Identifying Dynamic Replication Strategies for a High-Performance Data Grid
GRID '01 Proceedings of the Second International Workshop on Grid Computing
Giggle: a framework for constructing scalable replica location services
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Simulation of Dynamic Data Replication Strategies in Data Grids
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Grid Datafarm Architecture for Petascale Data Intensive Computing
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
Dynamic Replica Management in the Service Grid
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
Data Replication Strategies in Grid Environments
ICA3PP '02 Proceedings of the Fifth International Conference on Algorithms and Architectures for Parallel Processing
Enabling the Co-Allocation of Grid Data Transfers
GRID '03 Proceedings of the 4th International Workshop on Grid Computing
An evaluation of the close-to-files processor and data co-allocation policy in multiclusters
CLUSTER '04 Proceedings of the 2004 IEEE International Conference on Cluster Computing
Dynamic Data Replication based on Local Optimization Principle in Data Grid
GCC '07 Proceedings of the Sixth International Conference on Grid and Cooperative Computing
Replica selection strategies in data grid
Journal of Parallel and Distributed Computing
Three-layer control policy for grid resource management
Journal of Network and Computer Applications
The impact of data replication on job scheduling performance in the Data Grid
Future Generation Computer Systems
The complexity of static data replication in data grids
Parallel Computing
Realistic Workload Modeling and Its Performance Impacts in Large-Scale eScience Grids
IEEE Transactions on Parallel and Distributed Systems
On the Benefit of Processor Coallocation in Multicluster Grid Systems
IEEE Transactions on Parallel and Distributed Systems
Robust Load Delegation in Service Grid Environments
IEEE Transactions on Parallel and Distributed Systems
Dynamic replication in a data grid using a Modified BHR Region Based Algorithm
Future Generation Computer Systems
A Dynamic Replica Management Strategy Based on Data Grid
GCC '10 Proceedings of the 2010 Ninth International Conference on Grid and Cloud Computing
Dynamic QoS-aware data replication in grid environments based on data "importance"
Future Generation Computer Systems
A hybrid policy for fault tolerant load balancing in grid computing environments
Journal of Network and Computer Applications
A dynamic replica management strategy in data grid
Journal of Network and Computer Applications
Dynamic replica placement and selection strategies in data grids- A comprehensive survey
Journal of Parallel and Distributed Computing
Scalable service-oriented replication with flexible consistency guarantee in the cloud
Information Sciences: an International Journal
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Data Grid is a geographically distributed environment that deals with large-scale data-intensive applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Data replication is another key optimization technique for reducing access latency and managing large data by storing data in a wisely manner. In this paper two algorithms are proposed, first a novel job scheduling algorithm called Combined Scheduling Strategy (CSS) that uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers the number of jobs waiting in queue, the location of required data for the job and the computing capacity of sites. Second a dynamic data replication strategy, called the Modified Dynamic Hierarchical Replication Algorithm (MDHRA) that improves file access time. This strategy is an enhanced version of Dynamic Hierarchical Replication (DHR) strategy. Data replication should be used wisely because the storage capacity of each Grid site is limited. Thus, it is important to design an effective strategy for the replication replacement. MDHRA replaces replicas based on the last time the replica was requested, number of access, and size of replica. It selects the best replica location from among the many replicas based on response time that can be determined by considering the data transfer time, the storage access latency, the replica requests that waiting in the storage queue and the distance between nodes. The simulation results demonstrate the proposed replication and scheduling strategies give better performance compared to the other algorithms.