Advanced Replica-Based Data Access Prediction and Optimization Approach in Distributed Environment

  IJCOT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© - May Issue 2013 by IJCTT Journal
Volume-4 Issue-5                           
Year of Publication : 2013
Authors :P. Sathiya, K. N. Vimal Shankar

MLA

P. Sathiya, K. N. Vimal Shankar "Advanced Replica-Based Data Access Prediction and Optimization Approach in Distributed Environment"International Journal of Computer Trends and Technology (IJCTT),V4(5):1129-1134 May Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract: - The main purpose of the distributed system is to coordinate the use of shared resources or provide communication services to the users. In order to achieve high performances in distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. The data can be access in distributed manner within organizations allows more redundancy and high flexibility in structure for system behaviour. In this system applies many strategies for supporting the online prediction of system behaviour using PSO technique. The main aspect to accessing data is finding the system behaviour and checks the operation conducting on the system through the reducing the iteration process in migration and replication with support strategies models to designed for schedulers. If procedure a high throughput strategies models in a data access optimization behaviour for a map-reduce framework and also to predicate system behaviour. The data access operation finds to automatic and online prediction of read-and-write operations performed by optimization processes and dynamically to predict CPU performances to accessing the resources in efficient way. Data can be process in PSO technique based on scheduler by observe the metadata that placed in the data centres.

 

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Keywords — Distributed System, data centre, PSO (particle swarm optimization), map-reduce framework, CPU, metadata, Optimization