FIRE: A File Reunion Based Data Replication Strategy for Data Grids

Author(s):  
Abdul Rahman Abdurrab ◽  
Tao Xie
2016 ◽  
Vol 20 (3) ◽  
pp. 2551-2562 ◽  
Author(s):  
Junsang Kim ◽  
Youngkyun Kim ◽  
Changho Jeon

Author(s):  
Umesh Banodha ◽  
Praveen Kumar Kataria

Cloud is an emerging technology that stores the necessary data and electronic form of data is produced in gigantic quantity. It is vital to maintain the efficacy of this data the need of data recovery services is highly essential. Cloud computing is anticipated as the vital foundation for the creation of IT enterprise and it is an impeccable solution to move databases and application software to big data centers where managing data and services is not completely reliable. Our focus will be on the cloud data storage security which is a vital feature when it comes to giving quality service. It should also be noted that cloud environment comprises of extremely dynamic and heterogeneous environment and because of high scale physical data and resources, the failure of data centre nodes is completely normal.Therefore, cloud environment needs effective adaptive management of data replication to handle the indispensable characteristic of the cloud environment. Disaster recovery using cloud resources is an attractive approach and data replication strategy which attentively helps to choose the data files for replication and the strategy proposed tells dynamically about the number of replicas and effective data nodes for replication. Thus, the objective of future algorithm is useful to help users together the information from a remote location where network connectivity is absent and secondly to recover files in case it gets deleted or wrecked because of any reason. Even, time oriented problems are getting resolved so in less time recovery process is executed.


Author(s):  
ChenHan Liao ◽  
Na Helian ◽  
Sining Wu ◽  
Mamunur M. Rashid

Most replication methods either monitor the popularity of files or use complicated functions to calculate the overall cost of whether or not a replication decision or a deletion decision should be issued. However, once the replication decision is issued, the popularity of the files is changed and may have already impacted access latency and resource usage. This article proposes a decision-tree-based predictive file replication strategy that forecasts files’ future popularity based on their characteristics on the Grids. The proposed strategy has shown superb performance in terms of mean job time and effective network usage compared with the other two replication strategies, LRU and Economic under OptorSim simulation environment.


2019 ◽  
Vol 22 (4) ◽  
pp. 1199-1210 ◽  
Author(s):  
Said Limam ◽  
Riad Mokadem ◽  
Ghalem Belalem

Author(s):  
Abdenour Lazeb ◽  
Riad Mokadem ◽  
Ghalem Belalem

Applications produce huge volumes of data that are distributed on remote and heterogeneous sites. This generates problems related to access and sharing data. As a result, managing data in large-scale environments is a real challenge. In this context, large-scale data management systems often use data replication, a well-known technique that treats generated problems by storing multiple copies of data, called replicas, across multiple nodes. Most of the replication strategies in these environments are difficult to adapt to cloud environments. They aim to achieve the best performance of the system without meeting the important objectives of the cloud provider. This article proposes a new dynamic replication strategy. The proposed algorithm significantly improves provider gain without neglecting customer satisfaction.


2012 ◽  
Vol 28 (7) ◽  
pp. 1045-1057 ◽  
Author(s):  
Ming-Chang Lee ◽  
Fang-Yie Leu ◽  
Ying-ping Chen

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