To reduce energy consumption in cloud data centres,in this paper,we propose two algorithms called the Energy-aware Scheduling algorithm using Workload-aware Consolidation Technique(ESWCT) and the Energyaware Live Migr...To reduce energy consumption in cloud data centres,in this paper,we propose two algorithms called the Energy-aware Scheduling algorithm using Workload-aware Consolidation Technique(ESWCT) and the Energyaware Live Migration algorithm using Workload-aware Consolidation Technique(ELMWCT).As opposed to traditional energy-aware scheduling algorithms,which often focus on only one-dimensional resource,the two algorithms are based on the fact that multiple resources(such as CPU,memory and network bandwidth)are shared by users concurrently in cloud data centres and heterogeneous workloads have different resource consumption characteristics.Both algorithms investigate the problem of consolidating heterogeneous workloads.They try to execute all Virtual Machines(VMs) with the minimum amount of Physical Machines(PMs),and then power off unused physical servers to reduce power consumption.Simulation results show that both algorithms efficiently utilise the resources in cloud data centres,and the multidimensional resources have good balanced utilizations,which demonstrate their promising energy saving capability.展开更多
Aiming at the storage and management problems of massive remote sensing data,this paper gives a comprehensive analysis of the characteristics and advantages of thirteen data storage centers or systems at home and abro...Aiming at the storage and management problems of massive remote sensing data,this paper gives a comprehensive analysis of the characteristics and advantages of thirteen data storage centers or systems at home and abroad. They mainly include the NASA EOS,World Wind,Google Earth,Google Maps,Bing Maps,Microsoft TerraServer,ESA,Earth Simulator,GeoEye,Map World,China Centre for Resources Satellite Data and Application,National Satellite Meteorological Centre,and National Satellite Ocean Application Service. By summing up the practical data storage and management technologies in terms of remote sensing data storage organization and storage architecture,it will be helpful to seek more suitable techniques and methods for massive remote sensing data storage and management.展开更多
基金supported by the Opening Project of State key Laboratory of Networking and Switching Technology under Grant No.SKLNST-2010-1-03the National Natural Science Foundation of China under Grants No.U1333113,No.61303204+1 种基金the Sichuan Province seedling project under Grant No.2012ZZ036the Scientific Research Fund of Sichuan Normal University under Grant No.13KYL06
文摘To reduce energy consumption in cloud data centres,in this paper,we propose two algorithms called the Energy-aware Scheduling algorithm using Workload-aware Consolidation Technique(ESWCT) and the Energyaware Live Migration algorithm using Workload-aware Consolidation Technique(ELMWCT).As opposed to traditional energy-aware scheduling algorithms,which often focus on only one-dimensional resource,the two algorithms are based on the fact that multiple resources(such as CPU,memory and network bandwidth)are shared by users concurrently in cloud data centres and heterogeneous workloads have different resource consumption characteristics.Both algorithms investigate the problem of consolidating heterogeneous workloads.They try to execute all Virtual Machines(VMs) with the minimum amount of Physical Machines(PMs),and then power off unused physical servers to reduce power consumption.Simulation results show that both algorithms efficiently utilise the resources in cloud data centres,and the multidimensional resources have good balanced utilizations,which demonstrate their promising energy saving capability.
基金supported by the National Basic Research Program of China ("973" Program) (Grant No.61399)
文摘Aiming at the storage and management problems of massive remote sensing data,this paper gives a comprehensive analysis of the characteristics and advantages of thirteen data storage centers or systems at home and abroad. They mainly include the NASA EOS,World Wind,Google Earth,Google Maps,Bing Maps,Microsoft TerraServer,ESA,Earth Simulator,GeoEye,Map World,China Centre for Resources Satellite Data and Application,National Satellite Meteorological Centre,and National Satellite Ocean Application Service. By summing up the practical data storage and management technologies in terms of remote sensing data storage organization and storage architecture,it will be helpful to seek more suitable techniques and methods for massive remote sensing data storage and management.
