A data intensive distributed computing architecture for “grid” applications
Future Generation Computer Systems - Special issue on high performance computing and networking Europe 1999
A Genetic Algorithm Based Approach for Scheduling Decomposable Data Grid Applications
ICPP '04 Proceedings of the 2004 International Conference on Parallel Processing
A taxonomy of Data Grids for distributed data sharing, management, and processing
ACM Computing Surveys (CSUR)
Design and analysis of a load balancing strategy in data grids
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Resource-Aware Distributed Scheduling Strategies for Large-Scale Computational Cluster/Grid Systems
IEEE Transactions on Parallel and Distributed Systems
Adaptive Divisible Load Model for Scheduling Data-Intensive Grid Applications
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
A2DLT: Divisible Load Balancing Model for Scheduling Communication-Intensive Grid Applications
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
The impact of data replication on job scheduling performance in the Data Grid
Future Generation Computer Systems
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In many data grid applications, data can be decomposed into multiple independent sub-datasets and distributed for parallel execution and analysis. This property has been successfully employed by using Divisible Load Theory (DLT), which has been proved as a powerful tool for modeling divisible load problems in data-intensive grid. There are some scheduling models have been studied but no optimal solution has been reached due to the heterogeneity of the grids. This paper proposes a new model called Iterative DLT (IDLT) for scheduling divisible data grid applications. Recursive numerical closed form solutions are derived to find the optimal workload assigned to the processing nodes. Experimental results show that the proposed IDLT model obtains better solution than other models (almost optimal) in terms of makespan .