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

Informatics ◽  
2010 ◽  
Author(s):  
T. Amjad ◽  
M. Alam
Keyword(s):  
Replica Placement ◽  
Data Grids ◽  
Bayesian Theorem

A Comparative Study of Replica Placement Strategies in Data Grids

2007 ◽  
pp. 135-143 ◽  
Author(s):  
Qaisar Rasool ◽  
Jianzhong Li ◽  
George S. Oreku ◽  
Ehsan Ullah Munir ◽  
Donghua Yang
Keyword(s):  
Comparative Study ◽  
Replica Placement ◽  
Data Grids

Policy Driven Negotiation to Improve the QoS in Data Grid

2010 ◽  
pp. 1041-1056
Author(s):  
Ghalem Belalem
Keyword(s):  
Large Scale ◽  
Data Access ◽  
Data Grid ◽  
Management Service ◽  
Replica Placement ◽  
Data Grids ◽  
Large Scale Systems ◽  
Consistency Management ◽  
Access Latency ◽  
Replicated Data

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.

scholarly journals Optimal replica placement in hierarchical Data Grids with locality assurance

2008 ◽  
Vol 68 (12) ◽  
pp. 1517-1538 ◽  
Author(s):  
Jan-Jan Wu ◽  
Yi-Fang Lin ◽  
Pangfeng Liu
Keyword(s):  
Hierarchical Data ◽  
Replica Placement ◽  
Data Grids

Adaptive popularity-driven replica placement in hierarchical data grids

2009 ◽  
Vol 51 (3) ◽  
pp. 374-392 ◽  
Author(s):  
Mohammad Shorfuzzaman ◽  
Peter Graham ◽  
Rasit Eskicioglu
Keyword(s):  
Hierarchical Data ◽  
Replica Placement ◽  
Data Grids

scholarly journals Adaptive Replica Placement in Hierarchical Data Grids

2010 ◽  
Vol 256 ◽  
pp. 012020 ◽  
Author(s):  
Mohammad Shorfuzzaman ◽  
Peter Graham ◽  
Rasit Eskicioglu
Keyword(s):  
Hierarchical Data ◽  
Replica Placement ◽  
Data Grids

An efficient replica placement strategy in highly dynamic data grids

2011 ◽  
Vol 2 (2) ◽  
pp. 156 ◽  
Author(s):  
Faouzi Ben Charrada ◽  
Habib Ounelli ◽  
Hanene Chettaoui
Keyword(s):  
Replica Placement ◽  
Data Grids ◽  
Dynamic Data

Dynamic-Based Clustering for Replica Placement in Data Grids

2019 ◽  
Vol 10 (4) ◽  
pp. 58-80
Author(s):  
Rahma Souli Jbali ◽  
Minyar Sassi Hidri ◽  
Rahma Ben-Ayed
Keyword(s):  
Time Use ◽  
Active Sites ◽  
Job Scheduling ◽  
Large Mass ◽  
Biological Data ◽  
Replica Placement ◽  
Data Grids ◽  
Computing Power ◽  
Network Bandwidth ◽  
Major Factors

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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