Using Economic Model To Improve The Performance Of BHR Dynamic Replication Algorithm.

In grid computing environment, data is often replicated on many different sites to reduce access time. Static replication fixes the number of replicas whereas dynamic replication adapts to the demands of the grid by reducing or increasing the number of replicas or even moving the location of a replica to another site

Implementation of Sub-Grid-Federation Model for Performance Improvement in Federated Data Grid

In this work, a new model for federation data grid system called Sub-Grid-Federation was designed to improve access latency by accessing data from the nearest possible sites. The strategy in optimising data access was based on the process of searching into the area identified as ‘Network Core Area’ (NCA). The performance of access latency in Sub-Grid-Federation was tested based on the mathematical proving and simulated using OptorSim simulator. Four case studies were carried out and tested in Optimal Downloading Replication Strategy (ODRS) and the Sub-Grid-Federation. The results show that Sub-Grid-Federation is 20% better in terms of access latency and 21% better in terms of reducing remotes sites access compared to ODRS. The results indicate that the Sub-Grid-Federation is a better alternative for the implementation of collaboration and data sharing in data grid system.                                                                                    Keywords: Data grid, replication, scheduling, access latenc

A Prediction-Based Replication Algorithm for Improving Data Availability in Frid Environment

Data replication is a key optimization technique for reducing access latency and managing large data by storing replica of data in a wisely manner. In this paper, we propose a data replication algorithm, called the Prediction-Base Dynamic Replication (PBDR) algorithm that improves file access time. Restricted by the storage capacity, it is essential to design an effective strategy for the replication replacement task. PBDR deletes files by considering four important factors: the number of requests for the replica in the future times, availability, the size of the replica and the last time the replica was requested. Also, it can minimize access latency by selecting the best replica when various sites hold replicas of datasets. The algorithm is simulated using a data grid simulator, OptorSim, developed by European Data Grid projects. The experiment results show that PBDR strategy gives better performance compared to the other algorithms and prevents unnecessary creation of replica which leads to efficient storage usage

An Effective Weighted Data Replication Strategy for Data Grid

Data Grid is a good solution to large scale data management problems including efficient file transfer and replication. Dynamic data replication in Data Grid aims to improve data access time and to utilize network and storage resources efficiently. Since the data files are very large and the Grid storages are limited, managing replicas in storages for the purpose of more effective utilization of them require more attention.In this paper, a dynamic data replication strategy, called Modified Latest Access Largest Weight (MLALW) is proposed. This strategy is an enhanced version of Latest Access Largest Weight strategy.MLALW deletes files by considering three important factors: least frequently used replicas, least recently used replicas and the size of the replica. MLALW stores each replica in an appropriate site i.e. appropriate site in the region that has the highest number of access in future for that particular replica. The algorithm is simulated using a Data Grid simulator, OptorSim, developed by European Data Grid projects. The experiment results show that MLALW strategy gives better performance compared to the other algorithms and prevents unnecessary creation of replica which leads to efficient storage usage

Dynamic replication strategies in data grid systems: A survey

In data grid systems, data replication aims to increase availability, fault tolerance, load balancing and scalability while reducing bandwidth consumption, and job execution time. Several classification schemes for data replication were proposed in the literature, (i) static vs. dynamic, (ii) centralized vs. decentralized, (iii) push vs. pull, and (iv) objective function based. Dynamic data replication is a form of data replication that is performed with respect to the changing conditions of the grid environment. In this paper, we present a survey of recent dynamic data replication strategies. We study and classify these strategies by taking the target data grid architecture as the sole classifier. We discuss the key points of the studied strategies and provide feature comparison of them according to important metrics. Furthermore, the impact of data grid architecture on dynamic replication performance is investigated in a simulation study. Finally, some important issues and open research problems in the area are pointed out

Data Replication Strategies with Performance Objective in Data Grid Systems: A Survey

Replicating for performance constitutes an important issue in large-scale data management systems. In this context, a significant number of replication strategies have been proposed for data grid systems. Some works classified these strategies into static vs. dynamic or centralised vs. decentralised or client vs. server initiated strategies. Very few works deal with a replication strategy classification based on the role of these strategies when building a replica management system. In this paper, we propose a new replication strategy classification based on objective functions of these strategies. Also, each replication strategy is designed according to the data grid topology for which it was proposed. We point out the impact of the topology on replication performance although most of these strategies have been proposed for a hierarchical grid topology. We also study the impact of some factors on performance of these strategies, e.g. access pattern, bandwidth consumption and storage capacity