The trend of data intensive grid applications has brought grid storage protocols and servers into focus. The objective of this study is to gain an understanding of how time is spent in the storage protocols and servers. The storage protocols have a variety of tuning parameters. Some parameters improve single client performance at the expense of in- creased server load, thereby limiting the number of served clients. What ultimately matters is the throughput of the whole system. Some param- eters increase the flexibility or security of the system at some expense. The objective of this study is to make such trade-o?s clear and enable easy full system optimization.
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The unbounded increase in the computation and data requirements of scientific applications has necessitated the use of widely distributed compute and storage resources to meet the demand. In a widely distributed environment, data is no more locally accessible and has thus to be remotely retrieved and stored. Efficient and reliable access to data sources and archiving destinations in such an environment brings new challenges. Placing data on temporary local storage devices offers many advantages, but such “data placements” also require careful management of storage resources and data movement, i.e. allocating storage space, staging-in of input data, staging-out of generated data, and de-allocation of local storage after the data is safely stored at the destination. Traditional systems closely couple data placement and computation, and consider data placement as a side effect of computation. Data placement is either embedded in the computation and causes the computation to delay, or performed as simple scripts which do not have the privileges of a job.
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