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Existing computer systems do not allow the user to access the system when turning off the disk containing the OS for saving energy. Thus existing studies have focused on devising effective disk spin up/down schemes. This paper proposes the scheme to keep on providing limited services as well as to improve the energy consumption in computer system. To do so, we employ the method that moves the root...
Abstract-In order to meet the challenges of significant storage and application growth, as well as shortened backup windows and limited IT resources, more and more organizations embrace Hierarchical Storage Management (HSM). Parts of SAN file systems provide the ability to support HSM, but they didn't provide more details in design considerations and how to implement. In this paper, we present design...
The benefits of virtualization are typically considered to be server consolidation, (leading to the reduction of power and cooling costs) increased availability, isolation, ease of operating system deployment and simplified disaster recovery. High Performance Computing (HPC) environments pose one main challenge for virtualization: the need to maximize throughput with minimal loss of CPU and I/O efficiency...
Storage management server, compatible with decoupled data and meta data fashion, is being employed frantically to build large-scale distributed storage system for performance and capacity. To design this hot commodity on flexibly managing the extracted data with little meta data but extended attributes has become a big challenge. This paper breaks a new way to object orient store and implement the...
Offering better performance for random access compared to conventional hard disks and providing larger capacity and lower cost than DRAM, NAND flash based SSDsare integrated in server storage hierarchy as a second tier of disk cache between DRAM and disks for caching more data from disks to meet the increasingly intensive I/O demands. Unfortunately, available hybrid storage architectures cannot fully...
In current virtualized cloud platforms, resource provisioning strategy is still a big challenge. Provisioning will gain low resource utilization based on peak workload, and provisioning based on average work loads will sacrifice the potential revenue of cloud customers because of bad user experiences. VM-based performance isolation also restrains resource flowing on demand. As to memory, this eventually...
In this work we examine how transparent compression in the I/O path can improve space efficiency for online storage. We extend the block layer with the ability to compress and decompress data as they flow between the file-system and the disk. Achieving transparent compression requires extensive metadata management for dealing with variable block sizes, dynamic block mapping, block allocation, explicit...
As data sizes continue to increase, the concept of active storage is well fitted for many data analysis kernels. Nevertheless, while this concept has been investigated and deployed in a number of forms, enabling it from the parallel I/O software stack has been largely unexplored. In this paper, we propose and evaluate an active storage system that allows data analysis, mining, and statistical operations...
Modern enterprises employ hundreds of workstations for daily business operations, which consume a lot of energy and thus have significant operating costs. To reduce such costs, dynamic energy management is often employed. However, dynamic energy management, especially that for disks, introduces delays when an accessed disk is in a low power state and needs to be brought into active state. In this...
In this paper, we propose a Java application interface on InfiniBand network, named Jdib, which provides a viable approach for Java applications to use RDMA semantics directly. Most functions in InfiniBand Verbs are implemented in Jdib, which provides the feasibility of building high-performance cluster applications in Java. Further, a JNIO-based mechanism is used to get rid of extra data copy between...
Large-scale parallel applications often produce immense quantities of data that need to be analyzed. To avoid performing repeated, costly disk accesses, analysis of large data sets generally requires a commensurately large amount of memory. While some data-analysis tools can easily be parallelized to distribute memory across a cluster, other tools are either difficult to parallelize or, in the case...
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