为研究泵驱动两相复合制冷机组的工作特性,特别是换热性能随工质泵频率和室内外温差的变化规律,搭建泵驱动两相复合制冷机组的实验装置,对其进行了实验研究.在本实验装置中,蒸气压缩制冷模式分别采用2.63 k W压缩机和3.75 k W压缩机作...为研究泵驱动两相复合制冷机组的工作特性,特别是换热性能随工质泵频率和室内外温差的变化规律,搭建泵驱动两相复合制冷机组的实验装置,对其进行了实验研究.在本实验装置中,蒸气压缩制冷模式分别采用2.63 k W压缩机和3.75 k W压缩机作为驱动元件,泵驱动两相冷却模式采用带变频器的工质泵驱动.结果表明:室内温度25℃、室外温度10℃时,机组换热量随工质泵频率先增大后减小,在工质泵频率为35 Hz时达到最大值;能效比(energy efficiency ratio,EER)随工质泵频率初始变化不明显,然后逐渐降低.在室外温度为0~30℃时,实验获得了2种运行模式的性能变化情况和最佳转换温度.展开更多
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
文摘为研究泵驱动两相复合制冷机组的工作特性,特别是换热性能随工质泵频率和室内外温差的变化规律,搭建泵驱动两相复合制冷机组的实验装置,对其进行了实验研究.在本实验装置中,蒸气压缩制冷模式分别采用2.63 k W压缩机和3.75 k W压缩机作为驱动元件,泵驱动两相冷却模式采用带变频器的工质泵驱动.结果表明:室内温度25℃、室外温度10℃时,机组换热量随工质泵频率先增大后减小,在工质泵频率为35 Hz时达到最大值;能效比(energy efficiency ratio,EER)随工质泵频率初始变化不明显,然后逐渐降低.在室外温度为0~30℃时,实验获得了2种运行模式的性能变化情况和最佳转换温度.
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.
