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)
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
GEDAS: a data management system for data grid environments
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
New Optimal Load Allocation for Scheduling Divisible Data Grid Applications
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
A new algorithm for divisible load scheduling with different processor available times
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
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Scheduling an application in data grid is significantly complex and very challenging because of its heterogeneous in nature of the grid system. Divisible Load Theory (DLT) is a powerful model for modelling data-intensive grid problem where both communication and computation loads are partitionable. This paper presents a new divisible load balancing model known as adaptive ADLT (A2DLT) for scheduling the communication intensive grid applications. This model reduces the maximum completion time (makespan) as compared to the ADLT and Constraint DLT (CDLT) models. Experimental results showed that the model can balance the load efficiently, especially when the communication-intensive applications are considered.