中文摘要：

精力消费是为数据存储系统的一个关键问题，特别为现代数据中心。最近的调查证明了电源费用在一个典型数据中心等于大约 50% 总有成本，与被存储系统消费的大约 27% 系统电源。这份报纸瞄准提供一个有效解决方案给减少磁盘存储系统消费的精力，由建议一条新途径减少精力消费。不同于以前的途径，我们采用二个新图案。1 ) 我们介绍一个暑热知道、基于组的系统模型(女巫) 组织磁盘，所有磁盘在被划分成一个风骚的组和一个冷组。我们仅仅做在二个组之间的文件移植并且在一个单身的组以内避免移植，以便我们能减少文件移植的全部的费用。2 ) 我们使用一条按需的途径在象数据暑热的变化一样基于工作量变化的磁盘之中重新组织文件。我们进行包含二的踪迹驱动的实验真实并且九合成踪迹和我们根据不同度量标准使在我们的方法和竞争者方法之间的比较详细。当工作量变化时，结果证明我们的方法罐头动态地选择热文件和磁盘并且它能为所有踪迹减少精力消费。而且，它的时间表演比得上比较算法的。一般来说，我们的方法在所有实验展出最好的精力效率，并且维持在性能和精力消费之间的一宗改进交易有能力。

英文摘要：

Energy consumption has been a critical issue for data storage systems, especially for modern data centers. A recent survey has showed that power costs amount to about 50%of the total cost of ownership in a typical data center, with about 27% of the system power being consumed by storage systems. This paper aims at providing an effective solution to reducing the energy consumed by disk storage systems, by proposing a new approach to reduce the energy consumption. Differing from previous approaches, we adopt two new designs. 1） We introduce a hotness-aware and group-based system model （HAG） to organize the disks, in which all disks are partitioned into a hot group and a cold group. We only make file migration between the two groups and avoid the migration within a single group, so that we are able to reduce the total cost of file migration. 2） We use an on-demand approach to reorganize files among the disks that is based on workload change as well as the change of data hotness. We conduct trace-driven experiments involving two real and nine synthetic traces and we make detailed comparisons between our method and competitor methods according to different metrics. The results show that our method can dynamically select hot files and disks when the workload changes and that it is able to reduce energy consumption for all the traces. Furthermore, its time performance is comparable to that of the compared algorithms. In general, our method exhibits the best energy e？ciency in all experiments, and it is capable of maintaining an improved trade-off between performance and energy consumption.