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Singapore's A*STAR adopts HDS' pay-as-you-use storage model

T.C. Seow | May 5, 2014
The agency is said to have saved over 20 percent in storage costs using HDS's industry-first scalable storage consumption model.

The Computational Research Centre (CRC) of Singapore's Agency for Science, Technology and Research (A*STAR) has adopted a storage-on-demand model from Hitachi Data Systems (HDS) to manage the computational and storage needs of the entire A*STAR research community.

Under the current storage consumption models, storage solutions have to be upgraded every three to four years, requiring companies to purchase new hardware and migrate data to new systems constantly, said HDS. The migration process entails a high degree of complexity and costs, and often forces companies to buffer in additional capacity each time storage is procured. Often left unused, the additional purchased capacity takes up space and consumes electricity.

The implementation of HDS' storage-on-demand model, which adopts a pay-as-you-use concept, has reduced storage costs by over 20 percent and given A*STAR's research community greater control and insights into its storage utilisation needs.

The agency was able to eliminate the need for buffering, and HDS' detailed utilisation reports also manage and capture the cost of storage utilisation from each user within the research community.

The detailed reports meant that the CRC could accurately manage and capture the cost of storage utilisation from researchers' ongoing projects, and charge usage back to the researchers, based on utilisation. This has helped create greater accountability for storage use, while eliminating the manual process of estimating and calculating storage use per user.

"Given the increasing demand for scarce resources today, utility storage is a viable option for organisations looking to enhance productivity and manage costs," said Dr. John Kan, CIO, A*STAR and Deputy Chairman of CRC. "It is particularly pertinent for organisations like A*STAR, which have to cater for the multiple large-scale data sets generated for research purposes."


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