An Application of Bayesian Theorem for Optimal Replica Placement in Data Grids

Informatics ◽  
2010 ◽  
Author(s):  
T. Amjad ◽  
M. Alam
Author(s):  
Ghalem Belalem

Data grids have become an interesting and popular domain in grid community (Foster and Kesselmann, 2004). Generally, the grids are proposed as solutions for large scale systems, where data replication is a well-known technique used to reduce access latency and bandwidth, and increase availability. In splitting of the advantages of replication, there are many problems that should be solved such as, • The replica placement that determines the optimal locations of replicated data in order to reduce the storage cost and data access (Xu et al, 2002); • The problem of determining which replica will be accessed to in terms of consistency when we need to execute a read or write operation (Ranganathan and Foster, 2001); • The problem of degree of replication which consists in finding a minimal number of replicas without reducing the performance of user applications; • The problem of replica consistency that concerns the consistency of a set of replicated data. This consistency provides a completely coherent view of all the replicas for a user (Gray et al 1996). Our principal aim, in this article, is to integrate into consistency management service, an approach based on an economic model for resolving conflicts detected in the data grid.


2008 ◽  
Vol 68 (12) ◽  
pp. 1517-1538 ◽  
Author(s):  
Jan-Jan Wu ◽  
Yi-Fang Lin ◽  
Pangfeng Liu

2009 ◽  
Vol 51 (3) ◽  
pp. 374-392 ◽  
Author(s):  
Mohammad Shorfuzzaman ◽  
Peter Graham ◽  
Rasit Eskicioglu

2010 ◽  
Vol 256 ◽  
pp. 012020 ◽  
Author(s):  
Mohammad Shorfuzzaman ◽  
Peter Graham ◽  
Rasit Eskicioglu

2011 ◽  
Vol 2 (2) ◽  
pp. 156 ◽  
Author(s):  
Faouzi Ben Charrada ◽  
Habib Ounelli ◽  
Hanene Chettaoui

Author(s):  
Rahma Souli Jbali ◽  
Minyar Sassi Hidri ◽  
Rahma Ben-Ayed

Data grids allow the placing of data based on two major challenges: placement of a large mass of data and job scheduling. This strategy proposes that each one is built on the other one in order to offer a high availability of storage spaces. The aim is to reduce access latencies and give improved usage of resources such as network, bandwidth, storage, and computing power. The choice of combining the two strategies in a dynamic replica placement and job scheduling, called ClusOptimizer, while using MapReduce-driven clustering to place a replica seems to be an appropriate answer to the needs since it allows us to distribute the data over all the machines of the platform. Herein, major factors which are mean job execution time, use of storage resources, and the number of active sites, can influence the efficiency. Then, a comparative study between strategies is performed to show the importance of the solution in replica placement according to jobs' frequency and the database's size in the case of biological data.


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