterization is critical for resource management and scheduling.Recently,with the fast development of container technique,more and more cloud service providers like Google and Alibaba adopt containers to ...Workload characterization is critical for resource management and scheduling.Recently,with the fast development of container technique,more and more cloud service providers like Google and Alibaba adopt containers to provide cloud services,due to the low overheads.However,the characteristics of co-located diverse services(e.g.,interactive on-line services,off-line computing services)running in containers are still not clear.In this paper,we present a comprehensive analysis of the characteristics of co-located workloads running in containers on the same server from the perspective of hardware events.Our study quantifies and reveals the system behavior from the micro-architecture level when workloads are running in different co-location patterns.Through the analysis of typical hardware events,we provide recommended/unrecommended co-location workload patterns which provide valuable deployment suggestions for datacenter administrators.展开更多
文摘选址问题是任何一个商业机构都要面临的重大决策问题之一,它受多种因素制约,比如社会经济学、地质学、生态学以及决策者的特定需求等。现有的选址方法(通常被经济学家采用)大多利用主观评价,可扩展性差。空间co-location模式挖掘是空间数据挖掘的一个重要研究方向。一个频繁co-location模式是一组空间特征的子集,它们的实例在空间中频繁关联。利用co-location模式的这种特征间"共存"关系,提出了一种基于co-location模式的地址选择算法,该算法基于本体描述空间数据的分类信息,并在本体的指导下对用户感兴趣的兴趣点(Point of Interest)进行关键co-location模式挖掘,同时针对实际情况对数据进行了预处理以增加算法的有效性。在真实数据集(北京市的兴趣点数据)上的评估实验显示该算法具有较高的准确率,选择的地址具有高可靠性。
基金This work is supported by the National Key Research and Development Program of China under Grant No.2018YFB1004804the National Natural Science Foundation of China under Grant No.61702492the Shenzhen Basic Research Program under Grant Nos.JCYJ20170818153016513 and JCYJ20170307164747920,and Alibaba Innovative Research(AIR)Project.
文摘Workload characterization is critical for resource management and scheduling.Recently,with the fast development of container technique,more and more cloud service providers like Google and Alibaba adopt containers to provide cloud services,due to the low overheads.However,the characteristics of co-located diverse services(e.g.,interactive on-line services,off-line computing services)running in containers are still not clear.In this paper,we present a comprehensive analysis of the characteristics of co-located workloads running in containers on the same server from the perspective of hardware events.Our study quantifies and reveals the system behavior from the micro-architecture level when workloads are running in different co-location patterns.Through the analysis of typical hardware events,we provide recommended/unrecommended co-location workload patterns which provide valuable deployment suggestions for datacenter administrators.
