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A dynamic replica management strategy in data grid

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Replication is a technique used in data grid to improve fault tolerance and to reduce the bandwidth consumption.

Managing this huge amount of data in a centralized way is ineffective due to extensive access latency and load on the central server.

Data Grids aggregate a collection of distributed resources placed in different parts of the world to enable users to share data and resources.

Data replication is an important technique to manage large data in a distributed manner.

There are three key issues in all the data replication algorithms which are replica placement, replica management and replica selection.

Meanwhile, even though the memory and storage size of new computers are ever increasing, they are still not keeping up with the request of storing large number of data.

each node along its path to the requester.

Enhanced Dynamic Hierarchical Replication and Weighted Scheduling

Strategy in Data Grid

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Best replica placement plays an important role for obtaining maximum benefit from replication as well as reducing storage cost and mean job execution time.

Data replication is a key optimization technique for reducing access latency and managing large data by storing data in a wise manner.

Effective scheduling in the Grid can reduce the amount of data transferred among nodes by submitting a job to a node where most of the requested data files are available.

Effective scheduling of jobs is necessary in such a system to use available resources such as computational, storage and network efficiently.

Storing replicas close to the users or grid computation nodes improves response time, fault tolerance and decreases bandwidth consumption.

The files of Grid environments that can be changed by Grid users might bring an important problem of maintaining data consistency among the various replicas distributed in different machines.

So the sum of them along with the proper weight (w1,w2) for each factor yields the combined cost (CCi,j) of executing job i in site j.

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