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GAS

Grid-based data infrastructures have been used in the last decades to address the increasing needs of application in terms of data volume and throughput. Data grids have been defined as a set of storage resources and data retrieval elements, which allow applications to access data through specific software mechanisms~. The usual manner of providing this fuctionality is by means of the creation of data access and storage services. Nevertheless, the use of a grid environment makes the management and tuning of these data services very complex and thus, it is difficult for them to meet a specific level of QoS, mainly because the performance of such services is linked to an unmanageable set of characteristics of the underlying infrastructure. Moreover, these systems are often based on specific features that require specialized administrator skills.

Even today, a solution to the problem of accessing data sources in an efficient and easy way, assuring one committed QoS level, has not yet been achieved. Ross et al. identified several challenges for I/O systems emphasizing the need of increasing the manageability of I/O systems, due to their high complexity. Autonomic computing can offer self-management features to I/O systems, something that constitutes an important step forward in the I/O field. This approach is commonly known as autonomic storage.

The Grid Autonomic Storage (GAS) research project aims to combine concepts from autonomic computing and I/O for the creation of an autonomic subsystem intended to provide self-management features to a data grid. This autonomic subsystem are oriented to predict the future performance of the grid components, which can vary due to dynamic changes in the environment operation and, according to this prediction, decide the target resources for data placement. This contributes to improve the QoS of data access and storage services in the grid.

public/gas.1289414939.txt.gz ยท Last modified: 2010/11/10 19:48 by ascampos
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