Recently, high-energy consumption has become a serious concern for both storage servers and data centers. Recent
research studies have utilized the short response times of multiple speed disks to decrease energy consumption.
However, very few manufactures can produce the multiple speeds hard disk because of its complexity. The main
limitation to MAID system is that we must assume the frequently accessed data is less than 5%, in fact, in most strong
coupling system, the data can't be cached due to the access pattern and moreover the first accessed data which are
usually frequently accessed are not cached, as a result, system performance is heavily degraded. In this paper, we
propose the new storage system called saving energy RAID (SERAID), in which we place the frequently accessed data
into solid state disks (SSD) and place the less frequently accessed ones into conventional hard disks (CHD). Because the
energy consumption is very low and the random read/write rate is very fast in SSD, we can get high availability and high
saving energy RAID system at the expense of very few costs. The simulation result shows that the random write performance
of SERAID is 10 times rapid than those of traditional RAID and the random read performance of SERAID is
5 times rapid than those of conventional RAID. Besides that, the mean energy consumption of SERAIDsystem is lower
than that of traditional RAID.
Nowadays, content-based network storage has become the hot research spot of academy and corporation. In order to
solve the problem of hit rate decline causing by migration and achieve the content-based query, we exploit a new
content-aware storage system which supports metadata retrieval to improve the query performance. Firstly, we extend
the SCSI command descriptor block to enable system understand those self-defined query requests. Secondly, the
extracted metadata is encoded by extensible markup language to improve the universality. Thirdly, according to the
demand of information lifecycle management (ILM), we store those data in different storage level and use corresponding
query strategy to retrieval them. Fourthly, as the file content identifier plays an important role in locating data and
calculating block correlation, we use it to fetch files and sort query results through friendly user interface. Finally, the
experiments indicate that the retrieval strategy and sort algorithm have enhanced the retrieval efficiency and precision.